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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
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White-Label Jiminny Instance
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Win/Loss Analysis for a Deal
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Ask Jiminny for Open and Closed Deals
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Ask Jiminny Anything on Deal level
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Automatically record all calendar meetings
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Recording Consent
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Automated CRM Filling
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
Read time 12 min
12 min
Views 14
14
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Epic
Link to Epik in Jira
Document status
DRAFT
Epic
Document status
Link to Epik in Jira
DRAFT
Change emoji Objective
Objective
Change emoji Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a
data layer for AI-driven revenue workflows
, not just a standalone product.
👤 Target user
👤 Target user
👤 Target user
Revenue teams using AI tools (Sales,
CS
, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
🤕
🤕
Pain point or problem
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
💥
💥
Impact and benefits
💥 Impact and benefits
For customers
For customers
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
For Jiminny
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
💡
💡
Solution ideas
💡 Solution ideas
Build an
AI Knowledge Connector (
MCP
-based)
that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides
structured insights
(summaries, action items, scoring etc.)
Supports
deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Change emoji Success metrics
Success metrics
Change emoji Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
No sort applied to the column
Metric
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Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
Goal
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
Decrease the manual copying of transcripts
Metric
No sort applied to the column
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
decrease by X %
Change emoji User interaction and design
User interaction and design
Change emoji User interaction and design
Simple setup flow:
Generate API key / connect
MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus:
usable by non-technical users
Change emoji Detailed Requirements
Detailed Requirements
Change emoji Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
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Importance
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Notes
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Requirement
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Importance
No sort applied to the column
Notes
No sort applied to the column
Requirement
No sort applied to the column
Importance
No sort applied to the column
Notes
No sort applied to the column
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide
MCP
endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH
For each deal expose:
calls/meetings/emails
Identified risks
contacts related to the deal with their job title
linked account details
all CRM field information that we have in
DI
+ fields used for CRM filling
example - get_deal_summary
request -
{
"tool": "get_deal_summary",
"arguments": {
"deal_name": "ACME"
}
}
responce:
{
"deal_name": "ACME",
"stage": "Negotiation",
"recent_activity": [
"Demo call on 2026-04-01",
"Pricing discussion on 2026-04-03"
],
"risks": [
"Pricing concerns",
"Security review pending"
], "next_steps": [
"Send follow-up to VP Sales",
"Share security documentation"
]
}
Ensure the AI can easily understand the schema of the data coming from Jiminny
HIGH
Models perform much better when tools have:
clear names
rich descriptions
predictable schemas
Provide clear, actionable error states for customers when
MCP
connections fail
HIGH
Error messages should guide agents toward solutions with specific suggestions and next steps.
Expired or revoked key
— “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the
MCP
server.”
Rate limit hit
— “Error: Rate limit exceeded. Too many requests have been made to the Jiminny
MCP
server in a short period. Please wait 60 seconds before retrying.”
Permissions mismatch
— if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”
Trying to access private meeting
- “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the
MCP
connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”
Downgraded/Capture account
— if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error:
MCP
access unavailable. The Jiminny
MCP
connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”
Jiminny-side outage
— “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”
No data found
- “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”
Allow customers to securely control who and what data is accessible externally
HIGH
Ensure
MCP
only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the
MCP
as well.
Private meetings shouldn’t be exposed via
MCP
HIGH
When a meetings is set to Private then the information about it shouldn’t be exposed through the
MCP...
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idle
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
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Product Strategy 2025+ Change emoji
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Ways of working - Product processes Change emoji
Ways of working - Product processes
Ways of working - Product processes
Core Product Performance Metrics 📈
Core Product Performance Metrics
📈
Core Product Performance Metrics
Product Documentation Change emoji
Product Documentation
Product Documentation
Product Briefs Change emoji
Product Briefs
Product Briefs
Change emoji Activity Export
Activity Export
Change emoji Autologging activities
Autologging activities
Deal Insights - Multiple currencies
Deal Insights - Multiple currencies
Reinvent Themes & Topics (and ACS)
Reinvent Themes & Topics (and ACS)
Change emoji Billing Portal
Billing Portal
Change emoji Upload Video/Audio Recordings
Upload Video/Audio Recordings
White-Label Jiminny Instance
White-Label Jiminny Instance
Win/Loss Analysis for a Deal
Win/Loss Analysis for a Deal
Change emoji Hubspot app
Hubspot app
Change emoji Automatically hard delete data for churned customers
Automatically hard delete data for churned customers
Change emoji Ask Jiminny Anything on Call level
Ask Jiminny Anything on Call level
Change emoji Ask Jiminny for Open and Closed Deals
Ask Jiminny for Open and Closed Deals
Change emoji Ask Jiminny Anything on Deal level
Ask Jiminny Anything on Deal level
Change emoji Automatically record all calendar meetings
Automatically record all calendar meetings
Change emoji Product Tiering
Product Tiering
Change emoji Recording Consent
Recording Consent
Change emoji Automated CRM Filling
Automated CRM Filling
Change emoji Automated Exec Reports
Automated Exec Reports
Change emoji Auto-detect Activity Type
Auto-detect Activity Type
AI Signals & Alerts
AI Signals & Alerts
Change emoji AJA on Anything
AJA on Anything
Change emoji AI Call Scoring
AI Call Scoring
Jiminny MCP Connector
Jiminny MCP Connector
Desktop app to record a meetings without visible Notetaker
Desktop app to record a meetings without visible Notetaker
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
Read time 12 min
12 min
Views 14
14
Add a reaction
Add a reaction
Epic
Link to Epik in Jira
Document status
DRAFT
Epic
Document status
Link to Epik in Jira
DRAFT
Change emoji Objective
Objective
Change emoji Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a
data layer for AI-driven revenue workflows
, not just a standalone product.
👤 Target user
👤 Target user
👤 Target user
Revenue teams using AI tools (Sales,
CS
, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
🤕
🤕
Pain point or problem
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
💥
💥
Impact and benefits
💥 Impact and benefits
For customers
For customers
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
For Jiminny
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
💡
💡
Solution ideas
💡 Solution ideas
Build an
AI Knowledge Connector (
MCP
-based)
that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides
structured insights
(summaries, action items, scoring etc.)
Supports
deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Change emoji Success metrics
Success metrics
Change emoji Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
No sort applied to the column
Metric
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
Goal
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
Decrease the manual copying of transcripts
Metric
No sort applied to the column
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
decrease by X %
Change emoji User interaction and design
User interaction and design
Change emoji User interaction and design
Simple setup flow:
Generate API key / connect
MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus:
usable by non-technical users
Change emoji Detailed Requirements
Detailed Requirements
Change emoji Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
No sort applied to the column
Importance
No sort applied to the column
Notes
No sort applied to the column
Requirement
No sort applied to the column
Importance
No sort applied to the column
Notes
No sort applied to the column
Requirement
No sort applied to the column
Importance
No sort applied to the column
Notes
No sort applied to the column
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide
MCP
endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH
For each deal expose:
calls/meetings/emails
Identified risks
contacts related to the deal with their job title
linked account details
all CRM field information that we have in
DI
+ fields used for CRM filling
example - get_deal_summary
request -
{
"tool": "get_deal_summary",
"arguments": {
"deal_name": "ACME"
}
}
responce:
{
"deal_name": "ACME",
"stage": "Negotiation",
"recent_activity": [
"Demo call on 2026-04-01",
"Pricing discussion on 2026-04-03"
],
"risks": [
"Pricing concerns",
"Security review pending"
], "next_steps": [
"Send follow-up to VP Sales",
"Share security documentation"
]
}
Ensure the AI can easily understand the schema of the data coming from Jiminny
HIGH
Models perform much better when tools have:
clear names
rich descriptions
predictable schemas
Provide clear, actionable error states for customers when
MCP
connections fail
HIGH
Error messages should guide agents toward solutions with specific suggestions and next steps.
Expired or revoked key
— “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the
MCP
server.”
Rate limit hit
— “Error: Rate limit exceeded. Too many requests have been made to the Jiminny
MCP
server in a short period. Please wait 60 seconds before retrying.”
Permissions mismatch
— if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”
Trying to access private meeting
- “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the
MCP
connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”
Downgraded/Capture account
— if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error:
MCP
access unavailable. The Jiminny
MCP
connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”
Jiminny-side outage
— “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”
No data found
- “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”
Allow customers to securely control who and what data is accessible externally
HIGH
Ensure
MCP
only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the
MCP
as well.
Private meetings shouldn’t be exposed via
MCP
HIGH
When a meetings is set to Private then the information about it shouldn’t be exposed through the
MCP...
|
NULL
|
|
71946
|
NULL
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0
|
2026-04-22T14:32:23.632336+00:00
|
/Users/lukas/.screenpipe/data/data/2026-04-22/1776 /Users/lukas/.screenpipe/data/data/2026-04-22/1776868343632_m2.jpg...
|
Firefox
|
Jiminny MCP Connector - Product - Confluence — Wor Jiminny MCP Connector - Product - Confluence — Work...
|
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|
jiminny.atlassian.net/wiki/spaces/PROD/pages/37282 jiminny.atlassian.net/wiki/spaces/PROD/pages/3728244737/Jiminny+MCP+Connector...
|
monitor_2
|
NULL
|
NULL
|
NULL
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NULL
|
Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
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Product Strategy 2025+ Change emoji
Product Strategy 2025+
Product Strategy 2025+
Competitive analysis Change emoji
Competitive analysis
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Ways of working - Product processes Change emoji
Ways of working - Product processes
Ways of working - Product processes
Core Product Performance Metrics 📈
Core Product Performance Metrics
📈
Core Product Performance Metrics
Product Documentation Change emoji
Product Documentation
Product Documentation
Product Briefs Change emoji
Product Briefs
Product Briefs
Change emoji Activity Export
Activity Export
Change emoji Autologging activities
Autologging activities
Deal Insights - Multiple currencies
Deal Insights - Multiple currencies
Reinvent Themes & Topics (and ACS)
Reinvent Themes & Topics (and ACS)
Change emoji Billing Portal
Billing Portal
Change emoji Upload Video/Audio Recordings
Upload Video/Audio Recordings
White-Label Jiminny Instance
White-Label Jiminny Instance
Win/Loss Analysis for a Deal
Win/Loss Analysis for a Deal
Change emoji Hubspot app
Hubspot app
Change emoji Automatically hard delete data for churned customers
Automatically hard delete data for churned customers
Change emoji Ask Jiminny Anything on Call level
Ask Jiminny Anything on Call level
Change emoji Ask Jiminny for Open and Closed Deals
Ask Jiminny for Open and Closed Deals
Change emoji Ask Jiminny Anything on Deal level
Ask Jiminny Anything on Deal level
Change emoji Automatically record all calendar meetings
Automatically record all calendar meetings
Change emoji Product Tiering
Product Tiering
Change emoji Recording Consent
Recording Consent
Change emoji Automated CRM Filling
Automated CRM Filling
Change emoji Automated Exec Reports
Automated Exec Reports
Change emoji Auto-detect Activity Type
Auto-detect Activity Type
AI Signals & Alerts
AI Signals & Alerts
Change emoji AJA on Anything
AJA on Anything
Change emoji AI Call Scoring
AI Call Scoring
Jiminny MCP Connector
Jiminny MCP Connector
Desktop app to record a meetings without visible Notetaker
Desktop app to record a meetings without visible Notetaker
Feedback Change emoji
Feedback
Feedback
Research & User Feedback Change emoji
Research & User Feedback
Research & User Feedback
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Jiminny MCP Connector
Updated 10m ago
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
Read time 12 min
12 min
Views 14
14
Add a reaction
Add a reaction
Epic
Link to Epik in Jira
Document status
DRAFT
Epic
Document status
Link to Epik in Jira
DRAFT
Change emoji Objective
Objective
Change emoji Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a
data layer for AI-driven revenue workflows
, not just a standalone product.
👤 Target user
👤 Target user
👤 Target user
Revenue teams using AI tools (Sales,
CS
, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
🤕
🤕
Pain point or problem
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
💥
💥
Impact and benefits
💥 Impact and benefits
For customers
For customers
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
For Jiminny
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
💡
💡
Solution ideas
💡 Solution ideas
Build an
AI Knowledge Connector (
MCP
-based)
that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides
structured insights
(summaries, action items, scoring etc.)
Supports
deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Change emoji Success metrics
Success metrics
Change emoji Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
No sort applied to the column
Metric
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
Goal
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
Decrease the manual copying of transcripts
Metric
No sort applied to the column
% of customers connecting external AI tools
Number of
MCP...
|
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
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Core Product Performance Metrics 📈
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Product Documentation Change emoji
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Product Briefs Change emoji
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Change emoji Activity Export
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Change emoji Autologging activities
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Deal Insights - Multiple currencies
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Reinvent Themes & Topics (and ACS)
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Upload Video/Audio Recordings
White-Label Jiminny Instance
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Win/Loss Analysis for a Deal
Win/Loss Analysis for a Deal
Change emoji Hubspot app
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Change emoji Automatically hard delete data for churned customers
Automatically hard delete data for churned customers
Change emoji Ask Jiminny Anything on Call level
Ask Jiminny Anything on Call level
Change emoji Ask Jiminny for Open and Closed Deals
Ask Jiminny for Open and Closed Deals
Change emoji Ask Jiminny Anything on Deal level
Ask Jiminny Anything on Deal level
Change emoji Automatically record all calendar meetings
Automatically record all calendar meetings
Change emoji Product Tiering
Product Tiering
Change emoji Recording Consent
Recording Consent
Change emoji Automated CRM Filling
Automated CRM Filling
Change emoji Automated Exec Reports
Automated Exec Reports
Change emoji Auto-detect Activity Type
Auto-detect Activity Type
AI Signals & Alerts
AI Signals & Alerts
Change emoji AJA on Anything
AJA on Anything
Change emoji AI Call Scoring
AI Call Scoring
Jiminny MCP Connector
Jiminny MCP Connector
Desktop app to record a meetings without visible Notetaker
Desktop app to record a meetings without visible Notetaker
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
Read time 12 min
12 min
Views 14
14
Add a reaction
Add a reaction
Epic
Link to Epik in Jira
Document status
DRAFT
Epic
Document status
Link to Epik in Jira
DRAFT
Change emoji Objective
Objective
Change emoji Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a
data layer for AI-driven revenue workflows
, not just a standalone product.
👤 Target user
👤 Target user
👤 Target user
Revenue teams using AI tools (Sales,
CS
, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
🤕
🤕
Pain point or problem
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
💥
💥
Impact and benefits
💥 Impact and benefits
For customers
For customers
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
For Jiminny
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
💡
💡
Solution ideas
💡 Solution ideas
Build an
AI Knowledge Connector (
MCP
-based)
that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides
structured insights
(summaries, action items, scoring etc.)
Supports
deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Change emoji Success metrics
Success metrics
Change emoji Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
No sort applied to the column
Metric
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
Goal
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
Decrease the manual copying of transcripts
Metric
No sort applied to the column
% of customers connecting external AI tools
Number of
MCP...
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2026-04-22T14:32:46.901717+00:00
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/Users/lukas/.screenpipe/data/data/2026-04-22/1776 /Users/lukas/.screenpipe/data/data/2026-04-22/1776868366901_m1.jpg...
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Jiminny MCP Connector - Product - Confluence — Wor Jiminny MCP Connector - Product - Confluence — Work...
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True
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jiminny.atlassian.net/wiki/spaces/PROD/pages/37282 jiminny.atlassian.net/wiki/spaces/PROD/pages/3728244737/Jiminny+MCP+Connector...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
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Product Strategy 2025+ Change emoji
Product Strategy 2025+
Product Strategy 2025+
Competitive analysis Change emoji
Competitive analysis
Competitive analysis
Ways of working - Product processes Change emoji
Ways of working - Product processes
Ways of working - Product processes
Core Product Performance Metrics 📈
Core Product Performance Metrics
📈
Core Product Performance Metrics
Product Documentation Change emoji
Product Documentation
Product Documentation
Product Briefs Change emoji
Product Briefs
Product Briefs
Change emoji Activity Export
Activity Export
Change emoji Autologging activities
Autologging activities
Deal Insights - Multiple currencies
Deal Insights - Multiple currencies
Reinvent Themes & Topics (and ACS)
Reinvent Themes & Topics (and ACS)
Change emoji Billing Portal
Billing Portal
Change emoji Upload Video/Audio Recordings
Upload Video/Audio Recordings
White-Label Jiminny Instance
White-Label Jiminny Instance
Win/Loss Analysis for a Deal
Win/Loss Analysis for a Deal
Change emoji Hubspot app
Hubspot app
Change emoji Automatically hard delete data for churned customers
Automatically hard delete data for churned customers
Change emoji Ask Jiminny Anything on Call level
Ask Jiminny Anything on Call level
Change emoji Ask Jiminny for Open and Closed Deals
Ask Jiminny for Open and Closed Deals
Change emoji Ask Jiminny Anything on Deal level
Ask Jiminny Anything on Deal level
Change emoji Automatically record all calendar meetings
Automatically record all calendar meetings
Change emoji Product Tiering
Product Tiering
Change emoji Recording Consent
Recording Consent
Change emoji Automated CRM Filling
Automated CRM Filling
Change emoji Automated Exec Reports
Automated Exec Reports
Change emoji Auto-detect Activity Type
Auto-detect Activity Type
AI Signals & Alerts
AI Signals & Alerts
Change emoji AJA on Anything
AJA on Anything
Change emoji AI Call Scoring
AI Call Scoring
Jiminny MCP Connector
Jiminny MCP Connector
Desktop app to record a meetings without visible Notetaker
Desktop app to record a meetings without visible Notetaker
Feedback Change emoji
Feedback
Feedback
Research & User Feedback Change emoji
Research & User Feedback
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Jiminny MCP Connector
Updated 10m ago
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
Read time 12 min
12 min
Views 14
14
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Link to Epik in Jira
Document status
DRAFT
Epic
Document status
Link to Epik in Jira
DRAFT
Change emoji Objective
Objective
Change emoji Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a
data layer for AI-driven revenue workflows
, not just a standalone product.
👤 Target user
👤 Target user
👤 Target user
Revenue teams using AI tools (Sales,
CS
, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
🤕
🤕
Pain point or problem
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
💥
💥
Impact and benefits
💥 Impact and benefits
For customers
For customers
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
For Jiminny
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
💡
💡
Solution ideas
💡 Solution ideas
Build an
AI Knowledge Connector (
MCP
-based)
that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides
structured insights
(summaries, action items, scoring etc.)
Supports
deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Change emoji Success metrics
Success metrics
Change emoji Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
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Metric
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Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
Goal
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
Decrease the manual copying of transcripts
Metric
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% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
decrease by X %
Change emoji User interaction and design
User interaction and design
Change emoji User interaction and design
Simple setup flow:
Generate API key / connect
MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus:
usable by non-technical users
Change emoji Detailed Requirements
Detailed Requirements
Change emoji Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
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Importance
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Notes
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Requirement
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Importance
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Notes
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Requirement
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Importance
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Notes
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Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide
MCP
endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH
For each deal expose:
calls/meetings/emails
Identified risks
contacts related to the deal with their job title
linked account details
all CRM field information that we have in
DI
+ fields used for CRM filling
example - get_deal_summary
request -
{
"tool": "get_deal_summary",
"arguments": {
"deal_name": "ACME"
}
}
responce:
{
"deal_name": "ACME",
"stage": "Negotiation",
"recent_activity": [
"Demo call on 2026-04-01",
"Pricing discussion on 2026-04-03"
],
"risks": [
"Pricing concerns",
"Security review pending"
], "next_steps": [
"Send follow-up to VP Sales",
"Share security documentation"
]
}
Ensure the AI can easily understand the schema of the data coming from Jiminny
HIGH
Models perform much better when tools have:
clear names
rich descriptions
predictable schemas
Provide clear, actionable error states for customers when
MCP
connections fail...
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type","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Notes","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Call Score","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"CRM information","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to understand deal status across multiple calls/emails","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"For each deal expose:","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"calls/meetings/emails","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Identified risks","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"contacts related to the deal with their job title","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"linked account details","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"all CRM field information that we have in","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"DI","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"+ fields used for CRM filling","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"example - get_deal_summary","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"request -","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"{","depth":21,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"tool\": \"get_deal_summary\",","depth":21,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"arguments\": {","depth":21,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"deal_name\": \"ACME\"","depth":21,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":21,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":21,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"responce:","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"{","depth":21,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"deal_name\": \"ACME\",","depth":21,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"stage\": \"Negotiation\",","depth":21,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"recent_activity\": [","depth":21,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"Demo call on 2026-04-01\",","depth":21,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"Pricing discussion on 2026-04-03\"","depth":21,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"],","depth":21,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"risks\": [","depth":21,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"Pricing concerns\",","depth":21,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"Security review pending\"","depth":21,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"], \"next_steps\": [","depth":21,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"Send follow-up to VP 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names","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"rich descriptions","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"predictable schemas","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide clear, actionable error states for customers when","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"MCP","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"connections fail","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"}]...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
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Change emoji Autologging activities
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Deal Insights - Multiple currencies
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Reinvent Themes & Topics (and ACS)
Reinvent Themes & Topics (and ACS)
Change emoji Billing Portal
Billing Portal
Change emoji Upload Video/Audio Recordings
Upload Video/Audio Recordings
White-Label Jiminny Instance
White-Label Jiminny Instance
Win/Loss Analysis for a Deal
Win/Loss Analysis for a Deal
Change emoji Hubspot app
Hubspot app
Change emoji Automatically hard delete data for churned customers
Automatically hard delete data for churned customers
Change emoji Ask Jiminny Anything on Call level
Ask Jiminny Anything on Call level
Change emoji Ask Jiminny for Open and Closed Deals
Ask Jiminny for Open and Closed Deals
Change emoji Ask Jiminny Anything on Deal level
Ask Jiminny Anything on Deal level
Change emoji Automatically record all calendar meetings
Automatically record all calendar meetings
Change emoji Product Tiering
Product Tiering
Change emoji Recording Consent
Recording Consent
Change emoji Automated CRM Filling
Automated CRM Filling
Change emoji Automated Exec Reports
Automated Exec Reports
Change emoji Auto-detect Activity Type
Auto-detect Activity Type
AI Signals & Alerts
AI Signals & Alerts
Change emoji AJA on Anything
AJA on Anything
Change emoji AI Call Scoring
AI Call Scoring
Jiminny MCP Connector
Jiminny MCP Connector
Desktop app to record a meetings without visible Notetaker
Desktop app to record a meetings without visible Notetaker
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Jiminny MCP Connector
Updated 10m ago
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
Read time 12 min
12 min
Views 14
14
Add a reaction
Add a reaction
Epic
Link to Epik in Jira
Document status
DRAFT
Epic
Document status
Link to Epik in Jira
DRAFT
Change emoji Objective
Objective
Change emoji Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a
data layer for AI-driven revenue workflows
, not just a standalone product.
👤 Target user
👤 Target user
👤 Target user
Revenue teams using AI tools (Sales,
CS
, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
🤕
🤕
Pain point or problem
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
💥
💥
Impact and benefits
💥 Impact and benefits
For customers
For customers
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
For Jiminny
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
💡
💡
Solution ideas
💡 Solution ideas
Build an
AI Knowledge Connector (
MCP
-based)
that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides
structured insights
(summaries, action items, scoring etc.)
Supports
deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Change emoji Success metrics
Success metrics
Change emoji Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
No sort applied to the column
Metric
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
Goal
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
Decrease the manual copying of transcripts
Metric
No sort applied to the column
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
decrease by X %
Change emoji User interaction and design
User interaction and design
Change emoji User interaction and design
Simple setup flow:
Generate API key / connect
MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus:
usable by non-technical users
Change emoji Detailed Requirements
Detailed Requirements
Change emoji Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
No sort applied to the column
Importance
No sort applied to the column
Notes
No sort applied to the column
Requirement
No sort applied to the column
Importance
No sort applied to the column
Notes
No sort applied to the column
Requirement
No sort applied to the column
Importance
No sort applied to the column
Notes
No sort applied to the column
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide
MCP
endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH
For each deal expose:
calls/meetings/emails
Identified risks
contacts related to the deal with their job title
linked account details
all CRM field information that we have in
DI
+ fields used for CRM filling
example - get_deal_summary
request -
{
"tool": "get_deal_summary",
"arguments": {
"deal_name": "ACME"
}
}
responce:
{
"deal_name": "ACME",
"stage": "Negotiation",
"recent_activity": [
"Demo call on 2026-04-01",
"Pricing discussion on 2026-04-03"
],
"risks": [
"Pricing concerns",
"Security review pending"
], "next_steps": [
"Send follow-up to VP Sales",
"Share security documentation"
]
}
Ensure the AI can easily understand the schema of the data coming from Jiminny
HIGH
Models perform much better when tools have:
clear names
rich descriptions
predictable schemas
Provide clear, actionable error states for customers when
MCP
connections fail...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Jiminny MCP Connector - Product - Confluence
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Product Strategy 2025+ Change emoji
Product Strategy 2025+
Product Strategy 2025+
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Competitive analysis
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Ways of working - Product processes
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Reinvent Themes & Topics (and ACS)
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Change emoji Upload Video/Audio Recordings
Upload Video/Audio Recordings
White-Label Jiminny Instance
White-Label Jiminny Instance
Win/Loss Analysis for a Deal
Win/Loss Analysis for a Deal
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Hubspot app
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Automatically hard delete data for churned customers
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Ask Jiminny Anything on Call level
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Ask Jiminny for Open and Closed Deals
Change emoji Ask Jiminny Anything on Deal level
Ask Jiminny Anything on Deal level
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Automatically record all calendar meetings
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Recording Consent
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Automated CRM Filling
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Jiminny MCP Connector
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Jiminny MCP Connector
Updated 10m ago
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
Read time 12 min
12 min
Views 14
14
Add a reaction
Add a reaction
Epic
Link to Epik in Jira
Document status
DRAFT
Epic
Document status
Link to Epik in Jira
DRAFT
Change emoji Objective
Objective
Change emoji Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a
data layer for AI-driven revenue workflows
, not just a standalone product.
👤 Target user
👤 Target user
👤 Target user
Revenue teams using AI tools (Sales,
CS
, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
🤕
🤕
Pain point or problem
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
💥
💥
Impact and benefits
💥 Impact and benefits
For customers
For customers
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
For Jiminny
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
💡
💡
Solution ideas
💡 Solution ideas
Build an
AI Knowledge Connector (
MCP
-based)
that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides
structured insights
(summaries, action items, scoring etc.)
Supports
deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Change emoji Success metrics
Success metrics
Change emoji Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
No sort applied to the column
Metric
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
Goal
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
Decrease the manual copying of transcripts
Metric
No sort applied to the column
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
decrease by X %
Change emoji User interaction and design
User interaction and design
Change emoji User interaction and design
Simple setup flow:
Generate API key / connect
MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus:
usable by non-technical users
Change emoji Detailed Requirements
Detailed Requirements
Change emoji Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
No sort applied to the column
Importance
No sort applied to the column
Notes
No sort applied to the column
Requirement
No sort applied to the column
Importance
No sort applied to the column
Notes
No sort applied to the column
Requirement
No sort applied to the column
Importance
No sort applied to the column
Notes
No sort applied to the column
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide
MCP
endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH
For each deal expose:
calls/meetings/emails
Identified risks
contacts related to the deal with their job title
linked account details
all CRM field information that we have in
DI
+ fields used for CRM filling
example - get_deal_summary
request -
{
"tool": "get_deal_summary",
"arguments": {
"deal_name": "ACME"
}
}
responce:
{
"deal_name": "ACME",
"stage": "Negotiation",
"recent_activity": [
"Demo call on 2026-04-01",
"Pricing discussion on 2026-04-03"
],
"risks": [
"Pricing concerns",
"Security review pending"
], "next_steps": [
"Send follow-up to VP Sales",
"Share security documentation"
]
}
Ensure the AI can easily understand the schema of the data coming from Jiminny
HIGH
Models perform much better when tools have:
clear names
rich descriptions
predictable schemas
Provide clear, actionable error states for customers when
MCP
connections fail
HIGH
Error messages should guide agents toward solutions with specific suggestions and next steps.
Expired or revoked key
— “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the
MCP
server.”
Rate limit hit
— “Error: Rate limit exceeded. Too many requests have been made to the Jiminny
MCP
server in a short period. Please wait 60 seconds before retrying.”
Permissions mismatch
— if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”
Trying to access private meeting
- “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the
MCP
connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”
Downgraded/Capture account
— if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error:
MCP
access unavailable. The Jiminny
MCP
connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”
Jiminny-side outage
— “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”
No data found
- “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”
Allow customers to securely control who and what data is accessible externally
HIGH
Ensure
MCP
only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the
MCP
as well.
Private meetings shouldn’t be exposed via
MCP
HIGH
When a meetings is set to Private then the information about it shouldn’t be exposed through the
MCP
.
Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows
HIGH
Ensure data is structured to be easily combined with:
CRM (accounts, deals)
external docs
Use consistent identifiers:
account IDs...
|
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
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Product Strategy 2025+ Change emoji
Product Strategy 2025+
Product Strategy 2025+
Competitive analysis Change emoji
Competitive analysis
Competitive analysis
Ways of working - Product processes Change emoji
Ways of working - Product processes
Ways of working - Product processes
Core Product Performance Metrics 📈
Core Product Performance Metrics
📈
Core Product Performance Metrics
Product Documentation Change emoji
Product Documentation
Product Documentation
Product Briefs Change emoji
Product Briefs
Product Briefs
Change emoji Activity Export
Activity Export
Change emoji Autologging activities
Autologging activities
Deal Insights - Multiple currencies
Deal Insights - Multiple currencies
Reinvent Themes & Topics (and ACS)
Reinvent Themes & Topics (and ACS)
Change emoji Billing Portal
Billing Portal
Change emoji Upload Video/Audio Recordings
Upload Video/Audio Recordings
White-Label Jiminny Instance
White-Label Jiminny Instance
Win/Loss Analysis for a Deal
Win/Loss Analysis for a Deal
Change emoji Hubspot app
Hubspot app
Change emoji Automatically hard delete data for churned customers
Automatically hard delete data for churned customers
Change emoji Ask Jiminny Anything on Call level
Ask Jiminny Anything on Call level
Change emoji Ask Jiminny for Open and Closed Deals
Ask Jiminny for Open and Closed Deals
Change emoji Ask Jiminny Anything on Deal level
Ask Jiminny Anything on Deal level
Change emoji Automatically record all calendar meetings
Automatically record all calendar meetings
Change emoji Product Tiering
Product Tiering
Change emoji Recording Consent
Recording Consent
Change emoji Automated CRM Filling
Automated CRM Filling
Change emoji Automated Exec Reports
Automated Exec Reports
Change emoji Auto-detect Activity Type
Auto-detect Activity Type
AI Signals & Alerts
AI Signals & Alerts
Change emoji AJA on Anything
AJA on Anything
Change emoji AI Call Scoring
AI Call Scoring
Jiminny MCP Connector
Jiminny MCP Connector
Desktop app to record a meetings without visible Notetaker
Desktop app to record a meetings without visible Notetaker
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Jiminny MCP Connector
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
Read time 12 min
12 min
Views 14
14
Add a reaction
Add a reaction
Epic
Link to Epik in Jira
Document status
DRAFT
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Change emoji Objective
Objective
Change emoji Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a
data layer for AI-driven revenue workflows
, not just a standalone product.
👤 Target user
👤 Target user
👤 Target user
Revenue teams using AI tools (Sales,
CS
, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
🤕
🤕
Pain point or problem
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
💥
💥
Impact and benefits
💥 Impact and benefits
For customers
For customers
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
For Jiminny
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
💡
💡
Solution ideas
💡 Solution ideas
Build an
AI Knowledge Connector (
MCP
-based)
that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides
structured insights
(summaries, action items, scoring etc.)
Supports
deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Change emoji Success metrics
Success metrics
Change emoji Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
No sort applied to the column
Metric
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
Goal
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
Decrease the manual copying of transcripts
Metric
No sort applied to the column
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
decrease by X %
Change emoji User interaction and design
User interaction and design
Change emoji User interaction and design
Simple setup flow:
Generate API key / connect
MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus:
usable by non-technical users
Change emoji Detailed Requirements
Detailed Requirements
Change emoji Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
No sort applied to the column
Importance
No sort applied to the column
Notes
No sort applied to the column
Requirement
No sort applied to the column
Importance
No sort applied to the column
Notes
No sort applied to the column
Requirement
No sort applied to the column
Importance
No sort applied to the column
Notes
No sort applied to the column
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide
MCP
endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH
For each deal expose:
calls/meetings/emails
Identified risks
contacts related to the deal with their job title
linked account details
all CRM field information that we have in
DI
+ fields used for CRM filling
example - get_deal_summary
request -
{
"tool": "get_deal_summary",
"arguments": {
"deal_name": "ACME"
}
}
responce:
{
"deal_name": "ACME",
"stage": "Negotiation",
"recent_activity": [
"Demo call on 2026-04-01",
"Pricing discussion on 2026-04-03"
],
"risks": [
"Pricing concerns",
"Security review pending"
], "next_steps": [
"Send follow-up to VP Sales",
"Share security documentation"
]
}
Ensure the AI can easily understand the schema of the data coming from Jiminny
HIGH
Models perform much better when tools have:
clear names
rich descriptions
predictable schemas
Provide clear, actionable error states for customers when
MCP
connections fail
HIGH
Error messages should guide agents toward solutions with specific suggestions and next steps.
Expired or revoked key
— “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the
MCP
server.”
Rate limit hit
— “Error: Rate limit exceeded. Too many requests have been made to the Jiminny
MCP
server in a short period. Please wait 60 seconds before retrying.”
Permissions mismatch
— if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”
Trying to access private meeting
- “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the
MCP
connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”
Downgraded/Capture account
— if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error:
MCP
access unavailable. The Jiminny
MCP
connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”
Jiminny-side outage
— “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”
No data found
- “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”
Allow customers to securely control who and what data is accessible externally
HIGH
Ensure
MCP
only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the
MCP
as well.
Private meetings shouldn’t be exposed via
MCP
HIGH
When a meetings is set to Private then the information about it shouldn’t be exposed through the
MCP
.
Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows
HIGH
Ensure data is structured to be easily combined with:
CRM (accounts, deals)
external docs
Use consistent identifiers:
account IDs...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
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Product Strategy 2025+ Change emoji
Product Strategy 2025+
Product Strategy 2025+
Competitive analysis Change emoji
Competitive analysis
Competitive analysis
Ways of working - Product processes Change emoji
Ways of working - Product processes
Ways of working - Product processes
Core Product Performance Metrics 📈
Core Product Performance Metrics
📈
Core Product Performance Metrics
Product Documentation Change emoji
Product Documentation
Product Documentation
Product Briefs Change emoji
Product Briefs
Product Briefs
Change emoji Activity Export
Activity Export
Change emoji Autologging activities
Autologging activities
Deal Insights - Multiple currencies
Deal Insights - Multiple currencies
Reinvent Themes & Topics (and ACS)
Reinvent Themes & Topics (and ACS)
Change emoji Billing Portal
Billing Portal
Change emoji Upload Video/Audio Recordings
Upload Video/Audio Recordings
White-Label Jiminny Instance
White-Label Jiminny Instance
Win/Loss Analysis for a Deal
Win/Loss Analysis for a Deal
Change emoji Hubspot app
Hubspot app
Change emoji Automatically hard delete data for churned customers
Automatically hard delete data for churned customers
Change emoji Ask Jiminny Anything on Call level
Ask Jiminny Anything on Call level
Change emoji Ask Jiminny for Open and Closed Deals
Ask Jiminny for Open and Closed Deals
Change emoji Ask Jiminny Anything on Deal level
Ask Jiminny Anything on Deal level
Change emoji Automatically record all calendar meetings
Automatically record all calendar meetings
Change emoji Product Tiering
Product Tiering
Change emoji Recording Consent
Recording Consent
Change emoji Automated CRM Filling
Automated CRM Filling
Change emoji Automated Exec Reports
Automated Exec Reports
Change emoji Auto-detect Activity Type
Auto-detect Activity Type
AI Signals & Alerts
AI Signals & Alerts
Change emoji AJA on Anything
AJA on Anything
Change emoji AI Call Scoring
AI Call Scoring
Jiminny MCP Connector
Jiminny MCP Connector
Desktop app to record a meetings without visible Notetaker
Desktop app to record a meetings without visible Notetaker
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Jiminny MCP Connector
Updated 10m ago
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
Read time 12 min
12 min
Views 14
14
Add a reaction
Add a reaction
Epic
Link to Epik in Jira
Document status
DRAFT
Epic
Document status
Link to Epik in Jira
DRAFT
Change emoji Objective
Objective
Change emoji Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a
data layer for AI-driven revenue workflows
, not just a standalone product.
👤 Target user
👤 Target user
👤 Target user
Revenue teams using AI tools (Sales,
CS
, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
🤕
🤕
Pain point or problem
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
💥
💥
Impact and benefits
💥 Impact and benefits
For customers
For customers
For customers...
|
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
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Core Product Performance Metrics 📈
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Product Documentation Change emoji
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Deal Insights - Multiple currencies
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Reinvent Themes & Topics (and ACS)
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Change emoji Billing Portal
Billing Portal
Change emoji Upload Video/Audio Recordings
Upload Video/Audio Recordings
White-Label Jiminny Instance
White-Label Jiminny Instance
Win/Loss Analysis for a Deal
Win/Loss Analysis for a Deal
Change emoji Hubspot app
Hubspot app
Change emoji Automatically hard delete data for churned customers
Automatically hard delete data for churned customers
Change emoji Ask Jiminny Anything on Call level
Ask Jiminny Anything on Call level
Change emoji Ask Jiminny for Open and Closed Deals
Ask Jiminny for Open and Closed Deals
Change emoji Ask Jiminny Anything on Deal level
Ask Jiminny Anything on Deal level
Change emoji Automatically record all calendar meetings
Automatically record all calendar meetings
Change emoji Product Tiering
Product Tiering
Change emoji Recording Consent
Recording Consent
Change emoji Automated CRM Filling
Automated CRM Filling
Change emoji Automated Exec Reports
Automated Exec Reports
Change emoji Auto-detect Activity Type
Auto-detect Activity Type
AI Signals & Alerts
AI Signals & Alerts
Change emoji AJA on Anything
AJA on Anything
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AI Call Scoring
Jiminny MCP Connector
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Desktop app to record a meetings without visible Notetaker
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
Read time 12 min
12 min
Views 14
14
Add a reaction
Add a reaction
Epic
Link to Epik in Jira
Document status
DRAFT
Epic
Document status
Link to Epik in Jira
DRAFT
Change emoji Objective
Objective
Change emoji Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a
data layer for AI-driven revenue workflows
, not just a standalone product.
👤 Target user
👤 Target user
👤 Target user
Revenue teams using AI tools (Sales,
CS
, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
🤕
🤕
Pain point or problem
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
💥
💥
Impact and benefits
💥 Impact and benefits
For customers
For customers
For customers...
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2026-04-22T14:32:49.862264+00:00
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/Users/lukas/.screenpipe/data/data/2026-04-22/1776 /Users/lukas/.screenpipe/data/data/2026-04-22/1776868369862_m1.jpg...
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Firefox
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Jiminny MCP Connector - Product - Confluence — Wor Jiminny MCP Connector - Product - Confluence — Work...
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True
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jiminny.atlassian.net/wiki/spaces/PROD/pages/37282 jiminny.atlassian.net/wiki/spaces/PROD/pages/3728244737/Jiminny+MCP+Connector...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
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Product Strategy 2025+ Change emoji
Product Strategy 2025+
Product Strategy 2025+
Competitive analysis Change emoji
Competitive analysis
Competitive analysis
Ways of working - Product processes Change emoji
Ways of working - Product processes
Ways of working - Product processes
Core Product Performance Metrics 📈
Core Product Performance Metrics
📈
Core Product Performance Metrics
Product Documentation Change emoji
Product Documentation
Product Documentation
Product Briefs Change emoji
Product Briefs
Product Briefs
Change emoji Activity Export
Activity Export
Change emoji Autologging activities
Autologging activities
Deal Insights - Multiple currencies
Deal Insights - Multiple currencies
Reinvent Themes & Topics (and ACS)
Reinvent Themes & Topics (and ACS)
Change emoji Billing Portal
Billing Portal
Change emoji Upload Video/Audio Recordings
Upload Video/Audio Recordings
White-Label Jiminny Instance
White-Label Jiminny Instance
Win/Loss Analysis for a Deal
Win/Loss Analysis for a Deal
Change emoji Hubspot app
Hubspot app
Change emoji Automatically hard delete data for churned customers
Automatically hard delete data for churned customers
Change emoji Ask Jiminny Anything on Call level
Ask Jiminny Anything on Call level
Change emoji Ask Jiminny for Open and Closed Deals
Ask Jiminny for Open and Closed Deals
Change emoji Ask Jiminny Anything on Deal level
Ask Jiminny Anything on Deal level
Change emoji Automatically record all calendar meetings
Automatically record all calendar meetings
Change emoji Product Tiering
Product Tiering
Change emoji Recording Consent
Recording Consent
Change emoji Automated CRM Filling
Automated CRM Filling
Change emoji Automated Exec Reports
Automated Exec Reports
Change emoji Auto-detect Activity Type
Auto-detect Activity Type
AI Signals & Alerts
AI Signals & Alerts
Change emoji AJA on Anything
AJA on Anything
Change emoji AI Call Scoring
AI Call Scoring
Jiminny MCP Connector
Jiminny MCP Connector
Desktop app to record a meetings without visible Notetaker
Desktop app to record a meetings without visible Notetaker
Feedback Change emoji
Feedback
Feedback
Research & User Feedback Change emoji
Research & User Feedback
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Jiminny MCP Connector
Updated 10m ago
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
Read time 12 min
12 min
Views 14
14
Add a reaction
Add a reaction
Epic
Link to Epik in Jira
Document status
DRAFT
Epic
Document status
Link to Epik in Jira
DRAFT
Change emoji Objective
Objective
Change emoji Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a
data layer for AI-driven revenue workflows
, not just a standalone product.
👤 Target user
👤 Target user
👤 Target user
Revenue teams using AI tools (Sales,
CS
, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
🤕
🤕
Pain point or problem
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
💥
💥
Impact and benefits
💥 Impact and benefits
For customers
For customers
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
For Jiminny
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
💡
💡
Solution ideas
💡 Solution ideas
Build an
AI Knowledge Connector (
MCP
-based)
that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides
structured insights
(summaries, action items, scoring etc.)
Supports
deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Change emoji Success metrics
Success metrics
Change emoji Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
No sort applied to the column
Metric
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
Goal
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
Decrease the manual copying of transcripts...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
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Jiminny MCP Connector
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
Read time 12 min
12 min
Views 14
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Change emoji Objective
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Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a
data layer for AI-driven revenue workflows
, not just a standalone product.
👤 Target user
👤 Target user
👤 Target user
Revenue teams using AI tools (Sales,
CS
, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
🤕
🤕
Pain point or problem
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
💥
💥
Impact and benefits
💥 Impact and benefits
For customers
For customers
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
For Jiminny
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
💡
💡
Solution ideas
💡 Solution ideas
Build an
AI Knowledge Connector (
MCP
-based)
that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides
structured insights
(summaries, action items, scoring etc.)
Supports
deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Change emoji Success metrics
Success metrics
Change emoji Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
No sort applied to the column
Metric
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
Goal
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
Decrease the manual copying of transcripts...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
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White-Label Jiminny Instance
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Ask Jiminny for Open and Closed Deals
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Ask Jiminny Anything on Deal level
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Jiminny MCP Connector
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
Read time 12 min
12 min
Views 14
14
Add a reaction
Add a reaction
Epic
Link to Epik in Jira
Document status
DRAFT
Epic
Document status
Link to Epik in Jira
DRAFT
Change emoji Objective
Objective
Change emoji Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a
data layer for AI-driven revenue workflows
, not just a standalone product.
👤 Target user
👤 Target user
👤 Target user
Revenue teams using AI tools (Sales,
CS
, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
🤕
🤕
Pain point or problem
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
💥
💥
Impact and benefits
💥 Impact and benefits
For customers
For customers
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
For Jiminny
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
💡
💡
Solution ideas
💡 Solution ideas
Build an
AI Knowledge Connector (
MCP
-based)
that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides
structured insights
(summaries, action items, scoring etc.)
Supports
deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Change emoji Success metrics
Success metrics
Change emoji Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
No sort applied to the column
Metric
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
Goal
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
Decrease the manual copying of transcripts
Metric
No sort applied to the column
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
decrease by X %
Change emoji User interaction and design
User interaction and design
Change emoji User interaction and design
Simple setup flow:
Generate API key / connect
MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus:
usable by non-technical users
Change emoji Detailed Requirements
Detailed Requirements
Change emoji Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
No sort applied to the column
Importance
No sort applied to the column
Notes
No sort applied to the column
Requirement
No sort applied to the column
Importance
No sort applied to the column
Notes
No sort applied to the column
Requirement
No sort applied to the column
Importance
No sort applied to the column
Notes
No sort applied to the column
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide
MCP
endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH
For each deal expose:
calls/meetings/emails
Identified risks
contacts related to the deal with their job title
linked account details
all CRM field information that we have in
DI
+ fields used for CRM filling
example - get_deal_summary
request -
{
"tool": "get_deal_summary",
"arguments": {
"deal_name": "ACME"
}
}
responce:
{
"deal_name": "ACME",
"stage": "Negotiation",
"recent_activity": [
"Demo call on 2026-04-01",
"Pricing discussion on 2026-04-03"
],
"risks": [
"Pricing concerns",
"Security review pending"
], "next_steps": [
"Send follow-up to VP Sales",
"Share security documentation"
]
}
Ensure the AI can easily understand the schema of the data coming from Jiminny
HIGH
Models perform much better when tools have:
clear names
rich descriptions
predictable schemas
Provide clear, actionable error states for customers when
MCP
connections fail
HIGH
Error messages should guide agents toward solutions with specific suggestions and next steps.
Expired or revoked key
— “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the
MCP
server.”
Rate limit hit
— “Error: Rate limit exceeded. Too many requests have been made to the Jiminny
MCP
server in a short period. Please wait 60 seconds before retrying.”
Permissions mismatch
— if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”
Trying to access private meeting
- “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the
MCP
connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”
Downgraded/Capture account
— if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error:
MCP
access unavailable. The Jiminny
MCP
connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”
Jiminny-side outage
— “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”...
|
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The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"MCP","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"server.”","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Rate limit hit","depth":23,"bounds":{"left":0.8796542,"top":0.0,"width":0.031914894,"height":0.01556265},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"— “Error: Rate limit exceeded. Too many requests have been made to the Jiminny","depth":22,"bounds":{"left":0.8796542,"top":0.0,"width":0.08976064,"height":0.059457302},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"MCP","depth":22,"bounds":{"left":0.945645,"top":0.0,"width":0.011968086,"height":0.01556265},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"server in a short period. Please wait 60 seconds before retrying.”","depth":22,"bounds":{"left":0.8796542,"top":0.0,"width":0.09075798,"height":0.059457302},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Permissions mismatch","depth":23,"bounds":{"left":0.8796542,"top":0.022346368,"width":0.05851064,"height":0.01556265},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"— if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”","depth":22,"bounds":{"left":0.8796542,"top":0.022346368,"width":0.093417555,"height":0.16839585},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Trying to access private meeting","depth":23,"bounds":{"left":0.8796542,"top":0.20031923,"width":0.08460771,"height":0.01556265},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"- “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the","depth":22,"bounds":{"left":0.8796542,"top":0.20031923,"width":0.091090426,"height":0.10335196},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"MCP","depth":22,"bounds":{"left":0.9099069,"top":0.28810853,"width":0.012134309,"height":0.01556265},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”","depth":22,"bounds":{"left":0.8796542,"top":0.28810853,"width":0.09125665,"height":0.08100559},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Downgraded/Capture account","depth":23,"bounds":{"left":0.8796542,"top":0.37869114,"width":0.07829122,"height":0.01556265},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"— if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error:","depth":22,"bounds":{"left":0.8796542,"top":0.37869114,"width":0.09325133,"height":0.08100559},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"MCP","depth":22,"bounds":{"left":0.9368351,"top":0.4441341,"width":0.012134309,"height":0.01556265},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"access unavailable. The Jiminny","depth":22,"bounds":{"left":0.8796542,"top":0.4441341,"width":0.08976064,"height":0.037509978},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"MCP","depth":22,"bounds":{"left":0.94265294,"top":0.4660814,"width":0.011968086,"height":0.01556265},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”","depth":22,"bounds":{"left":0.8796542,"top":0.4660814,"width":0.08926197,"height":0.10335196},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny-side outage","depth":23,"bounds":{"left":0.8796542,"top":0.5786113,"width":0.053025264,"height":0.01556265},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"— “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”","depth":22,"bounds":{"left":0.8796542,"top":0.5786113,"width":0.09358378,"height":0.10335196},"help_text":"","role_description":"text","subrole":"AXUnknown"}]...
|
-5452217581881413622
|
-8728613913459198283
|
visual_change
|
accessibility
|
NULL
|
Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
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Product Strategy 2025+ Change emoji
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Ways of working - Product processes Change emoji
Ways of working - Product processes
Ways of working - Product processes
Core Product Performance Metrics 📈
Core Product Performance Metrics
📈
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Product Documentation Change emoji
Product Documentation
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Product Briefs Change emoji
Product Briefs
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Change emoji Activity Export
Activity Export
Change emoji Autologging activities
Autologging activities
Deal Insights - Multiple currencies
Deal Insights - Multiple currencies
Reinvent Themes & Topics (and ACS)
Reinvent Themes & Topics (and ACS)
Change emoji Billing Portal
Billing Portal
Change emoji Upload Video/Audio Recordings
Upload Video/Audio Recordings
White-Label Jiminny Instance
White-Label Jiminny Instance
Win/Loss Analysis for a Deal
Win/Loss Analysis for a Deal
Change emoji Hubspot app
Hubspot app
Change emoji Automatically hard delete data for churned customers
Automatically hard delete data for churned customers
Change emoji Ask Jiminny Anything on Call level
Ask Jiminny Anything on Call level
Change emoji Ask Jiminny for Open and Closed Deals
Ask Jiminny for Open and Closed Deals
Change emoji Ask Jiminny Anything on Deal level
Ask Jiminny Anything on Deal level
Change emoji Automatically record all calendar meetings
Automatically record all calendar meetings
Change emoji Product Tiering
Product Tiering
Change emoji Recording Consent
Recording Consent
Change emoji Automated CRM Filling
Automated CRM Filling
Change emoji Automated Exec Reports
Automated Exec Reports
Change emoji Auto-detect Activity Type
Auto-detect Activity Type
AI Signals & Alerts
AI Signals & Alerts
Change emoji AJA on Anything
AJA on Anything
Change emoji AI Call Scoring
AI Call Scoring
Jiminny MCP Connector
Jiminny MCP Connector
Desktop app to record a meetings without visible Notetaker
Desktop app to record a meetings without visible Notetaker
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Jiminny MCP Connector
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
Read time 12 min
12 min
Views 14
14
Add a reaction
Add a reaction
Epic
Link to Epik in Jira
Document status
DRAFT
Epic
Document status
Link to Epik in Jira
DRAFT
Change emoji Objective
Objective
Change emoji Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a
data layer for AI-driven revenue workflows
, not just a standalone product.
👤 Target user
👤 Target user
👤 Target user
Revenue teams using AI tools (Sales,
CS
, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
🤕
🤕
Pain point or problem
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
💥
💥
Impact and benefits
💥 Impact and benefits
For customers
For customers
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
For Jiminny
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
💡
💡
Solution ideas
💡 Solution ideas
Build an
AI Knowledge Connector (
MCP
-based)
that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides
structured insights
(summaries, action items, scoring etc.)
Supports
deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Change emoji Success metrics
Success metrics
Change emoji Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
No sort applied to the column
Metric
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
Goal
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
Decrease the manual copying of transcripts
Metric
No sort applied to the column
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
decrease by X %
Change emoji User interaction and design
User interaction and design
Change emoji User interaction and design
Simple setup flow:
Generate API key / connect
MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus:
usable by non-technical users
Change emoji Detailed Requirements
Detailed Requirements
Change emoji Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
No sort applied to the column
Importance
No sort applied to the column
Notes
No sort applied to the column
Requirement
No sort applied to the column
Importance
No sort applied to the column
Notes
No sort applied to the column
Requirement
No sort applied to the column
Importance
No sort applied to the column
Notes
No sort applied to the column
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide
MCP
endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH
For each deal expose:
calls/meetings/emails
Identified risks
contacts related to the deal with their job title
linked account details
all CRM field information that we have in
DI
+ fields used for CRM filling
example - get_deal_summary
request -
{
"tool": "get_deal_summary",
"arguments": {
"deal_name": "ACME"
}
}
responce:
{
"deal_name": "ACME",
"stage": "Negotiation",
"recent_activity": [
"Demo call on 2026-04-01",
"Pricing discussion on 2026-04-03"
],
"risks": [
"Pricing concerns",
"Security review pending"
], "next_steps": [
"Send follow-up to VP Sales",
"Share security documentation"
]
}
Ensure the AI can easily understand the schema of the data coming from Jiminny
HIGH
Models perform much better when tools have:
clear names
rich descriptions
predictable schemas
Provide clear, actionable error states for customers when
MCP
connections fail
HIGH
Error messages should guide agents toward solutions with specific suggestions and next steps.
Expired or revoked key
— “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the
MCP
server.”
Rate limit hit
— “Error: Rate limit exceeded. Too many requests have been made to the Jiminny
MCP
server in a short period. Please wait 60 seconds before retrying.”
Permissions mismatch
— if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”
Trying to access private meeting
- “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the
MCP
connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”
Downgraded/Capture account
— if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error:
MCP
access unavailable. The Jiminny
MCP
connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”
Jiminny-side outage
— “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”...
|
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1739
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2026-04-22T14:33:07.058510+00:00
|
/Users/lukas/.screenpipe/data/data/2026-04-22/1776 /Users/lukas/.screenpipe/data/data/2026-04-22/1776868387058_m2.jpg...
|
Firefox
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Jiminny MCP Connector - Product - Confluence — Wor Jiminny MCP Connector - Product - Confluence — Work...
|
True
|
jiminny.atlassian.net/wiki/spaces/PROD/pages/37282 jiminny.atlassian.net/wiki/spaces/PROD/pages/3728244737/Jiminny+MCP+Connector...
|
monitor_2
|
NULL
|
NULL
|
NULL
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NULL
|
Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
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Product Strategy 2025+ Change emoji
Product Strategy 2025+
Product Strategy 2025+
Competitive analysis Change emoji
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Competitive analysis
Ways of working - Product processes Change emoji
Ways of working - Product processes
Ways of working - Product processes
Core Product Performance Metrics 📈
Core Product Performance Metrics
📈
Core Product Performance Metrics
Product Documentation Change emoji
Product Documentation
Product Documentation
Product Briefs Change emoji
Product Briefs
Product Briefs
Change emoji Activity Export
Activity Export
Change emoji Autologging activities
Autologging activities
Deal Insights - Multiple currencies
Deal Insights - Multiple currencies
Reinvent Themes & Topics (and ACS)
Reinvent Themes & Topics (and ACS)
Change emoji Billing Portal
Billing Portal
Change emoji Upload Video/Audio Recordings
Upload Video/Audio Recordings
White-Label Jiminny Instance
White-Label Jiminny Instance
Win/Loss Analysis for a Deal
Win/Loss Analysis for a Deal
Change emoji Hubspot app
Hubspot app
Change emoji Automatically hard delete data for churned customers
Automatically hard delete data for churned customers
Change emoji Ask Jiminny Anything on Call level
Ask Jiminny Anything on Call level
Change emoji Ask Jiminny for Open and Closed Deals
Ask Jiminny for Open and Closed Deals
Change emoji Ask Jiminny Anything on Deal level
Ask Jiminny Anything on Deal level
Change emoji Automatically record all calendar meetings
Automatically record all calendar meetings
Change emoji Product Tiering
Product Tiering
Change emoji Recording Consent
Recording Consent
Change emoji Automated CRM Filling
Automated CRM Filling
Change emoji Automated Exec Reports
Automated Exec Reports
Change emoji Auto-detect Activity Type
Auto-detect Activity Type
AI Signals & Alerts
AI Signals & Alerts
Change emoji AJA on Anything
AJA on Anything
Change emoji AI Call Scoring
AI Call Scoring
Jiminny MCP Connector
Jiminny MCP Connector
Desktop app to record a meetings without visible Notetaker
Desktop app to record a meetings without visible Notetaker
Feedback Change emoji
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Jiminny MCP Connector
Updated 10m ago
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
Read time 12 min
12 min
Views 14
14
Add a reaction
Add a reaction
Epic
Link to Epik in Jira
Document status
DRAFT
Epic
Document status
Link to Epik in Jira
DRAFT
Change emoji Objective
Objective
Change emoji Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a
data layer for AI-driven revenue workflows
, not just a standalone product.
👤 Target user
👤 Target user
👤 Target user
Revenue teams using AI tools (Sales,
CS
, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
🤕
🤕
Pain point or problem
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
💥
💥
Impact and benefits
💥 Impact and benefits
For customers
For customers
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
For Jiminny
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
💡
💡
Solution ideas
💡 Solution ideas
Build an
AI Knowledge Connector (
MCP
-based)
that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides
structured insights
(summaries, action items, scoring etc.)
Supports
deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Change emoji Success metrics
Success metrics
Change emoji Success metrics...
|
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
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Product Strategy 2025+ Change emoji
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Ways of working - Product processes
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Billing Portal
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Upload Video/Audio Recordings
White-Label Jiminny Instance
White-Label Jiminny Instance
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Win/Loss Analysis for a Deal
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Hubspot app
Change emoji Automatically hard delete data for churned customers
Automatically hard delete data for churned customers
Change emoji Ask Jiminny Anything on Call level
Ask Jiminny Anything on Call level
Change emoji Ask Jiminny for Open and Closed Deals
Ask Jiminny for Open and Closed Deals
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Ask Jiminny Anything on Deal level
Change emoji Automatically record all calendar meetings
Automatically record all calendar meetings
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Product Tiering
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Recording Consent
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Automated CRM Filling
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Automated Exec Reports
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Jiminny MCP Connector
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Jiminny MCP Connector
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
Read time 12 min
12 min
Views 14
14
Add a reaction
Add a reaction
Epic
Link to Epik in Jira
Document status
DRAFT
Epic
Document status
Link to Epik in Jira
DRAFT
Change emoji Objective
Objective
Change emoji Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a
data layer for AI-driven revenue workflows
, not just a standalone product.
👤 Target user
👤 Target user
👤 Target user
Revenue teams using AI tools (Sales,
CS
, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
🤕
🤕
Pain point or problem
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
💥
💥
Impact and benefits
💥 Impact and benefits
For customers
For customers
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
For Jiminny
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
💡
💡
Solution ideas
💡 Solution ideas
Build an
AI Knowledge Connector (
MCP
-based)
that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides
structured insights
(summaries, action items, scoring etc.)
Supports
deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Change emoji Success metrics
Success metrics
Change emoji Success metrics...
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Jiminny MCP Connector - Product - Confluence — Wor Jiminny MCP Connector - Product - Confluence — Work...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
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You said what is the oauth required for mcp for clauide code and claude chat
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Defining OAuth for MCP
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Upload Video/Audio Recordings
White-Label Jiminny Instance
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Ask Jiminny for Open and Closed Deals
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Ask Jiminny Anything on Deal level
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Jiminny MCP Connector
Updated 10m ago
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
Read time 12 min
12 min
Views 14
14
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Epic
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Document status
DRAFT
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DRAFT
Change emoji Objective
Objective
Change emoji Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a
data layer for AI-driven revenue workflows
, not just a standalone product.
👤 Target user
👤 Target user
👤 Target user
Revenue teams using AI tools (Sales,
CS
, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
🤕
🤕
Pain point or problem
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
💥
💥
Impact and benefits
💥 Impact and benefits
For customers
For customers
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
For Jiminny
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
💡
💡
Solution ideas
💡 Solution ideas
Build an
AI Knowledge Connector (
MCP
-based)
that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides
structured insights
(summaries, action items, scoring etc.)
Supports
deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Change emoji Success metrics
Success metrics
Change emoji Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
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Metric
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Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
Goal
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
Decrease the manual copying of transcripts
Metric
No sort applied to the column
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
decrease by X %
Change emoji User interaction and design
User interaction and design
Change emoji User interaction and design
Simple setup flow:
Generate API key / connect
MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases...
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customers","depth":16,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Better AI outputs using real customer conversations","depth":18,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ability to build automated workflows across tools","depth":18,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"For Jiminny","depth":15,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"For Jiminny","depth":17,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"For Jiminny","depth":16,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Increased product stickiness (embedded in workflows)","depth":18,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Competitive parity with Gong / Avoma etc.","depth":18,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"💡 Solution ideas","depth":15,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXLink","text":"💡","depth":16,"help_text":"https://emojipedia.org/light-bulb/","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"💡","depth":17,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Solution ideas","depth":16,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"💡 Solution ideas","depth":16,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Build an","depth":16,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"AI Knowledge Connector (","depth":17,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"MCP","depth":18,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"-based)","depth":17,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"that:","depth":16,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Exposes Jiminny data (calls, transcripts, metadata) to AI tools","depth":18,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provides","depth":18,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"structured insights","depth":19,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"(summaries, action items, scoring etc.)","depth":18,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Supports","depth":18,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"deal-level context","depth":19,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)","depth":18,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"Change emoji Success metrics","depth":15,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Success metrics","depth":16,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Change emoji Success metrics","depth":16,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).","depth":16,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Goal","depth":21,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"No sort applied to the column","depth":20,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Metric","depth":21,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"No sort applied to the column","depth":20,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"% of customers connecting external AI tools","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Number of","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"MCP","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"/API calls per customer - and which ones are being used the most","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Decrease the manual copying of transcripts","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"decrease by X %","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Goal","depth":21,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"No sort applied to the column","depth":20,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Decrease the manual copying of transcripts","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Metric","depth":21,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"No sort applied to the column","depth":20,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"% of customers connecting external AI tools","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Number of","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"MCP","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"/API calls per customer - and which ones are being used the most","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"decrease by X %","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"Change emoji User interaction and design","depth":15,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"User interaction and design","depth":16,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Change emoji User interaction and design","depth":16,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Simple setup flow:","depth":18,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Generate API key / connect","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"MCP","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Clear instructions for Claude / Zapier setup","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide:","depth":18,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Example prompts for Claude","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Pre-built workflow templates for Zapier","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Clear documentation - with use cases","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"}]...
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-2378556027027145035
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
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Defining OAuth for MCP
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Core Product Performance Metrics 📈
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Product Documentation Change emoji
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Deal Insights - Multiple currencies
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Reinvent Themes & Topics (and ACS)
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Change emoji Upload Video/Audio Recordings
Upload Video/Audio Recordings
White-Label Jiminny Instance
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Change emoji Hubspot app
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Change emoji Automatically hard delete data for churned customers
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Change emoji Ask Jiminny Anything on Call level
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Jiminny MCP Connector
Updated 10m ago
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
Read time 12 min
12 min
Views 14
14
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Add a reaction
Epic
Link to Epik in Jira
Document status
DRAFT
Epic
Document status
Link to Epik in Jira
DRAFT
Change emoji Objective
Objective
Change emoji Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a
data layer for AI-driven revenue workflows
, not just a standalone product.
👤 Target user
👤 Target user
👤 Target user
Revenue teams using AI tools (Sales,
CS
, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
🤕
🤕
Pain point or problem
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
💥
💥
Impact and benefits
💥 Impact and benefits
For customers
For customers
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
For Jiminny
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
💡
💡
Solution ideas
💡 Solution ideas
Build an
AI Knowledge Connector (
MCP
-based)
that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides
structured insights
(summaries, action items, scoring etc.)
Supports
deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Change emoji Success metrics
Success metrics
Change emoji Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
No sort applied to the column
Metric
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
Goal
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
Decrease the manual copying of transcripts
Metric
No sort applied to the column
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
decrease by X %
Change emoji User interaction and design
User interaction and design
Change emoji User interaction and design
Simple setup flow:
Generate API key / connect
MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases...
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2026-04-22T14:33:39.727570+00:00
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/Users/lukas/.screenpipe/data/data/2026-04-22/1776 /Users/lukas/.screenpipe/data/data/2026-04-22/1776868419727_m2.jpg...
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Jiminny MCP Connector - Product - Confluence — Wor Jiminny MCP Connector - Product - Confluence — Work...
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jiminny.atlassian.net/wiki/spaces/PROD/pages/37282 jiminny.atlassian.net/wiki/spaces/PROD/pages/3728244737/Jiminny+MCP+Connector...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
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You said what is the oauth required for mcp for clauide code and claude chat
You said
what is the oauth required for mcp for clauide code and claude chat
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Gemini said
Gemini said
To understand the OAuth requirements for the Model Context Protocol (MCP) in Claude Code and Claude Chat,
it helps to look at how MCP handles secure connections.
MCP acts as a universal "USB port" that allows AI models to connect to external data sources and tools.
When connecting to remote,
HTTP-based MCP servers,
Anthropic relies on the
OAuth 2.1
authorization framework.
This ensures that Claude can access your protected tools securely using temporary tokens,
rather than requiring you to copy and paste long-lived API keys,
passwords,
or configuration secrets.
Here is exactly what is required and how OAuth is handled across both environments.
The Underlying OAuth Flow for MCP
The Underlying OAuth Flow for MCP
Whether you are using Claude Code or Claude Chat,
the underlying architecture is identical.
The Claude application acts as the
OAuth Client
,
and the remote tool you are connecting to acts as the
Resource Server
.
To build or connect to an MCP server,
the OAuth integration must support this standard discovery and token exchange flow:
Initial Handshake (Discovery):
Claude attempts to connect to the MCP server without credentials.
The server must reject this with a
401 Unauthorized
HTTP status and include a
WWW-Authenticate
header pointing to a Protected Resource Metadata (PRM) document.
Metadata Fetch:
Claude reads the PRM document to locate your specific Authorization Server (e.
g.,
Okta,
Auth0,
Clerk,
or a custom auth server).
User Consent:
Claude prompts the user to authenticate.
The user is temporarily redirected to a browser window to log in and explicitly grant permission (e.
g.,
"Allow Claude to access your GitHub repositories").
Token Exchange:
Claude receives a temporary authorization code via a redirect,
which it silently exchanges for a long-lived access token.
Access Granted:
Claude now includes this token in the header of subsequent requests to invoke tools and read data on your behalf.
MCP OAuth in Claude Code
MCP OAuth in Claude Code
Claude Code is Anthropic's command-line interface (CLI) tool for software engineering.
When you connect Claude Code to a protected remote MCP server,
the OAuth flow is adapted for the terminal:
Browser Handoff:
When Claude Code hits an MCP server that requires OAuth,
it will generate a secure authorization link and automatically open your default desktop web browser (or output the URL to the terminal for headless environments).
Authentication:
Once you log in and approve access in the browser,
the authorization server redirects the payload back to a local loopback port managed by Claude Code,
handing off the authorization code.
Alternative (Static Headers):
If setting up a full OAuth 2.
1 server is too complex for a quick internal tool,
Claude Code also allows you to bypass the browser flow by injecting static bearer tokens directly via the CLI.
You can do this using the
--header
flag (e.
g.,
claude mcp add my-server --transport http --header "Authorization: Bearer your-token"
).
MCP OAuth in Claude Chat (Claude Desktop & Web)
MCP OAuth in Claude Chat (Claude Desktop & Web)
"Claude Chat" encompasses the visual interfaces—both the Claude Desktop application and the Claude.
Claude.ai (Web Custom Connectors):
When you add a remote MCP server via the "Custom Connectors" menu in your settings,
Claude.
ai handles the OAuth flow entirely in the browser.
You simply paste the MCP server URL.
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Product Strategy 2025+ Change emoji
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Ways of working - Product processes Change emoji
Ways of working - Product processes
Ways of working - Product processes
Core Product Performance Metrics 📈
Core Product Performance Metrics
📈
Core Product Performance Metrics
Product Documentation Change emoji
Product Documentation
Product Documentation
Product Briefs Change emoji
Product Briefs
Product Briefs
Change emoji Activity Export
Activity Export
Change emoji Autologging activities
Autologging activities
Deal Insights - Multiple currencies
Deal Insights - Multiple currencies
Reinvent Themes & Topics (and ACS)
Reinvent Themes & Topics (and ACS)
Change emoji Billing Portal
Billing Portal
Change emoji Upload Video/Audio Recordings
Upload Video/Audio Recordings
White-Label Jiminny Instance
White-Label Jiminny Instance
Win/Loss Analysis for a Deal
Win/Loss Analysis for a Deal
Change emoji Hubspot app
Hubspot app
Change emoji Automatically hard delete data for churned customers
Automatically hard delete data for churned customers
Change emoji Ask Jiminny Anything on Call level
Ask Jiminny Anything on Call level
Change emoji Ask Jiminny for Open and Closed Deals
Ask Jiminny for Open and Closed Deals
Change emoji Ask Jiminny Anything on Deal level
Ask Jiminny Anything on Deal level
Change emoji Automatically record all calendar meetings
Automatically record all calendar meetings
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Product Tiering
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Recording Consent
Change emoji Automated CRM Filling
Automated CRM Filling
Change emoji Automated Exec Reports
Automated Exec Reports
Change emoji Auto-detect Activity Type
Auto-detect Activity Type
AI Signals & Alerts
AI Signals & Alerts
Change emoji AJA on Anything
AJA on Anything
Change emoji AI Call Scoring
AI Call Scoring
Jiminny MCP Connector
Jiminny MCP Connector
Desktop app to record a meetings without visible Notetaker
Desktop app to record a meetings without visible Notetaker
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Jiminny MCP Connector
Updated 10m ago
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
Read time 12 min
12 min
Views 14
14
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Add a reaction
Epic
Link to Epik in Jira
Document status
DRAFT
Epic
Document status
Link to Epik in Jira
DRAFT
Change emoji Objective
Objective
Change emoji Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a
data layer for AI-driven revenue workflows
, not just a standalone product.
👤 Target user
👤 Target user
👤 Target user
Revenue teams using AI tools (Sales,
CS
, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
🤕
🤕
Pain point or problem
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
💥
💥
Impact and benefits
💥 Impact and benefits
For customers
For customers
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
For Jiminny
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
💡
💡
Solution ideas
💡 Solution ideas
Build an
AI Knowledge Connector (
MCP
-based)
that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides
structured insights
(summaries, action items, scoring etc.)
Supports
deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Change emoji Success metrics
Success metrics
Change emoji Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
No sort applied to the column
Metric
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Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
Goal
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
Decrease the manual copying of transcripts
Metric
No sort applied to the column
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
decrease by X %
Change emoji User interaction and design
User interaction and design
Change emoji User interaction and design
Simple setup flow:
Generate API key / connect
MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus:
usable by non-technical users
Change emoji Detailed Requirements
Detailed Requirements
Change emoji Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
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Importance
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Notes
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Requirement
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Importance
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Notes
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Requirement
No sort applied to the column...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
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You said what is the oauth required for mcp for clauide code and claude chat
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what is the oauth required for mcp for clauide code and claude chat
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Gemini said
Gemini said
To understand the OAuth requirements for the Model Context Protocol (MCP) in Claude Code and Claude Chat,
it helps to look at how MCP handles secure connections.
MCP acts as a universal "USB port" that allows AI models to connect to external data sources and tools.
When connecting to remote,
HTTP-based MCP servers,
Anthropic relies on the
OAuth 2.1
authorization framework.
This ensures that Claude can access your protected tools securely using temporary tokens,
rather than requiring you to copy and paste long-lived API keys,
passwords,
or configuration secrets.
Here is exactly what is required and how OAuth is handled across both environments.
The Underlying OAuth Flow for MCP
The Underlying OAuth Flow for MCP
Whether you are using Claude Code or Claude Chat,
the underlying architecture is identical.
The Claude application acts as the
OAuth Client
,
and the remote tool you are connecting to acts as the
Resource Server
.
To build or connect to an MCP server,
the OAuth integration must support this standard discovery and token exchange flow:
Initial Handshake (Discovery):
Claude attempts to connect to the MCP server without credentials.
The server must reject this with a
401 Unauthorized
HTTP status and include a
WWW-Authenticate
header pointing to a Protected Resource Metadata (PRM) document.
Metadata Fetch:
Claude reads the PRM document to locate your specific Authorization Server (e.
g.,
Okta,
Auth0,
Clerk,
or a custom auth server).
User Consent:
Claude prompts the user to authenticate.
The user is temporarily redirected to a browser window to log in and explicitly grant permission (e.
g.,
"Allow Claude to access your GitHub repositories").
Token Exchange:
Claude receives a temporary authorization code via a redirect,
which it silently exchanges for a long-lived access token.
Access Granted:
Claude now includes this token in the header of subsequent requests to invoke tools and read data on your behalf.
MCP OAuth in Claude Code
MCP OAuth in Claude Code
Claude Code is Anthropic's command-line interface (CLI) tool for software engineering.
When you connect Claude Code to a protected remote MCP server,
the OAuth flow is adapted for the terminal:
Browser Handoff:
When Claude Code hits an MCP server that requires OAuth,
it will generate a secure authorization link and automatically open your default desktop web browser (or output the URL to the terminal for headless environments).
Authentication:
Once you log in and approve access in the browser,
the authorization server redirects the payload back to a local loopback port managed by Claude Code,
handing off the authorization code.
Alternative (Static Headers):
If setting up a full OAuth 2.
1 server is too complex for a quick internal tool,
Claude Code also allows you to bypass the browser flow by injecting static bearer tokens directly via the CLI.
You can do this using the
--header
flag (e.
g.,
claude mcp add my-server --transport http --header "Authorization: Bearer your-token"
).
MCP OAuth in Claude Chat (Claude Desktop & Web)
MCP OAuth in Claude Chat (Claude Desktop & Web)
"Claude Chat" encompasses the visual interfaces—both the Claude Desktop application and the Claude.
Claude.ai (Web Custom Connectors):
When you add a remote MCP server via the "Custom Connectors" menu in your settings,
Claude.
ai handles the OAuth flow entirely in the browser.
You simply paste the MCP server URL.
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Product Strategy 2025+ Change emoji
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Ways of working - Product processes Change emoji
Ways of working - Product processes
Ways of working - Product processes
Core Product Performance Metrics 📈
Core Product Performance Metrics
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Core Product Performance Metrics
Product Documentation Change emoji
Product Documentation
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Product Briefs Change emoji
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Change emoji Activity Export
Activity Export
Change emoji Autologging activities
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Deal Insights - Multiple currencies
Deal Insights - Multiple currencies
Reinvent Themes & Topics (and ACS)
Reinvent Themes & Topics (and ACS)
Change emoji Billing Portal
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Change emoji Upload Video/Audio Recordings
Upload Video/Audio Recordings
White-Label Jiminny Instance
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Win/Loss Analysis for a Deal
Win/Loss Analysis for a Deal
Change emoji Hubspot app
Hubspot app
Change emoji Automatically hard delete data for churned customers
Automatically hard delete data for churned customers
Change emoji Ask Jiminny Anything on Call level
Ask Jiminny Anything on Call level
Change emoji Ask Jiminny for Open and Closed Deals
Ask Jiminny for Open and Closed Deals
Change emoji Ask Jiminny Anything on Deal level
Ask Jiminny Anything on Deal level
Change emoji Automatically record all calendar meetings
Automatically record all calendar meetings
Change emoji Product Tiering
Product Tiering
Change emoji Recording Consent
Recording Consent
Change emoji Automated CRM Filling
Automated CRM Filling
Change emoji Automated Exec Reports
Automated Exec Reports
Change emoji Auto-detect Activity Type
Auto-detect Activity Type
AI Signals & Alerts
AI Signals & Alerts
Change emoji AJA on Anything
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Change emoji AI Call Scoring
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Jiminny MCP Connector
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Jiminny MCP Connector
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
Read time 12 min
12 min
Views 14
14
Add a reaction
Add a reaction
Epic
Link to Epik in Jira
Document status
DRAFT
Epic
Document status
Link to Epik in Jira
DRAFT
Change emoji Objective
Objective
Change emoji Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a
data layer for AI-driven revenue workflows
, not just a standalone product.
👤 Target user
👤 Target user
👤 Target user
Revenue teams using AI tools (Sales,
CS
, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
🤕
🤕
Pain point or problem
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
💥
💥
Impact and benefits
💥 Impact and benefits
For customers
For customers
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
For Jiminny
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
💡
💡
Solution ideas
💡 Solution ideas
Build an
AI Knowledge Connector (
MCP
-based)
that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides
structured insights
(summaries, action items, scoring etc.)
Supports
deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Change emoji Success metrics
Success metrics
Change emoji Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
No sort applied to the column
Metric
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
Goal
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
Decrease the manual copying of transcripts
Metric
No sort applied to the column
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
decrease by X %
Change emoji User interaction and design
User interaction and design
Change emoji User interaction and design
Simple setup flow:
Generate API key / connect
MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus:
usable by non-technical users
Change emoji Detailed Requirements
Detailed Requirements
Change emoji Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
No sort applied to the column
Importance
No sort applied to the column
Notes
No sort applied to the column
Requirement
No sort applied to the column
Importance
No sort applied to the column
Notes
No sort applied to the column
Requirement
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
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You said what is the oauth required for mcp for clauide code and claude chat
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what is the oauth required for mcp for clauide code and claude chat
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Gemini said
Gemini said
To understand the OAuth requirements for the Model Context Protocol (MCP) in Claude Code and Claude Chat, it helps to look at how MCP handles secure connections.
MCP acts as a universal "USB port" that allows AI models to connect to external data sources and tools.
When connecting to remote, HTTP-based MCP servers, Anthropic relies on the
OAuth 2.1
authorization framework.
This ensures that Claude can access your protected tools securely using temporary tokens, rather than requiring you to copy and paste long-lived API keys, passwords, or configuration secrets.
View source details for citations from Stytch, Model Context Protocol, and Zuplo. Opens side panel.
+2
Here is exactly what is required and how OAuth is handled across both environments.
The Underlying OAuth Flow for MCP
The Underlying OAuth Flow for MCP
Whether you are using Claude Code or Claude Chat, the underlying architecture is identical.
The Claude application acts as the
OAuth Client
, and the remote tool you are connecting to acts as the
Resource Server
.
View source details for citation from Model Context Protocol. Opens side panel.
To build or connect to an MCP server, the OAuth integration must support this standard discovery and token exchange flow:
Initial Handshake (Discovery):
Claude attempts to connect to the MCP server without credentials. The server must reject this with a
401 Unauthorized
HTTP status and include a
WWW-Authenticate
header pointing to a Protected Resource Metadata (PRM) document.
Metadata Fetch:
Claude reads the PRM document to locate your specific Authorization Server (e.g., Okta, Auth0, Clerk, or a custom auth server).
View source details for citation from Model Context Protocol. Opens side panel.
User Consent:
Claude prompts the user to authenticate.
The user is temporarily redirected to a browser window to log in and explicitly grant permission (e.g., "Allow Claude to access your GitHub repositories").
View source details for citations from GitHub and Model Context Protocol. Opens side panel.
+1
Token Exchange:
Claude receives a temporary authorization code via a redirect, which it silently exchanges for a long-lived access token.
View source details for citation from Upstash. Opens side panel.
Access Granted:
Claude now includes this token in the header of subsequent requests to invoke tools and read data on your behalf.
View source details for citation from Model Context Protocol. Opens side panel.
MCP OAuth in Claude Code
MCP OAuth in Claude Code
Claude Code is Anthropic's command-line interface (CLI) tool for software engineering. When you connect Claude Code to a protected remote MCP server, the OAuth flow is adapted for the terminal:
Browser Handoff:
When Claude Code hits an MCP server that requires OAuth, it will generate a secure authorization link and automatically open your default desktop web browser (or output the URL to the terminal for headless environments).
Authentication:
Once you log in and approve access in the browser, the authorization server redirects the payload back to a local loopback port managed by Claude Code, handing off the authorization code.
View source details for citation from Model Context Protocol. Opens side panel.
Alternative (Static Headers):
If setting up a full OAuth 2.1 server is too complex for a quick internal tool, Claude Code also allows you to bypass the browser flow by injecting static bearer tokens directly via the CLI.
You can do this using the
--header
flag (e.g.,
claude mcp add my-server --transport http --header "Authorization: Bearer your-token"
).
View source details for citation from TrueFoundry. Opens side panel.
MCP OAuth in Claude Chat (Claude Desktop & Web)
MCP OAuth in Claude Chat (Claude Desktop & Web)
"Claude Chat" encompasses the visual interfaces—both the Claude Desktop application and the Claude.ai web platform.
Claude.ai (Web Custom Connectors):
When you add a remote MCP server via the "Custom Connectors" menu in your settings, Claude.ai handles the OAuth flow entirely in the browser. You simply paste the MCP server URL. Claude automatically parses the 401 discovery response and redirects you to the third-party service to grant permission, before seamlessly dropping you back into your chat session.
Claude Desktop:
Claude Desktop traditionally relies on a local configuration file (
claude_desktop_config.json
). While local stdio servers often require pasting API keys into this file, remote HTTP MCP servers utilizing OAuth eliminate this security risk.
When Claude Desktop encounters an OAuth-protected server, it pops open your system's default browser to capture your consent.
It then stores the resulting token securely in your operating system's native keychain or credential manager, keeping your
config.json
free of sensitive plaintext passwords.
View source details for citation from GitHub. Opens side panel.
The Bottom Line:
You do not need to build a bespoke authentication protocol for MCP.
If your external tool already utilizes standard OpenID Connect or OAuth 2.0/2.1, Claude Code and Claude Chat are heavily standardized to automatically discover those endpoints, route the user for consent, and manage the resulting tokens behind the scenes.
View source details for citation from GitHub. Opens side panel.
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Product Strategy 2025+ Change emoji
Product Strategy 2025+
Product Strategy 2025+
Competitive analysis Change emoji
Competitive analysis
Competitive analysis
Ways of working - Product processes Change emoji
Ways of working - Product processes
Ways of working - Product processes
Core Product Performance Metrics 📈
Core Product Performance Metrics
📈
Core Product Performance Metrics
Product Documentation Change emoji
Product Documentation
Product Documentation
Product Briefs Change emoji
Product Briefs
Product Briefs
Change emoji Activity Export
Activity Export
Change emoji Autologging activities
Autologging activities
Deal Insights - Multiple currencies
Deal Insights - Multiple currencies
Reinvent Themes & Topics (and ACS)
Reinvent Themes & Topics (and ACS)
Change emoji Billing Portal
Billing Portal
Change emoji Upload Video/Audio Recordings
Upload Video/Audio Recordings
White-Label Jiminny Instance
White-Label Jiminny Instance
Win/Loss Analysis for a Deal
Win/Loss Analysis for a Deal
Change emoji Hubspot app
Hubspot app
Change emoji Automatically hard delete data for churned customers
Automatically hard delete data for churned customers
Change emoji Ask Jiminny Anything on Call level
Ask Jiminny Anything on Call level
Change emoji Ask Jiminny for Open and Closed Deals
Ask Jiminny for Open and Closed Deals
Change emoji Ask Jiminny Anything on Deal level
Ask Jiminny Anything on Deal level
Change emoji Automatically record all calendar meetings
Automatically record all calendar meetings
Change emoji Product Tiering
Product Tiering
Change emoji Recording Consent
Recording Consent
Change emoji Automated CRM Filling
Automated CRM Filling
Change emoji Automated Exec Reports
Automated Exec Reports
Change emoji Auto-detect Activity Type
Auto-detect Activity Type
AI Signals & Alerts
AI Signals & Alerts
Change emoji AJA on Anything
AJA on Anything
Change emoji AI Call Scoring
AI Call Scoring
Jiminny MCP Connector
Jiminny MCP Connector
Desktop app to record a meetings without visible Notetaker
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Jiminny MCP Connector
Updated 10m ago
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
Read time 12 min
12 min
Views 14
14
Add a reaction
Add a reaction
Epic
Link to Epik in Jira
Document status
DRAFT
Epic
Document status
Link to Epik in Jira
DRAFT
Change emoji Objective
Objective
Change emoji Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a
data layer for AI-driven revenue workflows
, not just a standalone product.
👤 Target user
👤 Target user
👤 Target user
Revenue teams using AI tools (Sales,
CS
, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
🤕
🤕
Pain point or problem
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
💥
💥
Impact and benefits
💥 Impact and benefits
For customers
For customers
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
For Jiminny
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
💡
💡
Solution ideas
💡 Solution ideas
Build an
AI Knowledge Connector (
MCP
-based)
that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides
structured insights
(summaries, action items, scoring etc.)
Supports
deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Change emoji Success metrics
Success metrics
Change emoji Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
No sort applied to the column
Metric
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
Goal
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
Decrease the manual copying of transcripts
Metric
No sort applied to the column
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
decrease by X %
Change emoji User interaction and design
User interaction and design
Change emoji User interaction and design
Simple setup flow:
Generate API key / connect
MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus:
usable by non-technical users
Change emoji Detailed Requirements
Detailed Requirements
Change emoji Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
No sort applied to the column
Importance
No sort applied to the column
Notes
No sort applied to the column
Requirement
No sort applied to the column
Importance
No sort applied to the column
Notes
No sort applied to the column
Requirement
No sort applied to the column
Importance
No sort applied to the column
Notes
No sort applied to the column
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide
MCP
endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH...
|
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Prioritize them (e.g., High, Medium, Low) and add notes for clarification.","depth":16,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Requirement","depth":22,"bounds":{"left":0.68633646,"top":0.15203512,"width":0.03307846,"height":0.01556265},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"No sort applied to the column","depth":21,"bounds":{"left":0.81133646,"top":0.14604948,"width":0.00930851,"height":0.022346368},"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Importance","depth":22,"bounds":{"left":0.82613033,"top":0.15203512,"width":0.030751329,"height":0.01556265},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"No sort applied to the 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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
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You said what is the oauth required for mcp for clauide code and claude chat
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what is the oauth required for mcp for clauide code and claude chat
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To understand the OAuth requirements for the Model Context Protocol (MCP) in Claude Code and Claude Chat, it helps to look at how MCP handles secure connections.
MCP acts as a universal "USB port" that allows AI models to connect to external data sources and tools.
When connecting to remote, HTTP-based MCP servers, Anthropic relies on the
OAuth 2.1
authorization framework.
This ensures that Claude can access your protected tools securely using temporary tokens, rather than requiring you to copy and paste long-lived API keys, passwords, or configuration secrets.
View source details for citations from Stytch, Model Context Protocol, and Zuplo. Opens side panel.
+2
Here is exactly what is required and how OAuth is handled across both environments.
The Underlying OAuth Flow for MCP
The Underlying OAuth Flow for MCP
Whether you are using Claude Code or Claude Chat, the underlying architecture is identical.
The Claude application acts as the
OAuth Client
, and the remote tool you are connecting to acts as the
Resource Server
.
View source details for citation from Model Context Protocol. Opens side panel.
To build or connect to an MCP server, the OAuth integration must support this standard discovery and token exchange flow:
Initial Handshake (Discovery):
Claude attempts to connect to the MCP server without credentials. The server must reject this with a
401 Unauthorized
HTTP status and include a
WWW-Authenticate
header pointing to a Protected Resource Metadata (PRM) document.
Metadata Fetch:
Claude reads the PRM document to locate your specific Authorization Server (e.g., Okta, Auth0, Clerk, or a custom auth server).
View source details for citation from Model Context Protocol. Opens side panel.
User Consent:
Claude prompts the user to authenticate.
The user is temporarily redirected to a browser window to log in and explicitly grant permission (e.g., "Allow Claude to access your GitHub repositories").
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+1
Token Exchange:
Claude receives a temporary authorization code via a redirect, which it silently exchanges for a long-lived access token.
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Access Granted:
Claude now includes this token in the header of subsequent requests to invoke tools and read data on your behalf.
View source details for citation from Model Context Protocol. Opens side panel.
MCP OAuth in Claude Code
MCP OAuth in Claude Code
Claude Code is Anthropic's command-line interface (CLI) tool for software engineering. When you connect Claude Code to a protected remote MCP server, the OAuth flow is adapted for the terminal:
Browser Handoff:
When Claude Code hits an MCP server that requires OAuth, it will generate a secure authorization link and automatically open your default desktop web browser (or output the URL to the terminal for headless environments).
Authentication:
Once you log in and approve access in the browser, the authorization server redirects the payload back to a local loopback port managed by Claude Code, handing off the authorization code.
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Alternative (Static Headers):
If setting up a full OAuth 2.1 server is too complex for a quick internal tool, Claude Code also allows you to bypass the browser flow by injecting static bearer tokens directly via the CLI.
You can do this using the
--header
flag (e.g.,
claude mcp add my-server --transport http --header "Authorization: Bearer your-token"
).
View source details for citation from TrueFoundry. Opens side panel.
MCP OAuth in Claude Chat (Claude Desktop & Web)
MCP OAuth in Claude Chat (Claude Desktop & Web)
"Claude Chat" encompasses the visual interfaces—both the Claude Desktop application and the Claude.ai web platform.
Claude.ai (Web Custom Connectors):
When you add a remote MCP server via the "Custom Connectors" menu in your settings, Claude.ai handles the OAuth flow entirely in the browser. You simply paste the MCP server URL. Claude automatically parses the 401 discovery response and redirects you to the third-party service to grant permission, before seamlessly dropping you back into your chat session.
Claude Desktop:
Claude Desktop traditionally relies on a local configuration file (
claude_desktop_config.json
). While local stdio servers often require pasting API keys into this file, remote HTTP MCP servers utilizing OAuth eliminate this security risk.
When Claude Desktop encounters an OAuth-protected server, it pops open your system's default browser to capture your consent.
It then stores the resulting token securely in your operating system's native keychain or credential manager, keeping your
config.json
free of sensitive plaintext passwords.
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The Bottom Line:
You do not need to build a bespoke authentication protocol for MCP.
If your external tool already utilizes standard OpenID Connect or OAuth 2.0/2.1, Claude Code and Claude Chat are heavily standardized to automatically discover those endpoints, route the user for consent, and manage the resulting tokens behind the scenes.
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Product Strategy 2025+ Change emoji
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Competitive analysis
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Ways of working - Product processes Change emoji
Ways of working - Product processes
Ways of working - Product processes
Core Product Performance Metrics 📈
Core Product Performance Metrics
📈
Core Product Performance Metrics
Product Documentation Change emoji
Product Documentation
Product Documentation
Product Briefs Change emoji
Product Briefs
Product Briefs
Change emoji Activity Export
Activity Export
Change emoji Autologging activities
Autologging activities
Deal Insights - Multiple currencies
Deal Insights - Multiple currencies
Reinvent Themes & Topics (and ACS)
Reinvent Themes & Topics (and ACS)
Change emoji Billing Portal
Billing Portal
Change emoji Upload Video/Audio Recordings
Upload Video/Audio Recordings
White-Label Jiminny Instance
White-Label Jiminny Instance
Win/Loss Analysis for a Deal
Win/Loss Analysis for a Deal
Change emoji Hubspot app
Hubspot app
Change emoji Automatically hard delete data for churned customers
Automatically hard delete data for churned customers
Change emoji Ask Jiminny Anything on Call level
Ask Jiminny Anything on Call level
Change emoji Ask Jiminny for Open and Closed Deals
Ask Jiminny for Open and Closed Deals
Change emoji Ask Jiminny Anything on Deal level
Ask Jiminny Anything on Deal level
Change emoji Automatically record all calendar meetings
Automatically record all calendar meetings
Change emoji Product Tiering
Product Tiering
Change emoji Recording Consent
Recording Consent
Change emoji Automated CRM Filling
Automated CRM Filling
Change emoji Automated Exec Reports
Automated Exec Reports
Change emoji Auto-detect Activity Type
Auto-detect Activity Type
AI Signals & Alerts
AI Signals & Alerts
Change emoji AJA on Anything
AJA on Anything
Change emoji AI Call Scoring
AI Call Scoring
Jiminny MCP Connector
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Desktop app to record a meetings without visible Notetaker
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Jiminny MCP Connector
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
Read time 12 min
12 min
Views 14
14
Add a reaction
Add a reaction
Epic
Link to Epik in Jira
Document status
DRAFT
Epic
Document status
Link to Epik in Jira
DRAFT
Change emoji Objective
Objective
Change emoji Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a
data layer for AI-driven revenue workflows
, not just a standalone product.
👤 Target user
👤 Target user
👤 Target user
Revenue teams using AI tools (Sales,
CS
, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
🤕
🤕
Pain point or problem
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
💥
💥
Impact and benefits
💥 Impact and benefits
For customers
For customers
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
For Jiminny
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
💡
💡
Solution ideas
💡 Solution ideas
Build an
AI Knowledge Connector (
MCP
-based)
that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides
structured insights
(summaries, action items, scoring etc.)
Supports
deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Change emoji Success metrics
Success metrics
Change emoji Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
No sort applied to the column
Metric
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
Goal
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
Decrease the manual copying of transcripts
Metric
No sort applied to the column
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
decrease by X %
Change emoji User interaction and design
User interaction and design
Change emoji User interaction and design
Simple setup flow:
Generate API key / connect
MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus:
usable by non-technical users
Change emoji Detailed Requirements
Detailed Requirements
Change emoji Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
No sort applied to the column
Importance
No sort applied to the column
Notes
No sort applied to the column
Requirement
No sort applied to the column
Importance
No sort applied to the column
Notes
No sort applied to the column
Requirement
No sort applied to the column
Importance
No sort applied to the column
Notes
No sort applied to the column
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide
MCP
endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
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You said what is the oauth required for mcp for clauide code and claude chat
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what is the oauth required for mcp for clauide code and claude chat
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Gemini said
Gemini said
To understand the OAuth requirements for the Model Context Protocol (MCP) in Claude Code and Claude Chat, it helps to look at how MCP handles secure connections.
MCP acts as a universal "USB port" that allows AI models to connect to external data sources and tools.
When connecting to remote, HTTP-based MCP servers, Anthropic relies on the
OAuth 2.1
authorization framework.
This ensures that Claude can access your protected tools securely using temporary tokens, rather than requiring you to copy and paste long-lived API keys, passwords, or configuration secrets.
View source details for citations from Stytch, Model Context Protocol, and Zuplo. Opens side panel.
+2
Here is exactly what is required and how OAuth is handled across both environments.
The Underlying OAuth Flow for MCP
The Underlying OAuth Flow for MCP
Whether you are using Claude Code or Claude Chat, the underlying architecture is identical.
The Claude application acts as the
OAuth Client
, and the remote tool you are connecting to acts as the
Resource Server
.
View source details for citation from Model Context Protocol. Opens side panel.
To build or connect to an MCP server, the OAuth integration must support this standard discovery and token exchange flow:
Initial Handshake (Discovery):
Claude attempts to connect to the MCP server without credentials. The server must reject this with a
401 Unauthorized
HTTP status and include a
WWW-Authenticate
header pointing to a Protected Resource Metadata (PRM) document.
Metadata Fetch:
Claude reads the PRM document to locate your specific Authorization Server (e.g., Okta, Auth0, Clerk, or a custom auth server).
View source details for citation from Model Context Protocol. Opens side panel.
User Consent:
Claude prompts the user to authenticate.
The user is temporarily redirected to a browser window to log in and explicitly grant permission (e.g., "Allow Claude to access your GitHub repositories").
View source details for citations from GitHub and Model Context Protocol. Opens side panel.
+1
Token Exchange:
Claude receives a temporary authorization code via a redirect, which it silently exchanges for a long-lived access token.
View source details for citation from Upstash. Opens side panel.
Access Granted:
Claude now includes this token in the header of subsequent requests to invoke tools and read data on your behalf.
View source details for citation from Model Context Protocol. Opens side panel.
MCP OAuth in Claude Code
MCP OAuth in Claude Code
Claude Code is Anthropic's command-line interface (CLI) tool for software engineering. When you connect Claude Code to a protected remote MCP server, the OAuth flow is adapted for the terminal:
Browser Handoff:
When Claude Code hits an MCP server that requires OAuth, it will generate a secure authorization link and automatically open your default desktop web browser (or output the URL to the terminal for headless environments).
Authentication:
Once you log in and approve access in the browser, the authorization server redirects the payload back to a local loopback port managed by Claude Code, handing off the authorization code.
View source details for citation from Model Context Protocol. Opens side panel.
Alternative (Static Headers):
If setting up a full OAuth 2.1 server is too complex for a quick internal tool, Claude Code also allows you to bypass the browser flow by injecting static bearer tokens directly via the CLI.
You can do this using the
--header
flag (e.g.,
claude mcp add my-server --transport http --header "Authorization: Bearer your-token"
).
View source details for citation from TrueFoundry. Opens side panel.
MCP OAuth in Claude Chat (Claude Desktop & Web)
MCP OAuth in Claude Chat (Claude Desktop & Web)
"Claude Chat" encompasses the visual interfaces—both the Claude Desktop application and the Claude.ai web platform.
Claude.ai (Web Custom Connectors):
When you add a remote MCP server via the "Custom Connectors" menu in your settings, Claude.ai handles the OAuth flow entirely in the browser. You simply paste the MCP server URL. Claude automatically parses the 401 discovery response and redirects you to the third-party service to grant permission, before seamlessly dropping you back into your chat session.
Claude Desktop:
Claude Desktop traditionally relies on a local configuration file (
claude_desktop_config.json
). While local stdio servers often require pasting API keys into this file, remote HTTP MCP servers utilizing OAuth eliminate this security risk.
When Claude Desktop encounters an OAuth-protected server, it pops open your system's default browser to capture your consent.
It then stores the resulting token securely in your operating system's native keychain or credential manager, keeping your
config.json
free of sensitive plaintext passwords.
View source details for citation from GitHub. Opens side panel.
The Bottom Line:
You do not need to build a bespoke authentication protocol for MCP.
If your external tool already utilizes standard OpenID Connect or OAuth 2.0/2.1, Claude Code and Claude Chat are heavily standardized to automatically discover those endpoints, route the user for consent, and manage the resulting tokens behind the scenes.
View source details for citation from GitHub. Opens side panel.
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Product Strategy 2025+ Change emoji
Product Strategy 2025+
Product Strategy 2025+
Competitive analysis Change emoji
Competitive analysis
Competitive analysis
Ways of working - Product processes Change emoji
Ways of working - Product processes
Ways of working - Product processes
Core Product Performance Metrics 📈
Core Product Performance Metrics
📈
Core Product Performance Metrics
Product Documentation Change emoji
Product Documentation
Product Documentation
Product Briefs Change emoji
Product Briefs
Product Briefs
Change emoji Activity Export
Activity Export
Change emoji Autologging activities
Autologging activities
Deal Insights - Multiple currencies
Deal Insights - Multiple currencies
Reinvent Themes & Topics (and ACS)
Reinvent Themes & Topics (and ACS)
Change emoji Billing Portal
Billing Portal
Change emoji Upload Video/Audio Recordings
Upload Video/Audio Recordings
White-Label Jiminny Instance
White-Label Jiminny Instance
Win/Loss Analysis for a Deal
Win/Loss Analysis for a Deal
Change emoji Hubspot app
Hubspot app
Change emoji Automatically hard delete data for churned customers
Automatically hard delete data for churned customers
Change emoji Ask Jiminny Anything on Call level
Ask Jiminny Anything on Call level
Change emoji Ask Jiminny for Open and Closed Deals
Ask Jiminny for Open and Closed Deals
Change emoji Ask Jiminny Anything on Deal level
Ask Jiminny Anything on Deal level
Change emoji Automatically record all calendar meetings
Automatically record all calendar meetings
Change emoji Product Tiering
Product Tiering
Change emoji Recording Consent
Recording Consent
Change emoji Automated CRM Filling
Automated CRM Filling
Change emoji Automated Exec Reports
Automated Exec Reports
Change emoji Auto-detect Activity Type
Auto-detect Activity Type
AI Signals & Alerts
AI Signals & Alerts
Change emoji AJA on Anything
AJA on Anything
Change emoji AI Call Scoring
AI Call Scoring
Jiminny MCP Connector
Jiminny MCP Connector
Desktop app to record a meetings without visible Notetaker
Desktop app to record a meetings without visible Notetaker
Feedback Change emoji
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Jiminny MCP Connector
Updated 10m ago
Updated 10m ago
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
Read time 12 min
12 min
Views 14
14
Add a reaction
Add a reaction
Epic
Link to Epik in Jira
Document status
DRAFT
Epic
Document status
Link to Epik in Jira
DRAFT
Change emoji Objective
Objective
Change emoji Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a
data layer for AI-driven revenue workflows
, not just a standalone product.
👤 Target user
👤 Target user
👤 Target user
Revenue teams using AI tools (Sales,
CS
, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
🤕
🤕
Pain point or problem
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
💥
💥
Impact and benefits
💥 Impact and benefits
For customers
For customers
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
For Jiminny
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
💡
💡
Solution ideas
💡 Solution ideas
Build an
AI Knowledge Connector (
MCP
-based)
that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides
structured insights
(summaries, action items, scoring etc.)
Supports
deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Change emoji Success metrics
Success metrics
Change emoji Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
No sort applied to the column
Metric
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
Goal
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
Decrease the manual copying of transcripts
Metric
No sort applied to the column
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
decrease by X %
Change emoji User interaction and design
User interaction and design
Change emoji User interaction and design
Simple setup flow:
Generate API key / connect
MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus:
usable by non-technical users
Change emoji Detailed Requirements
Detailed Requirements
Change emoji Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification....
|
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an","depth":16,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"AI Knowledge Connector (","depth":17,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"MCP","depth":18,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"-based)","depth":17,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"that:","depth":16,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Exposes Jiminny data (calls, transcripts, metadata) to AI tools","depth":18,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provides","depth":18,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"structured 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metrics","depth":16,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).","depth":16,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Goal","depth":21,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"No sort applied to the column","depth":20,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Metric","depth":21,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"No sort applied to the column","depth":20,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"% of customers connecting external AI tools","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Number of","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"MCP","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"/API calls per customer - 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and which ones are being used the most","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"decrease by X %","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"Change emoji User interaction and design","depth":15,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"User interaction and design","depth":16,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Change emoji User interaction and design","depth":16,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Simple setup flow:","depth":18,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Generate API key / connect","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"MCP","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Clear instructions for Claude / Zapier setup","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide:","depth":18,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Example prompts for Claude","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Pre-built workflow templates for Zapier","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Clear documentation - with use cases","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"👉 Focus:","depth":16,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"usable by non-technical users","depth":17,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"Change emoji Detailed Requirements","depth":15,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Detailed Requirements","depth":16,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Change emoji Detailed Requirements","depth":16,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.","depth":16,"help_text":"","role_description":"text","subrole":"AXUnknown"}]...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
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You said what is the oauth required for mcp for clauide code and claude chat
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what is the oauth required for mcp for clauide code and claude chat
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Gemini said
To understand the OAuth requirements for the Model Context Protocol (MCP) in Claude Code and Claude Chat, it helps to look at how MCP handles secure connections.
MCP acts as a universal "USB port" that allows AI models to connect to external data sources and tools.
When connecting to remote, HTTP-based MCP servers, Anthropic relies on the
OAuth 2.1
authorization framework.
This ensures that Claude can access your protected tools securely using temporary tokens, rather than requiring you to copy and paste long-lived API keys, passwords, or configuration secrets.
View source details for citations from Stytch, Model Context Protocol, and Zuplo. Opens side panel.
+2
Here is exactly what is required and how OAuth is handled across both environments.
The Underlying OAuth Flow for MCP
The Underlying OAuth Flow for MCP
Whether you are using Claude Code or Claude Chat, the underlying architecture is identical.
The Claude application acts as the
OAuth Client
, and the remote tool you are connecting to acts as the
Resource Server
.
View source details for citation from Model Context Protocol. Opens side panel.
To build or connect to an MCP server, the OAuth integration must support this standard discovery and token exchange flow:
Initial Handshake (Discovery):
Claude attempts to connect to the MCP server without credentials. The server must reject this with a
401 Unauthorized
HTTP status and include a
WWW-Authenticate
header pointing to a Protected Resource Metadata (PRM) document.
Metadata Fetch:
Claude reads the PRM document to locate your specific Authorization Server (e.g., Okta, Auth0, Clerk, or a custom auth server).
View source details for citation from Model Context Protocol. Opens side panel.
User Consent:
Claude prompts the user to authenticate.
The user is temporarily redirected to a browser window to log in and explicitly grant permission (e.g., "Allow Claude to access your GitHub repositories").
View source details for citations from GitHub and Model Context Protocol. Opens side panel.
+1
Token Exchange:
Claude receives a temporary authorization code via a redirect, which it silently exchanges for a long-lived access token.
View source details for citation from Upstash. Opens side panel.
Access Granted:
Claude now includes this token in the header of subsequent requests to invoke tools and read data on your behalf.
View source details for citation from Model Context Protocol. Opens side panel.
MCP OAuth in Claude Code
MCP OAuth in Claude Code
Claude Code is Anthropic's command-line interface (CLI) tool for software engineering. When you connect Claude Code to a protected remote MCP server, the OAuth flow is adapted for the terminal:
Browser Handoff:
When Claude Code hits an MCP server that requires OAuth, it will generate a secure authorization link and automatically open your default desktop web browser (or output the URL to the terminal for headless environments).
Authentication:
Once you log in and approve access in the browser, the authorization server redirects the payload back to a local loopback port managed by Claude Code, handing off the authorization code.
View source details for citation from Model Context Protocol. Opens side panel.
Alternative (Static Headers):
If setting up a full OAuth 2.1 server is too complex for a quick internal tool, Claude Code also allows you to bypass the browser flow by injecting static bearer tokens directly via the CLI.
You can do this using the
--header
flag (e.g.,
claude mcp add my-server --transport http --header "Authorization: Bearer your-token"
).
View source details for citation from TrueFoundry. Opens side panel.
MCP OAuth in Claude Chat (Claude Desktop & Web)
MCP OAuth in Claude Chat (Claude Desktop & Web)
"Claude Chat" encompasses the visual interfaces—both the Claude Desktop application and the Claude.ai web platform.
Claude.ai (Web Custom Connectors):
When you add a remote MCP server via the "Custom Connectors" menu in your settings, Claude.ai handles the OAuth flow entirely in the browser. You simply paste the MCP server URL. Claude automatically parses the 401 discovery response and redirects you to the third-party service to grant permission, before seamlessly dropping you back into your chat session.
Claude Desktop:
Claude Desktop traditionally relies on a local configuration file (
claude_desktop_config.json
). While local stdio servers often require pasting API keys into this file, remote HTTP MCP servers utilizing OAuth eliminate this security risk.
When Claude Desktop encounters an OAuth-protected server, it pops open your system's default browser to capture your consent.
It then stores the resulting token securely in your operating system's native keychain or credential manager, keeping your
config.json
free of sensitive plaintext passwords.
View source details for citation from GitHub. Opens side panel.
The Bottom Line:
You do not need to build a bespoke authentication protocol for MCP.
If your external tool already utilizes standard OpenID Connect or OAuth 2.0/2.1, Claude Code and Claude Chat are heavily standardized to automatically discover those endpoints, route the user for consent, and manage the resulting tokens behind the scenes.
View source details for citation from GitHub. Opens side panel.
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Upload Video/Audio Recordings
White-Label Jiminny Instance
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Win/Loss Analysis for a Deal
Win/Loss Analysis for a Deal
Change emoji Hubspot app
Hubspot app
Change emoji Automatically hard delete data for churned customers
Automatically hard delete data for churned customers
Change emoji Ask Jiminny Anything on Call level
Ask Jiminny Anything on Call level
Change emoji Ask Jiminny for Open and Closed Deals
Ask Jiminny for Open and Closed Deals
Change emoji Ask Jiminny Anything on Deal level
Ask Jiminny Anything on Deal level
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Automatically record all calendar meetings
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Change emoji Recording Consent
Recording Consent
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Automated CRM Filling
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Jiminny MCP Connector
Updated 10m ago
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
Read time 12 min
12 min
Views 14
14
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Epic
Link to Epik in Jira
Document status
DRAFT
Epic
Document status
Link to Epik in Jira
DRAFT
Change emoji Objective
Objective
Change emoji Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a
data layer for AI-driven revenue workflows
, not just a standalone product.
👤 Target user
👤 Target user
👤 Target user
Revenue teams using AI tools (Sales,
CS
, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
🤕
🤕
Pain point or problem
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
💥
💥
Impact and benefits
💥 Impact and benefits
For customers
For customers
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
For Jiminny
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
💡
💡
Solution ideas
💡 Solution ideas
Build an
AI Knowledge Connector (
MCP
-based)
that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides
structured insights
(summaries, action items, scoring etc.)
Supports
deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Change emoji Success metrics
Success metrics
Change emoji Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
No sort applied to the column
Metric
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
Goal
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
Decrease the manual copying of transcripts
Metric
No sort applied to the column
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
decrease by X %
Change emoji User interaction and design
User interaction and design
Change emoji User interaction and design
Simple setup flow:
Generate API key / connect
MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus:
usable by non-technical users
Change emoji Detailed Requirements
Detailed Requirements
Change emoji Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification....
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
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You said what is the oauth required for mcp for clauide code and claude chat
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what is the oauth required for mcp for clauide code and claude chat
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To understand the OAuth requirements for the Model Context Protocol (MCP) in Claude Code and Claude Chat, it helps to look at how MCP handles secure connections.
MCP acts as a universal "USB port" that allows AI models to connect to external data sources and tools.
When connecting to remote, HTTP-based MCP servers, Anthropic relies on the
OAuth 2.1
authorization framework.
This ensures that Claude can access your protected tools securely using temporary tokens, rather than requiring you to copy and paste long-lived API keys, passwords, or configuration secrets.
View source details for citations from Stytch, Model Context Protocol, and Zuplo. Opens side panel.
+2
Here is exactly what is required and how OAuth is handled across both environments.
The Underlying OAuth Flow for MCP
The Underlying OAuth Flow for MCP
Whether you are using Claude Code or Claude Chat, the underlying architecture is identical.
The Claude application acts as the
OAuth Client
, and the remote tool you are connecting to acts as the
Resource Server
.
View source details for citation from Model Context Protocol. Opens side panel.
To build or connect to an MCP server, the OAuth integration must support this standard discovery and token exchange flow:
Initial Handshake (Discovery):
Claude attempts to connect to the MCP server without credentials. The server must reject this with a
401 Unauthorized
HTTP status and include a
WWW-Authenticate
header pointing to a Protected Resource Metadata (PRM) document.
Metadata Fetch:
Claude reads the PRM document to locate your specific Authorization Server (e.g., Okta, Auth0, Clerk, or a custom auth server).
View source details for citation from Model Context Protocol. Opens side panel.
User Consent:
Claude prompts the user to authenticate.
The user is temporarily redirected to a browser window to log in and explicitly grant permission (e.g., "Allow Claude to access your GitHub repositories").
View source details for citations from GitHub and Model Context Protocol. Opens side panel.
+1
Token Exchange:
Claude receives a temporary authorization code via a redirect, which it silently exchanges for a long-lived access token.
View source details for citation from Upstash. Opens side panel.
Access Granted:
Claude now includes this token in the header of subsequent requests to invoke tools and read data on your behalf.
View source details for citation from Model Context Protocol. Opens side panel.
MCP OAuth in Claude Code
MCP OAuth in Claude Code
Claude Code is Anthropic's command-line interface (CLI) tool for software engineering. When you connect Claude Code to a protected remote MCP server, the OAuth flow is adapted for the terminal:
Browser Handoff:
When Claude Code hits an MCP server that requires OAuth, it will generate a secure authorization link and automatically open your default desktop web browser (or output the URL to the terminal for headless environments).
Authentication:
Once you log in and approve access in the browser, the authorization server redirects the payload back to a local loopback port managed by Claude Code, handing off the authorization code.
View source details for citation from Model Context Protocol. Opens side panel.
Alternative (Static Headers):
If setting up a full OAuth 2.1 server is too complex for a quick internal tool, Claude Code also allows you to bypass the browser flow by injecting static bearer tokens directly via the CLI.
You can do this using the
--header
flag (e.g.,
claude mcp add my-server --transport http --header "Authorization: Bearer your-token"
).
View source details for citation from TrueFoundry. Opens side panel.
MCP OAuth in Claude Chat (Claude Desktop & Web)
MCP OAuth in Claude Chat (Claude Desktop & Web)
"Claude Chat" encompasses the visual interfaces—both the Claude Desktop application and the Claude.ai web platform.
Claude.ai (Web Custom Connectors):
When you add a remote MCP server via the "Custom Connectors" menu in your settings, Claude.ai handles the OAuth flow entirely in the browser. You simply paste the MCP server URL. Claude automatically parses the 401 discovery response and redirects you to the third-party service to grant permission, before seamlessly dropping you back into your chat session.
Claude Desktop:
Claude Desktop traditionally relies on a local configuration file (
claude_desktop_config.json
). While local stdio servers often require pasting API keys into this file, remote HTTP MCP servers utilizing OAuth eliminate this security risk.
When Claude Desktop encounters an OAuth-protected server, it pops open your system's default browser to capture your consent.
It then stores the resulting token securely in your operating system's native keychain or credential manager, keeping your
config.json
free of sensitive plaintext passwords.
View source details for citation from GitHub. Opens side panel.
The Bottom Line:
You do not need to build a bespoke authentication protocol for MCP.
If your external tool already utilizes standard OpenID Connect or OAuth 2.0/2.1, Claude Code and Claude Chat are heavily standardized to automatically discover those endpoints, route the user for consent, and manage the resulting tokens behind the scenes.
View source details for citation from GitHub. Opens side panel.
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OAuth requirements for the Model Context Protocol (MCP) in Claude Code and Claude Chat, it helps to look at how MCP handles secure connections.","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"MCP acts as a universal \"USB port\" that allows AI models to connect to external data sources and tools.","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"When connecting to remote, HTTP-based MCP servers, Anthropic relies on the","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"OAuth 2.1","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"authorization framework.","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"This ensures that Claude can access your protected tools securely using temporary tokens, rather than requiring you to copy and paste long-lived API keys, passwords, or configuration secrets.","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"View source details for citations from Stytch, Model Context Protocol, and Zuplo. Opens side panel.","depth":24,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"+2","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Here is exactly what is required and how OAuth is handled across both environments.","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"The Underlying OAuth Flow for MCP","depth":23,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The Underlying OAuth Flow for MCP","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Whether you are using Claude Code or Claude Chat, the underlying architecture is identical.","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The Claude application acts as the","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"OAuth Client","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":", and the remote tool you are connecting to acts as the","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Resource Server","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":".","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"View source details for citation from Model Context Protocol. Opens side panel.","depth":24,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"To build or connect to an MCP server, the OAuth integration must support this standard discovery and token exchange flow:","depth":24,"bounds":{"left":0.3695146,"top":0.0,"width":0.11087101,"height":0.057861134},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Initial Handshake (Discovery):","depth":26,"bounds":{"left":0.38148272,"top":0.0,"width":0.07829122,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude attempts to connect to the MCP server without credentials. 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Export","depth":18,"bounds":{"left":0.5099734,"top":0.18914606,"width":0.14461437,"height":0.025538707},"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Activity Export","depth":21,"bounds":{"left":0.52925533,"top":0.19513169,"width":0.03274601,"height":0.01396648},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Change emoji Autologging activities","depth":18,"bounds":{"left":0.5099734,"top":0.21468475,"width":0.14461437,"height":0.025538707},"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Autologging activities","depth":21,"bounds":{"left":0.52925533,"top":0.22067039,"width":0.048204787,"height":0.01396648},"help_text":"","role_description":"text","subrole":"AXUnknown"}]...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
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Workers | Datadog
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Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
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Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
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You said what is the oauth required for mcp for clauide code and claude chat
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what is the oauth required for mcp for clauide code and claude chat
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Gemini said
Gemini said
To understand the OAuth requirements for the Model Context Protocol (MCP) in Claude Code and Claude Chat, it helps to look at how MCP handles secure connections.
MCP acts as a universal "USB port" that allows AI models to connect to external data sources and tools.
When connecting to remote, HTTP-based MCP servers, Anthropic relies on the
OAuth 2.1
authorization framework.
This ensures that Claude can access your protected tools securely using temporary tokens, rather than requiring you to copy and paste long-lived API keys, passwords, or configuration secrets.
View source details for citations from Stytch, Model Context Protocol, and Zuplo. Opens side panel.
+2
Here is exactly what is required and how OAuth is handled across both environments.
The Underlying OAuth Flow for MCP
The Underlying OAuth Flow for MCP
Whether you are using Claude Code or Claude Chat, the underlying architecture is identical.
The Claude application acts as the
OAuth Client
, and the remote tool you are connecting to acts as the
Resource Server
.
View source details for citation from Model Context Protocol. Opens side panel.
To build or connect to an MCP server, the OAuth integration must support this standard discovery and token exchange flow:
Initial Handshake (Discovery):
Claude attempts to connect to the MCP server without credentials. The server must reject this with a
401 Unauthorized
HTTP status and include a
WWW-Authenticate
header pointing to a Protected Resource Metadata (PRM) document.
Metadata Fetch:
Claude reads the PRM document to locate your specific Authorization Server (e.g., Okta, Auth0, Clerk, or a custom auth server).
View source details for citation from Model Context Protocol. Opens side panel.
User Consent:
Claude prompts the user to authenticate.
The user is temporarily redirected to a browser window to log in and explicitly grant permission (e.g., "Allow Claude to access your GitHub repositories").
View source details for citations from GitHub and Model Context Protocol. Opens side panel.
+1
Token Exchange:
Claude receives a temporary authorization code via a redirect, which it silently exchanges for a long-lived access token.
View source details for citation from Upstash. Opens side panel.
Access Granted:
Claude now includes this token in the header of subsequent requests to invoke tools and read data on your behalf.
View source details for citation from Model Context Protocol. Opens side panel.
MCP OAuth in Claude Code
MCP OAuth in Claude Code
Claude Code is Anthropic's command-line interface (CLI) tool for software engineering. When you connect Claude Code to a protected remote MCP server, the OAuth flow is adapted for the terminal:
Browser Handoff:
When Claude Code hits an MCP server that requires OAuth, it will generate a secure authorization link and automatically open your default desktop web browser (or output the URL to the terminal for headless environments).
Authentication:
Once you log in and approve access in the browser, the authorization server redirects the payload back to a local loopback port managed by Claude Code, handing off the authorization code.
View source details for citation from Model Context Protocol. Opens side panel.
Alternative (Static Headers):
If setting up a full OAuth 2.1 server is too complex for a quick internal tool, Claude Code also allows you to bypass the browser flow by injecting static bearer tokens directly via the CLI.
You can do this using the
--header
flag (e.g.,
claude mcp add my-server --transport http --header "Authorization: Bearer your-token"
).
View source details for citation from TrueFoundry. Opens side panel.
MCP OAuth in Claude Chat (Claude Desktop & Web)
MCP OAuth in Claude Chat (Claude Desktop & Web)
"Claude Chat" encompasses the visual interfaces—both the Claude Desktop application and the Claude.ai web platform.
Claude.ai (Web Custom Connectors):
When you add a remote MCP server via the "Custom Connectors" menu in your settings, Claude.ai handles the OAuth flow entirely in the browser. You simply paste the MCP server URL. Claude automatically parses the 401 discovery response and redirects you to the third-party service to grant permission, before seamlessly dropping you back into your chat session.
Claude Desktop:
Claude Desktop traditionally relies on a local configuration file (
claude_desktop_config.json
). While local stdio servers often require pasting API keys into this file, remote HTTP MCP servers utilizing OAuth eliminate this security risk.
When Claude Desktop encounters an OAuth-protected server, it pops open your system's default browser to capture your consent.
It then stores the resulting token securely in your operating system's native keychain or credential manager, keeping your
config.json
free of sensitive plaintext passwords.
View source details for citation from GitHub. Opens side panel.
The Bottom Line:
You do not need to build a bespoke authentication protocol for MCP.
If your external tool already utilizes standard OpenID Connect or OAuth 2.0/2.1, Claude Code and Claude Chat are heavily standardized to automatically discover those endpoints, route the user for consent, and manage the resulting tokens behind the scenes.
View source details for citation from GitHub. Opens side panel.
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71960
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1738
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5
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2026-04-22T14:34:25.204969+00:00
|
/Users/lukas/.screenpipe/data/data/2026-04-22/1776 /Users/lukas/.screenpipe/data/data/2026-04-22/1776868465204_m1.jpg...
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Jiminny MCP Connector - Product - Confluence — Wor Jiminny MCP Connector - Product - Confluence — Work...
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jiminny.atlassian.net/wiki/spaces/PROD/pages/37282 jiminny.atlassian.net/wiki/spaces/PROD/pages/3728244737/Jiminny+MCP+Connector...
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monitor_1
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NULL
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NULL
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NULL
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NULL
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Jiminny MCP Connector - Product - Confluence
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You said what is the oauth required for mcp for clauide code and claude chat
You said
what is the oauth required for mcp for clauide code and claude chat
Listen
Show more options
Show thinking
Gemini said
Gemini said
To understand the OAuth requirements for the Model Context Protocol (MCP) in Claude Code and Claude Chat, it helps to look at how MCP handles secure connections.
MCP acts as a universal "USB port" that allows AI models to connect to external data sources and tools.
When connecting to remote, HTTP-based MCP servers, Anthropic relies on the
OAuth 2.1
authorization framework.
This ensures that Claude can access your protected tools securely using temporary tokens, rather than requiring you to copy and paste long-lived API keys, passwords, or configuration secrets.
View source details for citations from Stytch, Model Context Protocol, and Zuplo. Opens side panel.
+2
Here is exactly what is required and how OAuth is handled across both environments.
The Underlying OAuth Flow for MCP
The Underlying OAuth Flow for MCP
Whether you are using Claude Code or Claude Chat, the underlying architecture is identical.
The Claude application acts as the
OAuth Client
, and the remote tool you are connecting to acts as the
Resource Server
.
View source details for citation from Model Context Protocol. Opens side panel.
To build or connect to an MCP server, the OAuth integration must support this standard discovery and token exchange flow:
Initial Handshake (Discovery):
Claude attempts to connect to the MCP server without credentials. The server must reject this with a
401 Unauthorized
HTTP status and include a
WWW-Authenticate
header pointing to a Protected Resource Metadata (PRM) document.
Metadata Fetch:
Claude reads the PRM document to locate your specific Authorization Server (e.g., Okta, Auth0, Clerk, or a custom auth server).
View source details for citation from Model Context Protocol. Opens side panel.
User Consent:
Claude prompts the user to authenticate.
The user is temporarily redirected to a browser window to log in and explicitly grant permission (e.g., "Allow Claude to access your GitHub repositories").
View source details for citations from GitHub and Model Context Protocol. Opens side panel.
+1
Token Exchange:
Claude receives a temporary authorization code via a redirect, which it silently exchanges for a long-lived access token.
View source details for citation from Upstash. Opens side panel.
Access Granted:
Claude now includes this token in the header of subsequent requests to invoke tools and read data on your behalf.
View source details for citation from Model Context Protocol. Opens side panel.
MCP OAuth in Claude Code
MCP OAuth in Claude Code
Claude Code is Anthropic's command-line interface (CLI) tool for software engineering. When you connect Claude Code to a protected remote MCP server, the OAuth flow is adapted for the terminal:
Browser Handoff:
When Claude Code hits an MCP server that requires OAuth, it will generate a secure authorization link and automatically open your default desktop web browser (or output the URL to the terminal for headless environments).
Authentication:
Once you log in and approve access in the browser, the authorization server redirects the payload back to a local loopback port managed by Claude Code, handing off the authorization code.
View source details for citation from Model Context Protocol. Opens side panel.
Alternative (Static Headers):
If setting up a full OAuth 2.1 server is too complex for a quick internal tool, Claude Code also allows you to bypass the browser flow by injecting static bearer tokens directly via the CLI.
You can do this using the
--header
flag (e.g.,
claude mcp add my-server --transport http --header "Authorization: Bearer your-token"
).
View source details for citation from TrueFoundry. Opens side panel.
MCP OAuth in Claude Chat (Claude Desktop & Web)
MCP OAuth in Claude Chat (Claude Desktop & Web)
"Claude Chat" encompasses the visual interfaces—both the Claude Desktop application and the Claude.ai web platform.
Claude.ai (Web Custom Connectors):
When you add a remote MCP server via the "Custom Connectors" menu in your settings, Claude.ai handles the OAuth flow entirely in the browser. You simply paste the MCP server URL. Claude automatically parses the 401 discovery response and redirects you to the third-party service to grant permission, before seamlessly dropping you back into your chat session.
Claude Desktop:
Claude Desktop traditionally relies on a local configuration file (
claude_desktop_config.json
). While local stdio servers often require pasting API keys into this file, remote HTTP MCP servers utilizing OAuth eliminate this security risk.
When Claude Desktop encounters an OAuth-protected server, it pops open your system's default browser to capture your consent.
It then stores the resulting token securely in your operating system's native keychain or credential manager, keeping your
config.json
free of sensitive plaintext passwords.
View source details for citation from GitHub. Opens side panel.
The Bottom Line:
You do not need to build a bespoke authentication protocol for MCP.
If your external tool already utilizes standard OpenID Connect or OAuth 2.0/2.1, Claude Code and Claude Chat are heavily standardized to automatically discover those endpoints, route the user for consent, and manage the resulting tokens behind the scenes.
View source details for citation from GitHub. Opens side panel.
Sources
Sources
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Jiminny MCP Connector
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
Read time 12 min
12 min
Views 14
14
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Document status
DRAFT
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Change emoji Objective
Objective
Change emoji Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a
data layer for AI-driven revenue workflows
, not just a standalone product.
👤 Target user...
|
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Opens side panel.","depth":26,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"User Consent:","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude prompts the user to authenticate.","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The user is temporarily redirected to a browser window to log in and explicitly grant permission (e.g., \"Allow Claude to access your GitHub repositories\").","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"View source details for citations from GitHub and Model Context Protocol. Opens side panel.","depth":26,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"+1","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Token Exchange:","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude receives a temporary authorization code via a redirect, which it silently exchanges for a long-lived access token.","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"View source details for citation from Upstash. Opens side panel.","depth":26,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Access Granted:","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude now includes this token in the header of subsequent requests to invoke tools and read data on your behalf.","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"View source details for citation from Model Context Protocol. Opens side panel.","depth":26,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXHeading","text":"MCP OAuth in Claude Code","depth":23,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"MCP OAuth in Claude Code","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude Code is Anthropic's command-line interface (CLI) tool for software engineering. When you connect Claude Code to a protected remote MCP server, the OAuth flow is adapted for the terminal:","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Browser Handoff:","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"When Claude Code hits an MCP server that requires OAuth, it will generate a secure authorization link and automatically open your default desktop web browser (or output the URL to the terminal for headless environments).","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Authentication:","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Once you log in and approve access in the browser, the authorization server redirects the payload back to a local loopback port managed by Claude Code, handing off the authorization code.","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"View source details for citation from Model Context Protocol. Opens side panel.","depth":26,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Alternative (Static Headers):","depth":26,"bounds":{"left":0.23159721,"top":0.0,"width":0.15520833,"height":0.022777777},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"If setting up a full OAuth 2.1 server is too complex for a quick internal tool, Claude Code also allows you to bypass the browser flow by injecting static bearer tokens directly via the CLI.","depth":26,"bounds":{"left":0.23159721,"top":0.0,"width":0.21875,"height":0.13833334},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"You can do this using the","depth":26,"bounds":{"left":0.23159721,"top":0.045,"width":0.20243056,"height":0.051666666},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"--header","depth":27,"bounds":{"left":0.36458334,"top":0.075555556,"width":0.046527777,"height":0.020555556},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"flag (e.g.,","depth":26,"bounds":{"left":0.23159721,"top":0.07388889,"width":0.20868056,"height":0.051666666},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"claude mcp add my-server --transport http --header \"Authorization: Bearer your-token\"","depth":27,"bounds":{"left":0.23159721,"top":0.104444444,"width":0.19826388,"height":0.07833333},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":").","depth":26,"bounds":{"left":0.4340278,"top":0.16055556,"width":0.0069444445,"height":0.022777777},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"View source details for citation from TrueFoundry. Opens side panel.","depth":26,"bounds":{"left":0.23576389,"top":0.19,"width":0.018055556,"height":0.022222223},"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXHeading","text":"MCP OAuth in Claude Chat (Claude Desktop & Web)","depth":23,"bounds":{"left":0.20729166,"top":0.265,"width":0.24305555,"height":0.053333335},"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"MCP OAuth in Claude Chat (Claude Desktop & Web)","depth":24,"bounds":{"left":0.20729166,"top":0.26722223,"width":0.24131945,"height":0.049444444},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"Claude Chat\" encompasses the visual interfaces—both the Claude Desktop application and the Claude.ai web platform.","depth":24,"bounds":{"left":0.20729166,"top":0.33055556,"width":0.21944444,"height":0.08055556},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude.ai (Web Custom Connectors):","depth":26,"bounds":{"left":0.23159721,"top":0.4261111,"width":0.19930555,"height":0.022777777},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"When you add a remote MCP server via the \"Custom Connectors\" menu in your settings, Claude.ai handles the OAuth flow entirely in the browser. You simply paste the MCP server URL. Claude automatically parses the 401 discovery response and redirects you to the third-party service to grant permission, before seamlessly dropping you back into your chat session.","depth":26,"bounds":{"left":0.23159721,"top":0.4261111,"width":0.21875,"height":0.2827778},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude Desktop:","depth":26,"bounds":{"left":0.23159721,"top":0.7238889,"width":0.08923611,"height":0.022777777},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude Desktop traditionally relies on a local configuration file (","depth":26,"bounds":{"left":0.23159721,"top":0.7238889,"width":0.21284722,"height":0.08055556},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"claude_desktop_config.json","depth":27,"bounds":{"left":0.2579861,"top":0.78333336,"width":0.15138888,"height":0.020555556},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"). 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Objective","depth":16,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.","depth":16,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Position Jiminny as a","depth":16,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"data layer for AI-driven revenue workflows","depth":17,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":", not just a standalone product.","depth":16,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"👤 Target user","depth":15,"help_text":"","role_description":"heading","subrole":"AXUnknown"}]...
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3290771524577391835
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7934688909578425078
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idle
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accessibility
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NULL
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
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Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
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Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
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You said what is the oauth required for mcp for clauide code and claude chat
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what is the oauth required for mcp for clauide code and claude chat
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Gemini said
Gemini said
To understand the OAuth requirements for the Model Context Protocol (MCP) in Claude Code and Claude Chat, it helps to look at how MCP handles secure connections.
MCP acts as a universal "USB port" that allows AI models to connect to external data sources and tools.
When connecting to remote, HTTP-based MCP servers, Anthropic relies on the
OAuth 2.1
authorization framework.
This ensures that Claude can access your protected tools securely using temporary tokens, rather than requiring you to copy and paste long-lived API keys, passwords, or configuration secrets.
View source details for citations from Stytch, Model Context Protocol, and Zuplo. Opens side panel.
+2
Here is exactly what is required and how OAuth is handled across both environments.
The Underlying OAuth Flow for MCP
The Underlying OAuth Flow for MCP
Whether you are using Claude Code or Claude Chat, the underlying architecture is identical.
The Claude application acts as the
OAuth Client
, and the remote tool you are connecting to acts as the
Resource Server
.
View source details for citation from Model Context Protocol. Opens side panel.
To build or connect to an MCP server, the OAuth integration must support this standard discovery and token exchange flow:
Initial Handshake (Discovery):
Claude attempts to connect to the MCP server without credentials. The server must reject this with a
401 Unauthorized
HTTP status and include a
WWW-Authenticate
header pointing to a Protected Resource Metadata (PRM) document.
Metadata Fetch:
Claude reads the PRM document to locate your specific Authorization Server (e.g., Okta, Auth0, Clerk, or a custom auth server).
View source details for citation from Model Context Protocol. Opens side panel.
User Consent:
Claude prompts the user to authenticate.
The user is temporarily redirected to a browser window to log in and explicitly grant permission (e.g., "Allow Claude to access your GitHub repositories").
View source details for citations from GitHub and Model Context Protocol. Opens side panel.
+1
Token Exchange:
Claude receives a temporary authorization code via a redirect, which it silently exchanges for a long-lived access token.
View source details for citation from Upstash. Opens side panel.
Access Granted:
Claude now includes this token in the header of subsequent requests to invoke tools and read data on your behalf.
View source details for citation from Model Context Protocol. Opens side panel.
MCP OAuth in Claude Code
MCP OAuth in Claude Code
Claude Code is Anthropic's command-line interface (CLI) tool for software engineering. When you connect Claude Code to a protected remote MCP server, the OAuth flow is adapted for the terminal:
Browser Handoff:
When Claude Code hits an MCP server that requires OAuth, it will generate a secure authorization link and automatically open your default desktop web browser (or output the URL to the terminal for headless environments).
Authentication:
Once you log in and approve access in the browser, the authorization server redirects the payload back to a local loopback port managed by Claude Code, handing off the authorization code.
View source details for citation from Model Context Protocol. Opens side panel.
Alternative (Static Headers):
If setting up a full OAuth 2.1 server is too complex for a quick internal tool, Claude Code also allows you to bypass the browser flow by injecting static bearer tokens directly via the CLI.
You can do this using the
--header
flag (e.g.,
claude mcp add my-server --transport http --header "Authorization: Bearer your-token"
).
View source details for citation from TrueFoundry. Opens side panel.
MCP OAuth in Claude Chat (Claude Desktop & Web)
MCP OAuth in Claude Chat (Claude Desktop & Web)
"Claude Chat" encompasses the visual interfaces—both the Claude Desktop application and the Claude.ai web platform.
Claude.ai (Web Custom Connectors):
When you add a remote MCP server via the "Custom Connectors" menu in your settings, Claude.ai handles the OAuth flow entirely in the browser. You simply paste the MCP server URL. Claude automatically parses the 401 discovery response and redirects you to the third-party service to grant permission, before seamlessly dropping you back into your chat session.
Claude Desktop:
Claude Desktop traditionally relies on a local configuration file (
claude_desktop_config.json
). While local stdio servers often require pasting API keys into this file, remote HTTP MCP servers utilizing OAuth eliminate this security risk.
When Claude Desktop encounters an OAuth-protected server, it pops open your system's default browser to capture your consent.
It then stores the resulting token securely in your operating system's native keychain or credential manager, keeping your
config.json
free of sensitive plaintext passwords.
View source details for citation from GitHub. Opens side panel.
The Bottom Line:
You do not need to build a bespoke authentication protocol for MCP.
If your external tool already utilizes standard OpenID Connect or OAuth 2.0/2.1, Claude Code and Claude Chat are heavily standardized to automatically discover those endpoints, route the user for consent, and manage the resulting tokens behind the scenes.
View source details for citation from GitHub. Opens side panel.
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Jiminny MCP Connector
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
Read time 12 min
12 min
Views 14
14
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Epic
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Document status
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DRAFT
Change emoji Objective
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Change emoji Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a
data layer for AI-driven revenue workflows
, not just a standalone product.
👤 Target user...
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NULL
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71961
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1739
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8
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2026-04-22T14:34:55.647368+00:00
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/Users/lukas/.screenpipe/data/data/2026-04-22/1776 /Users/lukas/.screenpipe/data/data/2026-04-22/1776868495647_m2.jpg...
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Jiminny MCP Connector - Product - Confluence — Wor Jiminny MCP Connector - Product - Confluence — Work...
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jiminny.atlassian.net/wiki/spaces/PROD/pages/37282 jiminny.atlassian.net/wiki/spaces/PROD/pages/3728244737/Jiminny+MCP+Connector...
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monitor_2
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NULL
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NULL
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NULL
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
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You said what is the oauth required for mcp for clauide code and claude chat
You said
what is the oauth required for mcp for clauide code and claude chat
Listen
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Gemini said
Gemini said
To understand the OAuth requirements for the Model Context Protocol (MCP) in Claude Code and Claude Chat, it helps to look at how MCP handles secure connections.
MCP acts as a universal "USB port" that allows AI models to connect to external data sources and tools.
When connecting to remote, HTTP-based MCP servers, Anthropic relies on the
OAuth 2.1
authorization framework.
This ensures that Claude can access your protected tools securely using temporary tokens, rather than requiring you to copy and paste long-lived API keys, passwords, or configuration secrets.
View source details for citations from Stytch, Model Context Protocol, and Zuplo. Opens side panel.
+2
Here is exactly what is required and how OAuth is handled across both environments.
The Underlying OAuth Flow for MCP
The Underlying OAuth Flow for MCP
Whether you are using Claude Code or Claude Chat, the underlying architecture is identical.
The Claude application acts as the
OAuth Client
, and the remote tool you are connecting to acts as the
Resource Server
.
View source details for citation from Model Context Protocol. Opens side panel.
To build or connect to an MCP server, the OAuth integration must support this standard discovery and token exchange flow:
Initial Handshake (Discovery):
Claude attempts to connect to the MCP server without credentials. The server must reject this with a
401 Unauthorized
HTTP status and include a
WWW-Authenticate
header pointing to a Protected Resource Metadata (PRM) document.
Metadata Fetch:
Claude reads the PRM document to locate your specific Authorization Server (e.g., Okta, Auth0, Clerk, or a custom auth server).
View source details for citation from Model Context Protocol. Opens side panel.
User Consent:
Claude prompts the user to authenticate.
The user is temporarily redirected to a browser window to log in and explicitly grant permission (e.g., "Allow Claude to access your GitHub repositories").
View source details for citations from GitHub and Model Context Protocol. Opens side panel.
+1
Token Exchange:
Claude receives a temporary authorization code via a redirect, which it silently exchanges for a long-lived access token.
View source details for citation from Upstash. Opens side panel.
Access Granted:
Claude now includes this token in the header of subsequent requests to invoke tools and read data on your behalf.
View source details for citation from Model Context Protocol. Opens side panel.
MCP OAuth in Claude Code
MCP OAuth in Claude Code
Claude Code is Anthropic's command-line interface (CLI) tool for software engineering. When you connect Claude Code to a protected remote MCP server, the OAuth flow is adapted for the terminal:
Browser Handoff:
When Claude Code hits an MCP server that requires OAuth, it will generate a secure authorization link and automatically open your default desktop web browser (or output the URL to the terminal for headless environments).
Authentication:
Once you log in and approve access in the browser, the authorization server redirects the payload back to a local loopback port managed by Claude Code, handing off the authorization code.
View source details for citation from Model Context Protocol. Opens side panel.
Alternative (Static Headers):
If setting up a full OAuth 2.1 server is too complex for a quick internal tool, Claude Code also allows you to bypass the browser flow by injecting static bearer tokens directly via the CLI.
You can do this using the
--header
flag (e.g.,
claude mcp add my-server --transport http --header "Authorization: Bearer your-token"
).
View source details for citation from TrueFoundry. Opens side panel.
MCP OAuth in Claude Chat (Claude Desktop & Web)
MCP OAuth in Claude Chat (Claude Desktop & Web)
"Claude Chat" encompasses the visual interfaces—both the Claude Desktop application and the Claude.ai web platform.
Claude.ai (Web Custom Connectors):
When you add a remote MCP server via the "Custom Connectors" menu in your settings, Claude.ai handles the OAuth flow entirely in the browser. You simply paste the MCP server URL. Claude automatically parses the 401 discovery response and redirects you to the third-party service to grant permission, before seamlessly dropping you back into your chat session.
Claude Desktop:
Claude Desktop traditionally relies on a local configuration file (
claude_desktop_config.json
). While local stdio servers often require pasting API keys into this file, remote HTTP MCP servers utilizing OAuth eliminate this security risk.
When Claude Desktop encounters an OAuth-protected server, it pops open your system's default browser to capture your consent.
It then stores the resulting token securely in your operating system's native keychain or credential manager, keeping your
config.json
free of sensitive plaintext passwords.
View source details for citation from GitHub. Opens side panel.
The Bottom Line:
You do not need to build a bespoke authentication protocol for MCP.
If your external tool already utilizes standard OpenID Connect or OAuth 2.0/2.1, Claude Code and Claude Chat are heavily standardized to automatically discover those endpoints, route the user for consent, and manage the resulting tokens behind the scenes.
View source details for citation from GitHub. Opens side panel.
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Autologging activities
Deal Insights - Multiple currencies
Deal Insights - Multiple currencies
Reinvent Themes & Topics (and ACS)
Reinvent Themes & Topics (and ACS)
Change emoji Billing Portal
Billing Portal
Change emoji Upload Video/Audio Recordings
Upload Video/Audio Recordings
White-Label Jiminny Instance
White-Label Jiminny Instance
Win/Loss Analysis for a Deal
Win/Loss Analysis for a Deal
Change emoji Hubspot app
Hubspot app
Change emoji Automatically hard delete data for churned customers
Automatically hard delete data for churned customers
Change emoji Ask Jiminny Anything on Call level
Ask Jiminny Anything on Call level
Change emoji Ask Jiminny for Open and Closed Deals
Ask Jiminny for Open and Closed Deals
Change emoji Ask Jiminny Anything on Deal level
Ask Jiminny Anything on Deal level
Change emoji Automatically record all calendar meetings
Automatically record all calendar meetings
Change emoji Product Tiering
Product Tiering
Change emoji Recording Consent
Recording Consent
Change emoji Automated CRM Filling
Automated CRM Filling
Change emoji Automated Exec Reports
Automated Exec Reports
Change emoji Auto-detect Activity Type
Auto-detect Activity Type
AI Signals & Alerts
AI Signals & Alerts
Change emoji AJA on Anything
AJA on Anything
Change emoji AI Call Scoring
AI Call Scoring
Jiminny MCP Connector
Jiminny MCP Connector
Desktop app to record a meetings without visible Notetaker
Desktop app to record a meetings without visible Notetaker
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Feedback
Feedback
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Research & User Feedback
Research & User Feedback
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Jiminny MCP Connector
Updated 10m ago
Updated 10m ago
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
Read time 12 min
12 min
Views 14
14
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Add a reaction
Epic
Link to Epik in Jira
Document status
DRAFT
Epic
Document status
Link to Epik in Jira
DRAFT
Change emoji Objective
Objective
Change emoji Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a
data layer for AI-driven revenue workflows
, not just a standalone product.
👤 Target user
👤 Target user
👤 Target user
Revenue teams using AI tools (Sales,
CS
, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
🤕
🤕
Pain point or problem
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
💥
💥
Impact and benefits
💥 Impact and benefits
For customers
For customers
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
For Jiminny
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
💡
💡
Solution ideas
💡 Solution ideas
Build an
AI Knowledge Connector (
MCP
-based)
that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides
structured insights
(summaries, action items, scoring etc.)
Supports
deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Change emoji Success metrics
Success metrics
Change emoji Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
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Metric
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
Goal
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
Decrease the manual copying of transcripts
Metric
No sort applied to the column
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
decrease by X %
Change emoji User interaction and design
User interaction and design
Change emoji User interaction and design
Simple setup flow:
Generate API key / connect
MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus:
usable by non-technical users
Change emoji Detailed Requirements
Detailed Requirements
Change emoji Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
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Importance
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Notes
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Requirement
No sort applied to the column
Importance
No sort applied to the column
Notes
No sort applied to the column
Requirement
No sort applied to the column
Importance
No sort applied to the column
Notes
No sort applied to the column
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide
MCP
endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails...
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tools","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Expose Jiminny data:","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"list calls (filter by date, user, account, deal)","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"get call","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"get transcript (speaker-separated)","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"get call metadata","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"search calls (keyword + semantic)","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Support filtering by:","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"account / contact/ lead / deal","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"jiminny user","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"date range","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to search and query calls using AI tools","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Support keyword and semantic search across conversations","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide structured insights so customers can generate better outputs","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"For each call, expose:","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Summary","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Action items","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Key points","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Activity type","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Notes","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Call Score","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"CRM information","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to understand deal status across multiple calls/emails","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"}]...
|
-4509002142653947952
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-1811093948198848779
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idle
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accessibility
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NULL
|
Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
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You said what is the oauth required for mcp for clauide code and claude chat
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what is the oauth required for mcp for clauide code and claude chat
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Gemini said
Gemini said
To understand the OAuth requirements for the Model Context Protocol (MCP) in Claude Code and Claude Chat, it helps to look at how MCP handles secure connections.
MCP acts as a universal "USB port" that allows AI models to connect to external data sources and tools.
When connecting to remote, HTTP-based MCP servers, Anthropic relies on the
OAuth 2.1
authorization framework.
This ensures that Claude can access your protected tools securely using temporary tokens, rather than requiring you to copy and paste long-lived API keys, passwords, or configuration secrets.
View source details for citations from Stytch, Model Context Protocol, and Zuplo. Opens side panel.
+2
Here is exactly what is required and how OAuth is handled across both environments.
The Underlying OAuth Flow for MCP
The Underlying OAuth Flow for MCP
Whether you are using Claude Code or Claude Chat, the underlying architecture is identical.
The Claude application acts as the
OAuth Client
, and the remote tool you are connecting to acts as the
Resource Server
.
View source details for citation from Model Context Protocol. Opens side panel.
To build or connect to an MCP server, the OAuth integration must support this standard discovery and token exchange flow:
Initial Handshake (Discovery):
Claude attempts to connect to the MCP server without credentials. The server must reject this with a
401 Unauthorized
HTTP status and include a
WWW-Authenticate
header pointing to a Protected Resource Metadata (PRM) document.
Metadata Fetch:
Claude reads the PRM document to locate your specific Authorization Server (e.g., Okta, Auth0, Clerk, or a custom auth server).
View source details for citation from Model Context Protocol. Opens side panel.
User Consent:
Claude prompts the user to authenticate.
The user is temporarily redirected to a browser window to log in and explicitly grant permission (e.g., "Allow Claude to access your GitHub repositories").
View source details for citations from GitHub and Model Context Protocol. Opens side panel.
+1
Token Exchange:
Claude receives a temporary authorization code via a redirect, which it silently exchanges for a long-lived access token.
View source details for citation from Upstash. Opens side panel.
Access Granted:
Claude now includes this token in the header of subsequent requests to invoke tools and read data on your behalf.
View source details for citation from Model Context Protocol. Opens side panel.
MCP OAuth in Claude Code
MCP OAuth in Claude Code
Claude Code is Anthropic's command-line interface (CLI) tool for software engineering. When you connect Claude Code to a protected remote MCP server, the OAuth flow is adapted for the terminal:
Browser Handoff:
When Claude Code hits an MCP server that requires OAuth, it will generate a secure authorization link and automatically open your default desktop web browser (or output the URL to the terminal for headless environments).
Authentication:
Once you log in and approve access in the browser, the authorization server redirects the payload back to a local loopback port managed by Claude Code, handing off the authorization code.
View source details for citation from Model Context Protocol. Opens side panel.
Alternative (Static Headers):
If setting up a full OAuth 2.1 server is too complex for a quick internal tool, Claude Code also allows you to bypass the browser flow by injecting static bearer tokens directly via the CLI.
You can do this using the
--header
flag (e.g.,
claude mcp add my-server --transport http --header "Authorization: Bearer your-token"
).
View source details for citation from TrueFoundry. Opens side panel.
MCP OAuth in Claude Chat (Claude Desktop & Web)
MCP OAuth in Claude Chat (Claude Desktop & Web)
"Claude Chat" encompasses the visual interfaces—both the Claude Desktop application and the Claude.ai web platform.
Claude.ai (Web Custom Connectors):
When you add a remote MCP server via the "Custom Connectors" menu in your settings, Claude.ai handles the OAuth flow entirely in the browser. You simply paste the MCP server URL. Claude automatically parses the 401 discovery response and redirects you to the third-party service to grant permission, before seamlessly dropping you back into your chat session.
Claude Desktop:
Claude Desktop traditionally relies on a local configuration file (
claude_desktop_config.json
). While local stdio servers often require pasting API keys into this file, remote HTTP MCP servers utilizing OAuth eliminate this security risk.
When Claude Desktop encounters an OAuth-protected server, it pops open your system's default browser to capture your consent.
It then stores the resulting token securely in your operating system's native keychain or credential manager, keeping your
config.json
free of sensitive plaintext passwords.
View source details for citation from GitHub. Opens side panel.
The Bottom Line:
You do not need to build a bespoke authentication protocol for MCP.
If your external tool already utilizes standard OpenID Connect or OAuth 2.0/2.1, Claude Code and Claude Chat are heavily standardized to automatically discover those endpoints, route the user for consent, and manage the resulting tokens behind the scenes.
View source details for citation from GitHub. Opens side panel.
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Ask Jiminny for Open and Closed Deals
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Jiminny MCP Connector
Updated 10m ago
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
Read time 12 min
12 min
Views 14
14
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Add a reaction
Epic
Link to Epik in Jira
Document status
DRAFT
Epic
Document status
Link to Epik in Jira
DRAFT
Change emoji Objective
Objective
Change emoji Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a
data layer for AI-driven revenue workflows
, not just a standalone product.
👤 Target user
👤 Target user
👤 Target user
Revenue teams using AI tools (Sales,
CS
, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
🤕
🤕
Pain point or problem
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
💥
💥
Impact and benefits
💥 Impact and benefits
For customers
For customers
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
For Jiminny
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
💡
💡
Solution ideas
💡 Solution ideas
Build an
AI Knowledge Connector (
MCP
-based)
that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides
structured insights
(summaries, action items, scoring etc.)
Supports
deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Change emoji Success metrics
Success metrics
Change emoji Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
No sort applied to the column
Metric
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
Goal
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
Decrease the manual copying of transcripts
Metric
No sort applied to the column
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
decrease by X %
Change emoji User interaction and design
User interaction and design
Change emoji User interaction and design
Simple setup flow:
Generate API key / connect
MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus:
usable by non-technical users
Change emoji Detailed Requirements
Detailed Requirements
Change emoji Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
No sort applied to the column
Importance
No sort applied to the column
Notes
No sort applied to the column
Requirement
No sort applied to the column
Importance
No sort applied to the column
Notes
No sort applied to the column
Requirement
No sort applied to the column
Importance
No sort applied to the column
Notes
No sort applied to the column
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide
MCP
endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails...
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Jiminny MCP Connector - Product - Confluence — Wor Jiminny MCP Connector - Product - Confluence — Work...
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jiminny.atlassian.net/wiki/spaces/PROD/pages/37282 jiminny.atlassian.net/wiki/spaces/PROD/pages/3728244737/Jiminny+MCP+Connector...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
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You said what is the oauth required for mcp for clauide code and claude chat
You said
what is the oauth required for mcp for clauide code and claude chat
Listen
Show more options
Show thinking
Gemini said
Gemini said
To understand the OAuth requirements for the Model Context Protocol (MCP) in Claude Code and Claude Chat, it helps to look at how MCP handles secure connections.
MCP acts as a universal "USB port" that allows AI models to connect to external data sources and tools.
When connecting to remote, HTTP-based MCP servers, Anthropic relies on the
OAuth 2.1
authorization framework.
This ensures that Claude can access your protected tools securely using temporary tokens, rather than requiring you to copy and paste long-lived API keys, passwords, or configuration secrets.
View source details for citations from Stytch, Model Context Protocol, and Zuplo. Opens side panel.
+2
Here is exactly what is required and how OAuth is handled across both environments.
The Underlying OAuth Flow for MCP
The Underlying OAuth Flow for MCP
Whether you are using Claude Code or Claude Chat, the underlying architecture is identical.
The Claude application acts as the
OAuth Client
, and the remote tool you are connecting to acts as the
Resource Server
.
View source details for citation from Model Context Protocol. Opens side panel.
To build or connect to an MCP server, the OAuth integration must support this standard discovery and token exchange flow:
Initial Handshake (Discovery):
Claude attempts to connect to the MCP server without credentials. The server must reject this with a
401 Unauthorized
HTTP status and include a
WWW-Authenticate
header pointing to a Protected Resource Metadata (PRM) document.
Metadata Fetch:
Claude reads the PRM document to locate your specific Authorization Server (e.g., Okta, Auth0, Clerk, or a custom auth server).
View source details for citation from Model Context Protocol. Opens side panel.
User Consent:
Claude prompts the user to authenticate.
The user is temporarily redirected to a browser window to log in and explicitly grant permission (e.g., "Allow Claude to access your GitHub repositories").
View source details for citations from GitHub and Model Context Protocol. Opens side panel.
+1
Token Exchange:
Claude receives a temporary authorization code via a redirect, which it silently exchanges for a long-lived access token.
View source details for citation from Upstash. Opens side panel.
Access Granted:
Claude now includes this token in the header of subsequent requests to invoke tools and read data on your behalf.
View source details for citation from Model Context Protocol. Opens side panel.
MCP OAuth in Claude Code
MCP OAuth in Claude Code
Claude Code is Anthropic's command-line interface (CLI) tool for software engineering. When you connect Claude Code to a protected remote MCP server, the OAuth flow is adapted for the terminal:
Browser Handoff:
When Claude Code hits an MCP server that requires OAuth, it will generate a secure authorization link and automatically open your default desktop web browser (or output the URL to the terminal for headless environments).
Authentication:
Once you log in and approve access in the browser, the authorization server redirects the payload back to a local loopback port managed by Claude Code, handing off the authorization code.
View source details for citation from Model Context Protocol. Opens side panel.
Alternative (Static Headers):
If setting up a full OAuth 2.1 server is too complex for a quick internal tool, Claude Code also allows you to bypass the browser flow by injecting static bearer tokens directly via the CLI.
You can do this using the
--header
flag (e.g.,
claude mcp add my-server --transport http --header "Authorization: Bearer your-token"
).
View source details for citation from TrueFoundry. Opens side panel.
MCP OAuth in Claude Chat (Claude Desktop & Web)
MCP OAuth in Claude Chat (Claude Desktop & Web)
"Claude Chat" encompasses the visual interfaces—both the Claude Desktop application and the Claude.ai web platform.
Claude.ai (Web Custom Connectors):
When you add a remote MCP server via the "Custom Connectors" menu in your settings, Claude.ai handles the OAuth flow entirely in the browser. You simply paste the MCP server URL. Claude automatically parses the 401 discovery response and redirects you to the third-party service to grant permission, before seamlessly dropping you back into your chat session.
Claude Desktop:
Claude Desktop traditionally relies on a local configuration file (
claude_desktop_config.json
). While local stdio servers often require pasting API keys into this file, remote HTTP MCP servers utilizing OAuth eliminate this security risk.
When Claude Desktop encounters an OAuth-protected server, it pops open your system's default browser to capture your consent.
It then stores the resulting token securely in your operating system's native keychain or credential manager, keeping your
config.json
free of sensitive plaintext passwords.
View source details for citation from GitHub. Opens side panel.
The Bottom Line:
You do not need to build a bespoke authentication protocol for MCP.
If your external tool already utilizes standard OpenID Connect or OAuth 2.0/2.1, Claude Code and Claude Chat are heavily standardized to automatically discover those endpoints, route the user for consent, and manage the resulting tokens behind the scenes.
View source details for citation from GitHub. Opens side panel.
Sources
Sources
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encrypted
Open upload file menu
Tools
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Product Strategy 2025+ Change emoji
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Reinvent Themes & Topics (and ACS)
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Upload Video/Audio Recordings
White-Label Jiminny Instance
White-Label Jiminny Instance
Win/Loss Analysis for a Deal
Win/Loss Analysis for a Deal
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Change emoji Ask Jiminny for Open and Closed Deals
Ask Jiminny for Open and Closed Deals
Change emoji Ask Jiminny Anything on Deal level
Ask Jiminny Anything on Deal level
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Product Tiering
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Recording Consent
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Automated CRM Filling
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Jiminny MCP Connector
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Jiminny MCP Connector
Updated 10m ago
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
Read time 12 min
12 min
Views 14
14
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Add a reaction
Epic
Link to Epik in Jira
Document status
DRAFT
Epic
Document status
Link to Epik in Jira
DRAFT
Change emoji Objective
Objective
Change emoji Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a
data layer for AI-driven revenue workflows
, not just a standalone product.
👤 Target user
👤 Target user
👤 Target user
Revenue teams using AI tools (Sales,
CS
, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
🤕
🤕
Pain point or problem
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
💥
💥
Impact and benefits
💥 Impact and benefits
For customers
For customers
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
For Jiminny
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
💡
💡
Solution ideas
💡 Solution ideas
Build an
AI Knowledge Connector (
MCP
-based)
that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides
structured insights
(summaries, action items, scoring etc.)
Supports
deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Change emoji Success metrics
Success metrics
Change emoji Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
No sort applied to the column
Metric
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
Goal
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
Decrease the manual copying of transcripts
Metric
No sort applied to the column
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
decrease by X %
Change emoji User interaction and design
User interaction and design
Change emoji User interaction and design
Simple setup flow:
Generate API key / connect
MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus:
usable by non-technical users
Change emoji Detailed Requirements
Detailed Requirements
Change emoji Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
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Importance
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Notes
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Requirement
No sort applied to the column
Importance
No sort applied to the column
Notes
No sort applied to the column
Requirement
No sort applied to the column
Importance
No sort applied to the column
Notes
No sort applied to the column
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide
MCP
endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH...
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column","depth":21,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Requirement","depth":21,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"No sort applied to the column","depth":20,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Importance","depth":21,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"No sort applied to the column","depth":20,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Notes","depth":21,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"No sort applied to the column","depth":20,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"MCP","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"endpoint so customers can easily integrate Jiminny into their AI stack","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to access call data through AI tools","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Expose Jiminny data:","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"list calls (filter by date, user, account, deal)","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"get call","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"get transcript (speaker-separated)","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"get call metadata","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"search calls (keyword + semantic)","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Support filtering by:","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"account / contact/ lead / deal","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"jiminny user","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"date range","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to search and query calls using AI tools","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Support keyword and semantic search across conversations","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide structured insights so customers can generate better outputs","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"For each call, expose:","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Summary","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Action items","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Key points","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Activity type","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Notes","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Call Score","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"CRM information","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to understand deal status across multiple calls/emails","depth":20,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"}]...
|
1415353685212680760
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-1811093948198848795
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idle
|
accessibility
|
NULL
|
Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
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You said what is the oauth required for mcp for clauide code and claude chat
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what is the oauth required for mcp for clauide code and claude chat
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Gemini said
Gemini said
To understand the OAuth requirements for the Model Context Protocol (MCP) in Claude Code and Claude Chat, it helps to look at how MCP handles secure connections.
MCP acts as a universal "USB port" that allows AI models to connect to external data sources and tools.
When connecting to remote, HTTP-based MCP servers, Anthropic relies on the
OAuth 2.1
authorization framework.
This ensures that Claude can access your protected tools securely using temporary tokens, rather than requiring you to copy and paste long-lived API keys, passwords, or configuration secrets.
View source details for citations from Stytch, Model Context Protocol, and Zuplo. Opens side panel.
+2
Here is exactly what is required and how OAuth is handled across both environments.
The Underlying OAuth Flow for MCP
The Underlying OAuth Flow for MCP
Whether you are using Claude Code or Claude Chat, the underlying architecture is identical.
The Claude application acts as the
OAuth Client
, and the remote tool you are connecting to acts as the
Resource Server
.
View source details for citation from Model Context Protocol. Opens side panel.
To build or connect to an MCP server, the OAuth integration must support this standard discovery and token exchange flow:
Initial Handshake (Discovery):
Claude attempts to connect to the MCP server without credentials. The server must reject this with a
401 Unauthorized
HTTP status and include a
WWW-Authenticate
header pointing to a Protected Resource Metadata (PRM) document.
Metadata Fetch:
Claude reads the PRM document to locate your specific Authorization Server (e.g., Okta, Auth0, Clerk, or a custom auth server).
View source details for citation from Model Context Protocol. Opens side panel.
User Consent:
Claude prompts the user to authenticate.
The user is temporarily redirected to a browser window to log in and explicitly grant permission (e.g., "Allow Claude to access your GitHub repositories").
View source details for citations from GitHub and Model Context Protocol. Opens side panel.
+1
Token Exchange:
Claude receives a temporary authorization code via a redirect, which it silently exchanges for a long-lived access token.
View source details for citation from Upstash. Opens side panel.
Access Granted:
Claude now includes this token in the header of subsequent requests to invoke tools and read data on your behalf.
View source details for citation from Model Context Protocol. Opens side panel.
MCP OAuth in Claude Code
MCP OAuth in Claude Code
Claude Code is Anthropic's command-line interface (CLI) tool for software engineering. When you connect Claude Code to a protected remote MCP server, the OAuth flow is adapted for the terminal:
Browser Handoff:
When Claude Code hits an MCP server that requires OAuth, it will generate a secure authorization link and automatically open your default desktop web browser (or output the URL to the terminal for headless environments).
Authentication:
Once you log in and approve access in the browser, the authorization server redirects the payload back to a local loopback port managed by Claude Code, handing off the authorization code.
View source details for citation from Model Context Protocol. Opens side panel.
Alternative (Static Headers):
If setting up a full OAuth 2.1 server is too complex for a quick internal tool, Claude Code also allows you to bypass the browser flow by injecting static bearer tokens directly via the CLI.
You can do this using the
--header
flag (e.g.,
claude mcp add my-server --transport http --header "Authorization: Bearer your-token"
).
View source details for citation from TrueFoundry. Opens side panel.
MCP OAuth in Claude Chat (Claude Desktop & Web)
MCP OAuth in Claude Chat (Claude Desktop & Web)
"Claude Chat" encompasses the visual interfaces—both the Claude Desktop application and the Claude.ai web platform.
Claude.ai (Web Custom Connectors):
When you add a remote MCP server via the "Custom Connectors" menu in your settings, Claude.ai handles the OAuth flow entirely in the browser. You simply paste the MCP server URL. Claude automatically parses the 401 discovery response and redirects you to the third-party service to grant permission, before seamlessly dropping you back into your chat session.
Claude Desktop:
Claude Desktop traditionally relies on a local configuration file (
claude_desktop_config.json
). While local stdio servers often require pasting API keys into this file, remote HTTP MCP servers utilizing OAuth eliminate this security risk.
When Claude Desktop encounters an OAuth-protected server, it pops open your system's default browser to capture your consent.
It then stores the resulting token securely in your operating system's native keychain or credential manager, keeping your
config.json
free of sensitive plaintext passwords.
View source details for citation from GitHub. Opens side panel.
The Bottom Line:
You do not need to build a bespoke authentication protocol for MCP.
If your external tool already utilizes standard OpenID Connect or OAuth 2.0/2.1, Claude Code and Claude Chat are heavily standardized to automatically discover those endpoints, route the user for consent, and manage the resulting tokens behind the scenes.
View source details for citation from GitHub. Opens side panel.
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Upload Video/Audio Recordings
White-Label Jiminny Instance
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Win/Loss Analysis for a Deal
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Change emoji Automatically hard delete data for churned customers
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Change emoji Ask Jiminny Anything on Call level
Ask Jiminny Anything on Call level
Change emoji Ask Jiminny for Open and Closed Deals
Ask Jiminny for Open and Closed Deals
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Ask Jiminny Anything on Deal level
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Automatically record all calendar meetings
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Recording Consent
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Automated CRM Filling
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Jiminny MCP Connector
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
Read time 12 min
12 min
Views 14
14
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Epic
Link to Epik in Jira
Document status
DRAFT
Epic
Document status
Link to Epik in Jira
DRAFT
Change emoji Objective
Objective
Change emoji Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a
data layer for AI-driven revenue workflows
, not just a standalone product.
👤 Target user
👤 Target user
👤 Target user
Revenue teams using AI tools (Sales,
CS
, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
🤕
🤕
Pain point or problem
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
💥
💥
Impact and benefits
💥 Impact and benefits
For customers
For customers
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
For Jiminny
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
💡
💡
Solution ideas
💡 Solution ideas
Build an
AI Knowledge Connector (
MCP
-based)
that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides
structured insights
(summaries, action items, scoring etc.)
Supports
deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Change emoji Success metrics
Success metrics
Change emoji Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
No sort applied to the column
Metric
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
Goal
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
Decrease the manual copying of transcripts
Metric
No sort applied to the column
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
decrease by X %
Change emoji User interaction and design
User interaction and design
Change emoji User interaction and design
Simple setup flow:
Generate API key / connect
MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus:
usable by non-technical users
Change emoji Detailed Requirements
Detailed Requirements
Change emoji Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
No sort applied to the column
Importance
No sort applied to the column
Notes
No sort applied to the column
Requirement
No sort applied to the column
Importance
No sort applied to the column
Notes
No sort applied to the column
Requirement
No sort applied to the column
Importance
No sort applied to the column
Notes
No sort applied to the column
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide
MCP
endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH...
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Jiminny MCP Connector - Product - Confluence — Wor Jiminny MCP Connector - Product - Confluence — Work...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
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You said what is the oauth required for mcp for clauide code and claude chat
You said
what is the oauth required for mcp for clauide code and claude chat
Listen
Show more options
Show thinking
Gemini said
Gemini said
To understand the OAuth requirements for the Model Context Protocol (MCP) in Claude Code and Claude Chat, it helps to look at how MCP handles secure connections.
MCP acts as a universal "USB port" that allows AI models to connect to external data sources and tools.
When connecting to remote, HTTP-based MCP servers, Anthropic relies on the
OAuth 2.1
authorization framework.
This ensures that Claude can access your protected tools securely using temporary tokens, rather than requiring you to copy and paste long-lived API keys, passwords, or configuration secrets.
View source details for citations from Stytch, Model Context Protocol, and Zuplo. Opens side panel.
+2
Here is exactly what is required and how OAuth is handled across both environments.
The Underlying OAuth Flow for MCP
The Underlying OAuth Flow for MCP
Whether you are using Claude Code or Claude Chat, the underlying architecture is identical.
The Claude application acts as the
OAuth Client
, and the remote tool you are connecting to acts as the
Resource Server
.
View source details for citation from Model Context Protocol. Opens side panel.
To build or connect to an MCP server, the OAuth integration must support this standard discovery and token exchange flow:
Initial Handshake (Discovery):
Claude attempts to connect to the MCP server without credentials. The server must reject this with a
401 Unauthorized
HTTP status and include a
WWW-Authenticate
header pointing to a Protected Resource Metadata (PRM) document.
Metadata Fetch:
Claude reads the PRM document to locate your specific Authorization Server (e.g., Okta, Auth0, Clerk, or a custom auth server).
View source details for citation from Model Context Protocol. Opens side panel.
User Consent:
Claude prompts the user to authenticate.
The user is temporarily redirected to a browser window to log in and explicitly grant permission (e.g., "Allow Claude to access your GitHub repositories").
View source details for citations from GitHub and Model Context Protocol. Opens side panel.
+1
Token Exchange:
Claude receives a temporary authorization code via a redirect, which it silently exchanges for a long-lived access token.
View source details for citation from Upstash. Opens side panel.
Access Granted:
Claude now includes this token in the header of subsequent requests to invoke tools and read data on your behalf.
View source details for citation from Model Context Protocol. Opens side panel.
MCP OAuth in Claude Code
MCP OAuth in Claude Code
Claude Code is Anthropic's command-line interface (CLI) tool for software engineering. When you connect Claude Code to a protected remote MCP server, the OAuth flow is adapted for the terminal:
Browser Handoff:
When Claude Code hits an MCP server that requires OAuth, it will generate a secure authorization link and automatically open your default desktop web browser (or output the URL to the terminal for headless environments).
Authentication:
Once you log in and approve access in the browser, the authorization server redirects the payload back to a local loopback port managed by Claude Code, handing off the authorization code.
View source details for citation from Model Context Protocol. Opens side panel.
Alternative (Static Headers):
If setting up a full OAuth 2.1 server is too complex for a quick internal tool, Claude Code also allows you to bypass the browser flow by injecting static bearer tokens directly via the CLI.
You can do this using the
--header
flag (e.g.,
claude mcp add my-server --transport http --header "Authorization: Bearer your-token"
).
View source details for citation from TrueFoundry. Opens side panel.
MCP OAuth in Claude Chat (Claude Desktop & Web)
MCP OAuth in Claude Chat (Claude Desktop & Web)
"Claude Chat" encompasses the visual interfaces—both the Claude Desktop application and the Claude.ai web platform.
Claude.ai (Web Custom Connectors):
When you add a remote MCP server via the "Custom Connectors" menu in your settings, Claude.ai handles the OAuth flow entirely in the browser. You simply paste the MCP server URL. Claude automatically parses the 401 discovery response and redirects you to the third-party service to grant permission, before seamlessly dropping you back into your chat session.
Claude Desktop:
Claude Desktop traditionally relies on a local configuration file (
claude_desktop_config.json
). While local stdio servers often require pasting API keys into this file, remote HTTP MCP servers utilizing OAuth eliminate this security risk.
When Claude Desktop encounters an OAuth-protected server, it pops open your system's default browser to capture your consent.
It then stores the resulting token securely in your operating system's native keychain or credential manager, keeping your
config.json
free of sensitive plaintext passwords.
View source details for citation from GitHub. Opens side panel.
The Bottom Line:
You do not need to build a bespoke authentication protocol for MCP.
If your external tool already utilizes standard OpenID Connect or OAuth 2.0/2.1, Claude Code and Claude Chat are heavily standardized to automatically discover those endpoints, route the user for consent, and manage the resulting tokens behind the scenes.
View source details for citation from GitHub. Opens side panel.
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
Read time 12 min
12 min
Views 14
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said","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"what is the oauth required for mcp for clauide code and claude chat","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Listen","depth":22,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXButton","text":"Show more options","depth":20,"role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Show thinking","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"Gemini said","depth":20,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Gemini said","depth":21,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"To understand the OAuth requirements for the Model Context Protocol (MCP) in Claude Code and Claude Chat, it helps to look at how MCP handles secure connections.","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"MCP acts as a universal \"USB port\" that allows AI models to connect to external data sources and tools.","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"When connecting to remote, HTTP-based MCP servers, Anthropic relies on the","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"OAuth 2.1","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"authorization framework.","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"This ensures that Claude can access your protected tools securely using temporary tokens, rather than requiring you to copy and paste long-lived API keys, passwords, or configuration secrets.","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"View source details for citations from Stytch, Model Context Protocol, and Zuplo. Opens side panel.","depth":24,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"+2","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Here is exactly what is required and how OAuth is handled across both environments.","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"The Underlying OAuth Flow for MCP","depth":23,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The Underlying OAuth Flow for MCP","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Whether you are using Claude Code or Claude Chat, the underlying architecture is identical.","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The Claude application acts as the","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"OAuth Client","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":", and the remote tool you are connecting to acts as the","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Resource Server","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":".","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"View source details for citation from Model Context Protocol. Opens side panel.","depth":24,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"To build or connect to an MCP server, the OAuth integration must support this standard discovery and token exchange flow:","depth":24,"bounds":{"left":0.3695146,"top":0.0,"width":0.11087101,"height":0.057861134},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Initial Handshake (Discovery):","depth":26,"bounds":{"left":0.38148272,"top":0.0,"width":0.07829122,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude attempts to connect to the MCP server without credentials. The server must reject this with a","depth":26,"bounds":{"left":0.38148272,"top":0.0,"width":0.1043883,"height":0.07861133},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"401 Unauthorized","depth":27,"bounds":{"left":0.40940824,"top":0.035115723,"width":0.044714097,"height":0.014764565},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HTTP status and include a","depth":26,"bounds":{"left":0.38148272,"top":0.033918597,"width":0.09042553,"height":0.037110932},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"WWW-Authenticate","depth":27,"bounds":{"left":0.43334442,"top":0.05586592,"width":0.044714097,"height":0.014764565},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"header pointing to a Protected Resource Metadata (PRM) document.","depth":26,"bounds":{"left":0.38148272,"top":0.054668795,"width":0.09990027,"height":0.057861134},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Metadata Fetch:","depth":26,"bounds":{"left":0.38148272,"top":0.12330407,"width":0.042220745,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude reads the PRM document to locate your specific Authorization Server (e.g., Okta, Auth0, Clerk, or a custom auth server).","depth":26,"bounds":{"left":0.38148272,"top":0.12330407,"width":0.09840426,"height":0.07861133},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"View source details for citation from Model Context Protocol. 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14","depth":15,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"14","depth":17,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Add a reaction","depth":14,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Add a reaction","depth":16,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Epic","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Link to Epik in Jira","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"}]...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
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[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
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You said what is the oauth required for mcp for clauide code and claude chat
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what is the oauth required for mcp for clauide code and claude chat
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Gemini said
Gemini said
To understand the OAuth requirements for the Model Context Protocol (MCP) in Claude Code and Claude Chat, it helps to look at how MCP handles secure connections.
MCP acts as a universal "USB port" that allows AI models to connect to external data sources and tools.
When connecting to remote, HTTP-based MCP servers, Anthropic relies on the
OAuth 2.1
authorization framework.
This ensures that Claude can access your protected tools securely using temporary tokens, rather than requiring you to copy and paste long-lived API keys, passwords, or configuration secrets.
View source details for citations from Stytch, Model Context Protocol, and Zuplo. Opens side panel.
+2
Here is exactly what is required and how OAuth is handled across both environments.
The Underlying OAuth Flow for MCP
The Underlying OAuth Flow for MCP
Whether you are using Claude Code or Claude Chat, the underlying architecture is identical.
The Claude application acts as the
OAuth Client
, and the remote tool you are connecting to acts as the
Resource Server
.
View source details for citation from Model Context Protocol. Opens side panel.
To build or connect to an MCP server, the OAuth integration must support this standard discovery and token exchange flow:
Initial Handshake (Discovery):
Claude attempts to connect to the MCP server without credentials. The server must reject this with a
401 Unauthorized
HTTP status and include a
WWW-Authenticate
header pointing to a Protected Resource Metadata (PRM) document.
Metadata Fetch:
Claude reads the PRM document to locate your specific Authorization Server (e.g., Okta, Auth0, Clerk, or a custom auth server).
View source details for citation from Model Context Protocol. Opens side panel.
User Consent:
Claude prompts the user to authenticate.
The user is temporarily redirected to a browser window to log in and explicitly grant permission (e.g., "Allow Claude to access your GitHub repositories").
View source details for citations from GitHub and Model Context Protocol. Opens side panel.
+1
Token Exchange:
Claude receives a temporary authorization code via a redirect, which it silently exchanges for a long-lived access token.
View source details for citation from Upstash. Opens side panel.
Access Granted:
Claude now includes this token in the header of subsequent requests to invoke tools and read data on your behalf.
View source details for citation from Model Context Protocol. Opens side panel.
MCP OAuth in Claude Code
MCP OAuth in Claude Code
Claude Code is Anthropic's command-line interface (CLI) tool for software engineering. When you connect Claude Code to a protected remote MCP server, the OAuth flow is adapted for the terminal:
Browser Handoff:
When Claude Code hits an MCP server that requires OAuth, it will generate a secure authorization link and automatically open your default desktop web browser (or output the URL to the terminal for headless environments).
Authentication:
Once you log in and approve access in the browser, the authorization server redirects the payload back to a local loopback port managed by Claude Code, handing off the authorization code.
View source details for citation from Model Context Protocol. Opens side panel.
Alternative (Static Headers):
If setting up a full OAuth 2.1 server is too complex for a quick internal tool, Claude Code also allows you to bypass the browser flow by injecting static bearer tokens directly via the CLI.
You can do this using the
--header
flag (e.g.,
claude mcp add my-server --transport http --header "Authorization: Bearer your-token"
).
View source details for citation from TrueFoundry. Opens side panel.
MCP OAuth in Claude Chat (Claude Desktop & Web)
MCP OAuth in Claude Chat (Claude Desktop & Web)
"Claude Chat" encompasses the visual interfaces—both the Claude Desktop application and the Claude.ai web platform.
Claude.ai (Web Custom Connectors):
When you add a remote MCP server via the "Custom Connectors" menu in your settings, Claude.ai handles the OAuth flow entirely in the browser. You simply paste the MCP server URL. Claude automatically parses the 401 discovery response and redirects you to the third-party service to grant permission, before seamlessly dropping you back into your chat session.
Claude Desktop:
Claude Desktop traditionally relies on a local configuration file (
claude_desktop_config.json
). While local stdio servers often require pasting API keys into this file, remote HTTP MCP servers utilizing OAuth eliminate this security risk.
When Claude Desktop encounters an OAuth-protected server, it pops open your system's default browser to capture your consent.
It then stores the resulting token securely in your operating system's native keychain or credential manager, keeping your
config.json
free of sensitive plaintext passwords.
View source details for citation from GitHub. Opens side panel.
The Bottom Line:
You do not need to build a bespoke authentication protocol for MCP.
If your external tool already utilizes standard OpenID Connect or OAuth 2.0/2.1, Claude Code and Claude Chat are heavily standardized to automatically discover those endpoints, route the user for consent, and manage the resulting tokens behind the scenes.
View source details for citation from GitHub. Opens side panel.
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Jiminny MCP Connector
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By Galya Dimitrova
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Read time 12 min
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Views 14
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Epic
Link to Epik in Jira...
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/Users/lukas/.screenpipe/data/data/2026-04-22/1776 /Users/lukas/.screenpipe/data/data/2026-04-22/1776868527744_m1.jpg...
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Jiminny MCP Connector - Product - Confluence — Wor Jiminny MCP Connector - Product - Confluence — Work...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
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Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
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You said what is the oauth required for mcp for clauide code and claude chat
You said
what is the oauth required for mcp for clauide code and claude chat
Listen
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Gemini said
Gemini said
To understand the OAuth requirements for the Model Context Protocol (MCP) in Claude Code and Claude Chat, it helps to look at how MCP handles secure connections.
MCP acts as a universal "USB port" that allows AI models to connect to external data sources and tools.
When connecting to remote, HTTP-based MCP servers, Anthropic relies on the
OAuth 2.1
authorization framework.
This ensures that Claude can access your protected tools securely using temporary tokens, rather than requiring you to copy and paste long-lived API keys, passwords, or configuration secrets.
View source details for citations from Stytch, Model Context Protocol, and Zuplo. Opens side panel.
+2
Here is exactly what is required and how OAuth is handled across both environments.
The Underlying OAuth Flow for MCP
The Underlying OAuth Flow for MCP
Whether you are using Claude Code or Claude Chat, the underlying architecture is identical.
The Claude application acts as the
OAuth Client
, and the remote tool you are connecting to acts as the
Resource Server
.
View source details for citation from Model Context Protocol. Opens side panel.
To build or connect to an MCP server, the OAuth integration must support this standard discovery and token exchange flow:
Initial Handshake (Discovery):
Claude attempts to connect to the MCP server without credentials. The server must reject this with a
401 Unauthorized
HTTP status and include a
WWW-Authenticate
header pointing to a Protected Resource Metadata (PRM) document.
Metadata Fetch:
Claude reads the PRM document to locate your specific Authorization Server (e.g., Okta, Auth0, Clerk, or a custom auth server).
View source details for citation from Model Context Protocol. Opens side panel.
User Consent:
Claude prompts the user to authenticate.
The user is temporarily redirected to a browser window to log in and explicitly grant permission (e.g., "Allow Claude to access your GitHub repositories").
View source details for citations from GitHub and Model Context Protocol. Opens side panel.
+1
Token Exchange:
Claude receives a temporary authorization code via a redirect, which it silently exchanges for a long-lived access token.
View source details for citation from Upstash. Opens side panel.
Access Granted:
Claude now includes this token in the header of subsequent requests to invoke tools and read data on your behalf.
View source details for citation from Model Context Protocol. Opens side panel.
MCP OAuth in Claude Code
MCP OAuth in Claude Code
Claude Code is Anthropic's command-line interface (CLI) tool for software engineering. When you connect Claude Code to a protected remote MCP server, the OAuth flow is adapted for the terminal:
Browser Handoff:
When Claude Code hits an MCP server that requires OAuth, it will generate a secure authorization link and automatically open your default desktop web browser (or output the URL to the terminal for headless environments).
Authentication:
Once you log in and approve access in the browser, the authorization server redirects the payload back to a local loopback port managed by Claude Code, handing off the authorization code.
View source details for citation from Model Context Protocol. Opens side panel.
Alternative (Static Headers):
If setting up a full OAuth 2.1 server is too complex for a quick internal tool, Claude Code also allows you to bypass the browser flow by injecting static bearer tokens directly via the CLI.
You can do this using the
--header
flag (e.g.,
claude mcp add my-server --transport http --header "Authorization: Bearer your-token"
).
View source details for citation from TrueFoundry. Opens side panel.
MCP OAuth in Claude Chat (Claude Desktop & Web)
MCP OAuth in Claude Chat (Claude Desktop & Web)
"Claude Chat" encompasses the visual interfaces—both the Claude Desktop application and the Claude.ai web platform.
Claude.ai (Web Custom Connectors):
When you add a remote MCP server via the "Custom Connectors" menu in your settings, Claude.ai handles the OAuth flow entirely in the browser. You simply paste the MCP server URL. Claude automatically parses the 401 discovery response and redirects you to the third-party service to grant permission, before seamlessly dropping you back into your chat session.
Claude Desktop:
Claude Desktop traditionally relies on a local configuration file (
claude_desktop_config.json
). While local stdio servers often require pasting API keys into this file, remote HTTP MCP servers utilizing OAuth eliminate this security risk.
When Claude Desktop encounters an OAuth-protected server, it pops open your system's default browser to capture your consent.
It then stores the resulting token securely in your operating system's native keychain or credential manager, keeping your
config.json
free of sensitive plaintext passwords.
View source details for citation from GitHub. Opens side panel.
The Bottom Line:
You do not need to build a bespoke authentication protocol for MCP.
If your external tool already utilizes standard OpenID Connect or OAuth 2.0/2.1, Claude Code and Claude Chat are heavily standardized to automatically discover those endpoints, route the user for consent, and manage the resulting tokens behind the scenes.
View source details for citation from GitHub. Opens side panel.
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
Read time 12 min
12 min
Views 14
14
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Add a reaction
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Link to Epik in Jira
Document status
DRAFT
Epic
Document status
Link to Epik in Jira
DRAFT
Change emoji Objective
Objective
Change emoji Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a
data layer for AI-driven revenue workflows
, not just a standalone product.
👤 Target user
👤 Target user
👤 Target user
Revenue teams using AI tools (Sales,
CS
, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
🤕
🤕
Pain point or problem
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
💥
💥
Impact and benefits
💥 Impact and benefits
For customers
For customers
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
For Jiminny
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
💡
💡
Solution ideas
💡 Solution ideas
Build an
AI Knowledge Connector (
MCP
-based)
that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides
structured insights
(summaries, action items, scoring etc.)
Supports
deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Change emoji Success metrics
Success metrics
Change emoji Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
No sort applied to the column
Metric
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
Goal
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
Decrease the manual copying of transcripts
Metric
No sort applied to the column
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
decrease by X %
Change emoji User interaction and design
User interaction and design
Change emoji User interaction and design
Simple setup flow:
Generate API key / connect
MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus:
usable by non-technical users
Change emoji Detailed Requirements
Detailed Requirements
Change emoji Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
No sort applied to the column
Importance
No sort applied to the column
Notes
No sort applied to the column
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide
MCP
endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
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You said what is the oauth required for mcp for clauide code and claude chat
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what is the oauth required for mcp for clauide code and claude chat
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Gemini said
To understand the OAuth requirements for the Model Context Protocol (MCP) in Claude Code and Claude Chat, it helps to look at how MCP handles secure connections.
MCP acts as a universal "USB port" that allows AI models to connect to external data sources and tools.
When connecting to remote, HTTP-based MCP servers, Anthropic relies on the
OAuth 2.1
authorization framework.
This ensures that Claude can access your protected tools securely using temporary tokens, rather than requiring you to copy and paste long-lived API keys, passwords, or configuration secrets.
View source details for citations from Stytch, Model Context Protocol, and Zuplo. Opens side panel.
+2
Here is exactly what is required and how OAuth is handled across both environments.
The Underlying OAuth Flow for MCP
The Underlying OAuth Flow for MCP
Whether you are using Claude Code or Claude Chat, the underlying architecture is identical.
The Claude application acts as the
OAuth Client
, and the remote tool you are connecting to acts as the
Resource Server
.
View source details for citation from Model Context Protocol. Opens side panel.
To build or connect to an MCP server, the OAuth integration must support this standard discovery and token exchange flow:
Initial Handshake (Discovery):
Claude attempts to connect to the MCP server without credentials. The server must reject this with a
401 Unauthorized
HTTP status and include a
WWW-Authenticate
header pointing to a Protected Resource Metadata (PRM) document.
Metadata Fetch:
Claude reads the PRM document to locate your specific Authorization Server (e.g., Okta, Auth0, Clerk, or a custom auth server).
View source details for citation from Model Context Protocol. Opens side panel.
User Consent:
Claude prompts the user to authenticate.
The user is temporarily redirected to a browser window to log in and explicitly grant permission (e.g., "Allow Claude to access your GitHub repositories").
View source details for citations from GitHub and Model Context Protocol. Opens side panel.
+1
Token Exchange:
Claude receives a temporary authorization code via a redirect, which it silently exchanges for a long-lived access token.
View source details for citation from Upstash. Opens side panel.
Access Granted:
Claude now includes this token in the header of subsequent requests to invoke tools and read data on your behalf.
View source details for citation from Model Context Protocol. Opens side panel.
MCP OAuth in Claude Code
MCP OAuth in Claude Code
Claude Code is Anthropic's command-line interface (CLI) tool for software engineering. When you connect Claude Code to a protected remote MCP server, the OAuth flow is adapted for the terminal:
Browser Handoff:
When Claude Code hits an MCP server that requires OAuth, it will generate a secure authorization link and automatically open your default desktop web browser (or output the URL to the terminal for headless environments).
Authentication:
Once you log in and approve access in the browser, the authorization server redirects the payload back to a local loopback port managed by Claude Code, handing off the authorization code.
View source details for citation from Model Context Protocol. Opens side panel.
Alternative (Static Headers):
If setting up a full OAuth 2.1 server is too complex for a quick internal tool, Claude Code also allows you to bypass the browser flow by injecting static bearer tokens directly via the CLI.
You can do this using the
--header
flag (e.g.,
claude mcp add my-server --transport http --header "Authorization: Bearer your-token"
).
View source details for citation from TrueFoundry. Opens side panel.
MCP OAuth in Claude Chat (Claude Desktop & Web)
MCP OAuth in Claude Chat (Claude Desktop & Web)
"Claude Chat" encompasses the visual interfaces—both the Claude Desktop application and the Claude.ai web platform.
Claude.ai (Web Custom Connectors):
When you add a remote MCP server via the "Custom Connectors" menu in your settings, Claude.ai handles the OAuth flow entirely in the browser. You simply paste the MCP server URL. Claude automatically parses the 401 discovery response and redirects you to the third-party service to grant permission, before seamlessly dropping you back into your chat session.
Claude Desktop:
Claude Desktop traditionally relies on a local configuration file (
claude_desktop_config.json
). While local stdio servers often require pasting API keys into this file, remote HTTP MCP servers utilizing OAuth eliminate this security risk.
When Claude Desktop encounters an OAuth-protected server, it pops open your system's default browser to capture your consent.
It then stores the resulting token securely in your operating system's native keychain or credential manager, keeping your
config.json
free of sensitive plaintext passwords.
View source details for citation from GitHub. Opens side panel.
The Bottom Line:
You do not need to build a bespoke authentication protocol for MCP.
If your external tool already utilizes standard OpenID Connect or OAuth 2.0/2.1, Claude Code and Claude Chat are heavily standardized to automatically discover those endpoints, route the user for consent, and manage the resulting tokens behind the scenes.
View source details for citation from GitHub. Opens side panel.
Sources
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Product Strategy 2025+ Change emoji
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Ways of working - Product processes Change emoji
Ways of working - Product processes
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Core Product Performance Metrics 📈
Core Product Performance Metrics
📈
Core Product Performance Metrics
Product Documentation Change emoji
Product Documentation
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Product Briefs Change emoji
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Change emoji Activity Export
Activity Export
Change emoji Autologging activities
Autologging activities
Deal Insights - Multiple currencies
Deal Insights - Multiple currencies
Reinvent Themes & Topics (and ACS)
Reinvent Themes & Topics (and ACS)
Change emoji Billing Portal
Billing Portal
Change emoji Upload Video/Audio Recordings
Upload Video/Audio Recordings
White-Label Jiminny Instance
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Win/Loss Analysis for a Deal
Win/Loss Analysis for a Deal
Change emoji Hubspot app
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Change emoji Automatically hard delete data for churned customers
Automatically hard delete data for churned customers
Change emoji Ask Jiminny Anything on Call level
Ask Jiminny Anything on Call level
Change emoji Ask Jiminny for Open and Closed Deals
Ask Jiminny for Open and Closed Deals
Change emoji Ask Jiminny Anything on Deal level
Ask Jiminny Anything on Deal level
Change emoji Automatically record all calendar meetings
Automatically record all calendar meetings
Change emoji Product Tiering
Product Tiering
Change emoji Recording Consent
Recording Consent
Change emoji Automated CRM Filling
Automated CRM Filling
Change emoji Automated Exec Reports
Automated Exec Reports
Change emoji Auto-detect Activity Type
Auto-detect Activity Type
AI Signals & Alerts
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Change emoji AJA on Anything
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Jiminny MCP Connector
Updated 10m ago
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
Read time 12 min
12 min
Views 14
14
Add a reaction
Add a reaction
Epic
Link to Epik in Jira
Document status
DRAFT
Epic
Document status
Link to Epik in Jira
DRAFT
Change emoji Objective
Objective
Change emoji Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a
data layer for AI-driven revenue workflows
, not just a standalone product.
👤 Target user
👤 Target user
👤 Target user
Revenue teams using AI tools (Sales,
CS
, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
🤕
🤕
Pain point or problem
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
💥
💥
Impact and benefits
💥 Impact and benefits
For customers
For customers
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
For Jiminny
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
💡
💡
Solution ideas
💡 Solution ideas
Build an
AI Knowledge Connector (
MCP
-based)
that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides
structured insights
(summaries, action items, scoring etc.)
Supports
deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Change emoji Success metrics
Success metrics
Change emoji Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
No sort applied to the column
Metric
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
Goal
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
Decrease the manual copying of transcripts
Metric
No sort applied to the column
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
decrease by X %
Change emoji User interaction and design
User interaction and design
Change emoji User interaction and design
Simple setup flow:
Generate API key / connect
MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus:
usable by non-technical users
Change emoji Detailed Requirements
Detailed Requirements
Change emoji Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
No sort applied to the column
Importance
No sort applied to the column
Notes
No sort applied to the column
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide
MCP
endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)...
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Jiminny MCP Connector - Product - Confluence — Wor Jiminny MCP Connector - Product - Confluence — Work...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
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You said what is the oauth required for mcp for clauide code and claude chat
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what is the oauth required for mcp for clauide code and claude chat
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Gemini said
Gemini said
To understand the OAuth requirements for the Model Context Protocol (MCP) in Claude Code and Claude Chat, it helps to look at how MCP handles secure connections.
MCP acts as a universal "USB port" that allows AI models to connect to external data sources and tools.
When connecting to remote, HTTP-based MCP servers, Anthropic relies on the
OAuth 2.1
authorization framework.
This ensures that Claude can access your protected tools securely using temporary tokens, rather than requiring you to copy and paste long-lived API keys, passwords, or configuration secrets.
View source details for citations from Stytch, Model Context Protocol, and Zuplo. Opens side panel.
+2
Here is exactly what is required and how OAuth is handled across both environments.
The Underlying OAuth Flow for MCP
The Underlying OAuth Flow for MCP
Whether you are using Claude Code or Claude Chat, the underlying architecture is identical.
The Claude application acts as the
OAuth Client
, and the remote tool you are connecting to acts as the
Resource Server
.
View source details for citation from Model Context Protocol. Opens side panel.
To build or connect to an MCP server, the OAuth integration must support this standard discovery and token exchange flow:
Initial Handshake (Discovery):
Claude attempts to connect to the MCP server without credentials. The server must reject this with a
401 Unauthorized
HTTP status and include a
WWW-Authenticate
header pointing to a Protected Resource Metadata (PRM) document.
Metadata Fetch:
Claude reads the PRM document to locate your specific Authorization Server (e.g., Okta, Auth0, Clerk, or a custom auth server).
View source details for citation from Model Context Protocol. Opens side panel.
User Consent:
Claude prompts the user to authenticate.
The user is temporarily redirected to a browser window to log in and explicitly grant permission (e.g., "Allow Claude to access your GitHub repositories").
View source details for citations from GitHub and Model Context Protocol. Opens side panel.
+1
Token Exchange:
Claude receives a temporary authorization code via a redirect, which it silently exchanges for a long-lived access token.
View source details for citation from Upstash. Opens side panel.
Access Granted:
Claude now includes this token in the header of subsequent requests to invoke tools and read data on your behalf.
View source details for citation from Model Context Protocol. Opens side panel.
MCP OAuth in Claude Code
MCP OAuth in Claude Code
Claude Code is Anthropic's command-line interface (CLI) tool for software engineering. When you connect Claude Code to a protected remote MCP server, the OAuth flow is adapted for the terminal:
Browser Handoff:
When Claude Code hits an MCP server that requires OAuth, it will generate a secure authorization link and automatically open your default desktop web browser (or output the URL to the terminal for headless environments).
Authentication:
Once you log in and approve access in the browser, the authorization server redirects the payload back to a local loopback port managed by Claude Code, handing off the authorization code.
View source details for citation from Model Context Protocol. Opens side panel.
Alternative (Static Headers):
If setting up a full OAuth 2.1 server is too complex for a quick internal tool, Claude Code also allows you to bypass the browser flow by injecting static bearer tokens directly via the CLI.
You can do this using the
--header
flag (e.g.,
claude mcp add my-server --transport http --header "Authorization: Bearer your-token"
).
View source details for citation from TrueFoundry. Opens side panel.
MCP OAuth in Claude Chat (Claude Desktop & Web)
MCP OAuth in Claude Chat (Claude Desktop & Web)
"Claude Chat" encompasses the visual interfaces—both the Claude Desktop application and the Claude.ai web platform.
Claude.ai (Web Custom Connectors):
When you add a remote MCP server via the "Custom Connectors" menu in your settings, Claude.ai handles the OAuth flow entirely in the browser. You simply paste the MCP server URL. Claude automatically parses the 401 discovery response and redirects you to the third-party service to grant permission, before seamlessly dropping you back into your chat session.
Claude Desktop:
Claude Desktop traditionally relies on a local configuration file (
claude_desktop_config.json
). While local stdio servers often require pasting API keys into this file, remote HTTP MCP servers utilizing OAuth eliminate this security risk.
When Claude Desktop encounters an OAuth-protected server, it pops open your system's default browser to capture your consent.
It then stores the resulting token securely in your operating system's native keychain or credential manager, keeping your
config.json
free of sensitive plaintext passwords.
View source details for citation from GitHub. Opens side panel.
The Bottom Line:
You do not need to build a bespoke authentication protocol for MCP.
If your external tool already utilizes standard OpenID Connect or OAuth 2.0/2.1, Claude Code and Claude Chat are heavily standardized to automatically discover those endpoints, route the user for consent, and manage the resulting tokens behind the scenes.
View source details for citation from GitHub. Opens side panel.
Sources
Sources
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Product Strategy 2025+ Change emoji
Product Strategy 2025+
Product Strategy 2025+
Competitive analysis Change emoji
Competitive analysis
Competitive analysis
Ways of working - Product processes Change emoji
Ways of working - Product processes
Ways of working - Product processes
Core Product Performance Metrics 📈
Core Product Performance Metrics
📈
Core Product Performance Metrics
Product Documentation Change emoji
Product Documentation
Product Documentation
Product Briefs Change emoji
Product Briefs
Product Briefs
Change emoji Activity Export
Activity Export
Change emoji Autologging activities
Autologging activities
Deal Insights - Multiple currencies
Deal Insights - Multiple currencies
Reinvent Themes & Topics (and ACS)
Reinvent Themes & Topics (and ACS)
Change emoji Billing Portal
Billing Portal
Change emoji Upload Video/Audio Recordings
Upload Video/Audio Recordings
White-Label Jiminny Instance
White-Label Jiminny Instance
Win/Loss Analysis for a Deal
Win/Loss Analysis for a Deal
Change emoji Hubspot app
Hubspot app
Change emoji Automatically hard delete data for churned customers
Automatically hard delete data for churned customers
Change emoji Ask Jiminny Anything on Call level
Ask Jiminny Anything on Call level
Change emoji Ask Jiminny for Open and Closed Deals
Ask Jiminny for Open and Closed Deals
Change emoji Ask Jiminny Anything on Deal level
Ask Jiminny Anything on Deal level
Change emoji Automatically record all calendar meetings
Automatically record all calendar meetings
Change emoji Product Tiering
Product Tiering
Change emoji Recording Consent
Recording Consent
Change emoji Automated CRM Filling
Automated CRM Filling
Change emoji Automated Exec Reports
Automated Exec Reports
Change emoji Auto-detect Activity Type
Auto-detect Activity Type
AI Signals & Alerts
AI Signals & Alerts
Change emoji AJA on Anything
AJA on Anything
Change emoji AI Call Scoring
AI Call Scoring
Jiminny MCP Connector
Jiminny MCP Connector
Desktop app to record a meetings without visible Notetaker
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Jiminny MCP Connector
Updated 10m ago
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
Read time 12 min
12 min
Views 14
14
Add a reaction
Add a reaction
Epic
Link to Epik in Jira
Document status
DRAFT
Epic
Document status
Link to Epik in Jira
DRAFT
Change emoji Objective
Objective
Change emoji Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a
data layer for AI-driven revenue workflows
, not just a standalone product.
👤 Target user
👤 Target user
👤 Target user
Revenue teams using AI tools (Sales,
CS
, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
🤕
🤕
Pain point or problem
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
💥
💥
Impact and benefits
💥 Impact and benefits
For customers
For customers
For customers...
|
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benefits","depth":15,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXLink","text":"💥","depth":16,"help_text":"https://emojiterra.com/collision/","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"💥","depth":17,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Impact and benefits","depth":16,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"💥 Impact and benefits","depth":16,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXHeading","text":"For customers","depth":15,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"For customers","depth":17,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"For customers","depth":16,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false}]...
|
-5030661048126260970
|
9123641410228548343
|
click
|
accessibility
|
NULL
|
Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
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You said what is the oauth required for mcp for clauide code and claude chat
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what is the oauth required for mcp for clauide code and claude chat
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Gemini said
Gemini said
To understand the OAuth requirements for the Model Context Protocol (MCP) in Claude Code and Claude Chat, it helps to look at how MCP handles secure connections.
MCP acts as a universal "USB port" that allows AI models to connect to external data sources and tools.
When connecting to remote, HTTP-based MCP servers, Anthropic relies on the
OAuth 2.1
authorization framework.
This ensures that Claude can access your protected tools securely using temporary tokens, rather than requiring you to copy and paste long-lived API keys, passwords, or configuration secrets.
View source details for citations from Stytch, Model Context Protocol, and Zuplo. Opens side panel.
+2
Here is exactly what is required and how OAuth is handled across both environments.
The Underlying OAuth Flow for MCP
The Underlying OAuth Flow for MCP
Whether you are using Claude Code or Claude Chat, the underlying architecture is identical.
The Claude application acts as the
OAuth Client
, and the remote tool you are connecting to acts as the
Resource Server
.
View source details for citation from Model Context Protocol. Opens side panel.
To build or connect to an MCP server, the OAuth integration must support this standard discovery and token exchange flow:
Initial Handshake (Discovery):
Claude attempts to connect to the MCP server without credentials. The server must reject this with a
401 Unauthorized
HTTP status and include a
WWW-Authenticate
header pointing to a Protected Resource Metadata (PRM) document.
Metadata Fetch:
Claude reads the PRM document to locate your specific Authorization Server (e.g., Okta, Auth0, Clerk, or a custom auth server).
View source details for citation from Model Context Protocol. Opens side panel.
User Consent:
Claude prompts the user to authenticate.
The user is temporarily redirected to a browser window to log in and explicitly grant permission (e.g., "Allow Claude to access your GitHub repositories").
View source details for citations from GitHub and Model Context Protocol. Opens side panel.
+1
Token Exchange:
Claude receives a temporary authorization code via a redirect, which it silently exchanges for a long-lived access token.
View source details for citation from Upstash. Opens side panel.
Access Granted:
Claude now includes this token in the header of subsequent requests to invoke tools and read data on your behalf.
View source details for citation from Model Context Protocol. Opens side panel.
MCP OAuth in Claude Code
MCP OAuth in Claude Code
Claude Code is Anthropic's command-line interface (CLI) tool for software engineering. When you connect Claude Code to a protected remote MCP server, the OAuth flow is adapted for the terminal:
Browser Handoff:
When Claude Code hits an MCP server that requires OAuth, it will generate a secure authorization link and automatically open your default desktop web browser (or output the URL to the terminal for headless environments).
Authentication:
Once you log in and approve access in the browser, the authorization server redirects the payload back to a local loopback port managed by Claude Code, handing off the authorization code.
View source details for citation from Model Context Protocol. Opens side panel.
Alternative (Static Headers):
If setting up a full OAuth 2.1 server is too complex for a quick internal tool, Claude Code also allows you to bypass the browser flow by injecting static bearer tokens directly via the CLI.
You can do this using the
--header
flag (e.g.,
claude mcp add my-server --transport http --header "Authorization: Bearer your-token"
).
View source details for citation from TrueFoundry. Opens side panel.
MCP OAuth in Claude Chat (Claude Desktop & Web)
MCP OAuth in Claude Chat (Claude Desktop & Web)
"Claude Chat" encompasses the visual interfaces—both the Claude Desktop application and the Claude.ai web platform.
Claude.ai (Web Custom Connectors):
When you add a remote MCP server via the "Custom Connectors" menu in your settings, Claude.ai handles the OAuth flow entirely in the browser. You simply paste the MCP server URL. Claude automatically parses the 401 discovery response and redirects you to the third-party service to grant permission, before seamlessly dropping you back into your chat session.
Claude Desktop:
Claude Desktop traditionally relies on a local configuration file (
claude_desktop_config.json
). While local stdio servers often require pasting API keys into this file, remote HTTP MCP servers utilizing OAuth eliminate this security risk.
When Claude Desktop encounters an OAuth-protected server, it pops open your system's default browser to capture your consent.
It then stores the resulting token securely in your operating system's native keychain or credential manager, keeping your
config.json
free of sensitive plaintext passwords.
View source details for citation from GitHub. Opens side panel.
The Bottom Line:
You do not need to build a bespoke authentication protocol for MCP.
If your external tool already utilizes standard OpenID Connect or OAuth 2.0/2.1, Claude Code and Claude Chat are heavily standardized to automatically discover those endpoints, route the user for consent, and manage the resulting tokens behind the scenes.
View source details for citation from GitHub. Opens side panel.
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Ways of working - Product processes Change emoji
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Core Product Performance Metrics 📈
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Product Documentation Change emoji
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Deal Insights - Multiple currencies
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Reinvent Themes & Topics (and ACS)
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Ask Jiminny for Open and Closed Deals
Change emoji Ask Jiminny Anything on Deal level
Ask Jiminny Anything on Deal level
Change emoji Automatically record all calendar meetings
Automatically record all calendar meetings
Change emoji Product Tiering
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Change emoji Recording Consent
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Jiminny MCP Connector
Updated 10m ago
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
Read time 12 min
12 min
Views 14
14
Add a reaction
Add a reaction
Epic
Link to Epik in Jira
Document status
DRAFT
Epic
Document status
Link to Epik in Jira
DRAFT
Change emoji Objective
Objective
Change emoji Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a
data layer for AI-driven revenue workflows
, not just a standalone product.
👤 Target user
👤 Target user
👤 Target user
Revenue teams using AI tools (Sales,
CS
, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
🤕
🤕
Pain point or problem
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
💥
💥
Impact and benefits
💥 Impact and benefits
For customers
For customers
For customers...
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71966
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1738
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8
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2026-04-22T14:35:41.121554+00:00
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/Users/lukas/.screenpipe/data/data/2026-04-22/1776 /Users/lukas/.screenpipe/data/data/2026-04-22/1776868541121_m1.jpg...
|
Firefox
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Jiminny MCP Connector - Product - Confluence — Wor Jiminny MCP Connector - Product - Confluence — Work...
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True
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jiminny.atlassian.net/wiki/spaces/PROD/pages/37282 jiminny.atlassian.net/wiki/spaces/PROD/pages/3728244737/Jiminny+MCP+Connector...
|
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Jiminny MCP Connector - Product - Confluence
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You said what is the oauth required for mcp for clauide code and claude chat
You said
what is the oauth required for mcp for clauide code and claude chat
Listen
Show more options
Show thinking
Gemini said
Gemini said
To understand the OAuth requirements for the Model Context Protocol (MCP) in Claude Code and Claude Chat, it helps to look at how MCP handles secure connections.
MCP acts as a universal "USB port" that allows AI models to connect to external data sources and tools.
When connecting to remote, HTTP-based MCP servers, Anthropic relies on the
OAuth 2.1
authorization framework.
This ensures that Claude can access your protected tools securely using temporary tokens, rather than requiring you to copy and paste long-lived API keys, passwords, or configuration secrets.
View source details for citations from Stytch, Model Context Protocol, and Zuplo. Opens side panel.
+2
Here is exactly what is required and how OAuth is handled across both environments.
The Underlying OAuth Flow for MCP
The Underlying OAuth Flow for MCP
Whether you are using Claude Code or Claude Chat, the underlying architecture is identical.
The Claude application acts as the
OAuth Client
, and the remote tool you are connecting to acts as the
Resource Server
.
View source details for citation from Model Context Protocol. Opens side panel.
To build or connect to an MCP server, the OAuth integration must support this standard discovery and token exchange flow:
Initial Handshake (Discovery):
Claude attempts to connect to the MCP server without credentials. The server must reject this with a
401 Unauthorized
HTTP status and include a
WWW-Authenticate
header pointing to a Protected Resource Metadata (PRM) document.
Metadata Fetch:
Claude reads the PRM document to locate your specific Authorization Server (e.g., Okta, Auth0, Clerk, or a custom auth server).
View source details for citation from Model Context Protocol. Opens side panel.
User Consent:
Claude prompts the user to authenticate.
The user is temporarily redirected to a browser window to log in and explicitly grant permission (e.g., "Allow Claude to access your GitHub repositories").
View source details for citations from GitHub and Model Context Protocol. Opens side panel.
+1
Token Exchange:
Claude receives a temporary authorization code via a redirect, which it silently exchanges for a long-lived access token.
View source details for citation from Upstash. Opens side panel.
Access Granted:
Claude now includes this token in the header of subsequent requests to invoke tools and read data on your behalf.
View source details for citation from Model Context Protocol. Opens side panel.
MCP OAuth in Claude Code
MCP OAuth in Claude Code
Claude Code is Anthropic's command-line interface (CLI) tool for software engineering. When you connect Claude Code to a protected remote MCP server, the OAuth flow is adapted for the terminal:
Browser Handoff:
When Claude Code hits an MCP server that requires OAuth, it will generate a secure authorization link and automatically open your default desktop web browser (or output the URL to the terminal for headless environments).
Authentication:
Once you log in and approve access in the browser, the authorization server redirects the payload back to a local loopback port managed by Claude Code, handing off the authorization code.
View source details for citation from Model Context Protocol. Opens side panel.
Alternative (Static Headers):
If setting up a full OAuth 2.1 server is too complex for a quick internal tool, Claude Code also allows you to bypass the browser flow by injecting static bearer tokens directly via the CLI.
You can do this using the
--header
flag (e.g.,
claude mcp add my-server --transport http --header "Authorization: Bearer your-token"
).
View source details for citation from TrueFoundry. Opens side panel.
MCP OAuth in Claude Chat (Claude Desktop & Web)
MCP OAuth in Claude Chat (Claude Desktop & Web)
"Claude Chat" encompasses the visual interfaces—both the Claude Desktop application and the Claude.ai web platform.
Claude.ai (Web Custom Connectors):
When you add a remote MCP server via the "Custom Connectors" menu in your settings, Claude.ai handles the OAuth flow entirely in the browser. You simply paste the MCP server URL. Claude automatically parses the 401 discovery response and redirects you to the third-party service to grant permission, before seamlessly dropping you back into your chat session.
Claude Desktop:
Claude Desktop traditionally relies on a local configuration file (
claude_desktop_config.json
). While local stdio servers often require pasting API keys into this file, remote HTTP MCP servers utilizing OAuth eliminate this security risk.
When Claude Desktop encounters an OAuth-protected server, it pops open your system's default browser to capture your consent.
It then stores the resulting token securely in your operating system's native keychain or credential manager, keeping your
config.json
free of sensitive plaintext passwords.
View source details for citation from GitHub. Opens side panel.
The Bottom Line:
You do not need to build a bespoke authentication protocol for MCP.
If your external tool already utilizes standard OpenID Connect or OAuth 2.0/2.1, Claude Code and Claude Chat are heavily standardized to automatically discover those endpoints, route the user for consent, and manage the resulting tokens behind the scenes.
View source details for citation from GitHub. Opens side panel.
Sources
Sources
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Product Strategy 2025+ Change emoji
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Competitive analysis Change emoji
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Ways of working - Product processes Change emoji
Ways of working - Product processes
Ways of working - Product processes
Core Product Performance Metrics 📈
Core Product Performance Metrics
📈
Core Product Performance Metrics
Product Documentation Change emoji
Product Documentation
Product Documentation
Product Briefs Change emoji
Product Briefs
Product Briefs
Change emoji Activity Export
Activity Export
Change emoji Autologging activities
Autologging activities
Deal Insights - Multiple currencies
Deal Insights - Multiple currencies
Reinvent Themes & Topics (and ACS)
Reinvent Themes & Topics (and ACS)
Change emoji Billing Portal
Billing Portal
Change emoji Upload Video/Audio Recordings
Upload Video/Audio Recordings
White-Label Jiminny Instance
White-Label Jiminny Instance
Win/Loss Analysis for a Deal
Win/Loss Analysis for a Deal
Change emoji Hubspot app
Hubspot app
Change emoji Automatically hard delete data for churned customers
Automatically hard delete data for churned customers
Change emoji Ask Jiminny Anything on Call level
Ask Jiminny Anything on Call level
Change emoji Ask Jiminny for Open and Closed Deals
Ask Jiminny for Open and Closed Deals
Change emoji Ask Jiminny Anything on Deal level
Ask Jiminny Anything on Deal level
Change emoji Automatically record all calendar meetings
Automatically record all calendar meetings
Change emoji Product Tiering
Product Tiering
Change emoji Recording Consent
Recording Consent
Change emoji Automated CRM Filling
Automated CRM Filling
Change emoji Automated Exec Reports
Automated Exec Reports
Change emoji Auto-detect Activity Type
Auto-detect Activity Type
AI Signals & Alerts
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Change emoji AJA on Anything
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Jiminny MCP Connector
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Desktop app to record a meetings without visible Notetaker
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Jiminny MCP Connector
Updated 10m ago
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
Read time 12 min
12 min
Views 14
14
Add a reaction
Add a reaction
Epic
Link to Epik in Jira
Document status
DRAFT
Epic
Document status
Link to Epik in Jira
DRAFT
Change emoji Objective
Objective
Change emoji Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a
data layer for AI-driven revenue workflows
, not just a standalone product.
👤 Target user
👤 Target user
👤 Target user
Revenue teams using AI tools (Sales,
CS
, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
🤕
🤕
Pain point or problem
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
💥
💥
Impact and benefits
💥 Impact and benefits
For customers
For customers
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
For Jiminny
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
💡
💡
Solution ideas
💡 Solution ideas
Build an
AI Knowledge Connector (
MCP
-based)
that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides
structured insights
(summaries, action items, scoring etc.)
Supports
deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Change emoji Success metrics
Success metrics
Change emoji Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
No sort applied to the column
Metric
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
Goal
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
Decrease the manual copying of transcripts
Metric
No sort applied to the column
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
decrease by X %
Change emoji User interaction and design
User interaction and design...
|
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
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You said what is the oauth required for mcp for clauide code and claude chat
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what is the oauth required for mcp for clauide code and claude chat
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To understand the OAuth requirements for the Model Context Protocol (MCP) in Claude Code and Claude Chat, it helps to look at how MCP handles secure connections.
MCP acts as a universal "USB port" that allows AI models to connect to external data sources and tools.
When connecting to remote, HTTP-based MCP servers, Anthropic relies on the
OAuth 2.1
authorization framework.
This ensures that Claude can access your protected tools securely using temporary tokens, rather than requiring you to copy and paste long-lived API keys, passwords, or configuration secrets.
View source details for citations from Stytch, Model Context Protocol, and Zuplo. Opens side panel.
+2
Here is exactly what is required and how OAuth is handled across both environments.
The Underlying OAuth Flow for MCP
The Underlying OAuth Flow for MCP
Whether you are using Claude Code or Claude Chat, the underlying architecture is identical.
The Claude application acts as the
OAuth Client
, and the remote tool you are connecting to acts as the
Resource Server
.
View source details for citation from Model Context Protocol. Opens side panel.
To build or connect to an MCP server, the OAuth integration must support this standard discovery and token exchange flow:
Initial Handshake (Discovery):
Claude attempts to connect to the MCP server without credentials. The server must reject this with a
401 Unauthorized
HTTP status and include a
WWW-Authenticate
header pointing to a Protected Resource Metadata (PRM) document.
Metadata Fetch:
Claude reads the PRM document to locate your specific Authorization Server (e.g., Okta, Auth0, Clerk, or a custom auth server).
View source details for citation from Model Context Protocol. Opens side panel.
User Consent:
Claude prompts the user to authenticate.
The user is temporarily redirected to a browser window to log in and explicitly grant permission (e.g., "Allow Claude to access your GitHub repositories").
View source details for citations from GitHub and Model Context Protocol. Opens side panel.
+1
Token Exchange:
Claude receives a temporary authorization code via a redirect, which it silently exchanges for a long-lived access token.
View source details for citation from Upstash. Opens side panel.
Access Granted:
Claude now includes this token in the header of subsequent requests to invoke tools and read data on your behalf.
View source details for citation from Model Context Protocol. Opens side panel.
MCP OAuth in Claude Code
MCP OAuth in Claude Code
Claude Code is Anthropic's command-line interface (CLI) tool for software engineering. When you connect Claude Code to a protected remote MCP server, the OAuth flow is adapted for the terminal:
Browser Handoff:
When Claude Code hits an MCP server that requires OAuth, it will generate a secure authorization link and automatically open your default desktop web browser (or output the URL to the terminal for headless environments).
Authentication:
Once you log in and approve access in the browser, the authorization server redirects the payload back to a local loopback port managed by Claude Code, handing off the authorization code.
View source details for citation from Model Context Protocol. Opens side panel.
Alternative (Static Headers):
If setting up a full OAuth 2.1 server is too complex for a quick internal tool, Claude Code also allows you to bypass the browser flow by injecting static bearer tokens directly via the CLI.
You can do this using the
--header
flag (e.g.,
claude mcp add my-server --transport http --header "Authorization: Bearer your-token"
).
View source details for citation from TrueFoundry. Opens side panel.
MCP OAuth in Claude Chat (Claude Desktop & Web)
MCP OAuth in Claude Chat (Claude Desktop & Web)
"Claude Chat" encompasses the visual interfaces—both the Claude Desktop application and the Claude.ai web platform.
Claude.ai (Web Custom Connectors):
When you add a remote MCP server via the "Custom Connectors" menu in your settings, Claude.ai handles the OAuth flow entirely in the browser. You simply paste the MCP server URL. Claude automatically parses the 401 discovery response and redirects you to the third-party service to grant permission, before seamlessly dropping you back into your chat session.
Claude Desktop:
Claude Desktop traditionally relies on a local configuration file (
claude_desktop_config.json
). While local stdio servers often require pasting API keys into this file, remote HTTP MCP servers utilizing OAuth eliminate this security risk.
When Claude Desktop encounters an OAuth-protected server, it pops open your system's default browser to capture your consent.
It then stores the resulting token securely in your operating system's native keychain or credential manager, keeping your
config.json
free of sensitive plaintext passwords.
View source details for citation from GitHub. Opens side panel.
The Bottom Line:
You do not need to build a bespoke authentication protocol for MCP.
If your external tool already utilizes standard OpenID Connect or OAuth 2.0/2.1, Claude Code and Claude Chat are heavily standardized to automatically discover those endpoints, route the user for consent, and manage the resulting tokens behind the scenes.
View source details for citation from GitHub. Opens side panel.
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
Read time 12 min
12 min
Views 14
14
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Add a reaction
Epic
Link to Epik in Jira
Document status
DRAFT
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DRAFT
Change emoji Objective
Objective
Change emoji Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a
data layer for AI-driven revenue workflows
, not just a standalone product.
👤 Target user
👤 Target user
👤 Target user
Revenue teams using AI tools (Sales,
CS
, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
🤕
🤕
Pain point or problem
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
💥
💥
Impact and benefits
💥 Impact and benefits
For customers
For customers
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
For Jiminny
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
💡
💡
Solution ideas
💡 Solution ideas
Build an
AI Knowledge Connector (
MCP
-based)
that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides
structured insights
(summaries, action items, scoring etc.)
Supports
deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Change emoji Success metrics
Success metrics
Change emoji Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
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Metric
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Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
Goal
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
Decrease the manual copying of transcripts
Metric
No sort applied to the column
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
decrease by X %
Change emoji User interaction and design
User interaction and design...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
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You said what is the oauth required for mcp for clauide code and claude chat
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what is the oauth required for mcp for clauide code and claude chat
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Gemini said
Gemini said
To understand the OAuth requirements for the Model Context Protocol (MCP) in Claude Code and Claude Chat, it helps to look at how MCP handles secure connections.
MCP acts as a universal "USB port" that allows AI models to connect to external data sources and tools.
When connecting to remote, HTTP-based MCP servers, Anthropic relies on the
OAuth 2.1
authorization framework.
This ensures that Claude can access your protected tools securely using temporary tokens, rather than requiring you to copy and paste long-lived API keys, passwords, or configuration secrets.
View source details for citations from Stytch, Model Context Protocol, and Zuplo. Opens side panel.
+2
Here is exactly what is required and how OAuth is handled across both environments.
The Underlying OAuth Flow for MCP
The Underlying OAuth Flow for MCP
Whether you are using Claude Code or Claude Chat, the underlying architecture is identical.
The Claude application acts as the
OAuth Client
, and the remote tool you are connecting to acts as the
Resource Server
.
View source details for citation from Model Context Protocol. Opens side panel.
To build or connect to an MCP server, the OAuth integration must support this standard discovery and token exchange flow:
Initial Handshake (Discovery):
Claude attempts to connect to the MCP server without credentials. The server must reject this with a
401 Unauthorized
HTTP status and include a
WWW-Authenticate
header pointing to a Protected Resource Metadata (PRM) document.
Metadata Fetch:
Claude reads the PRM document to locate your specific Authorization Server (e.g., Okta, Auth0, Clerk, or a custom auth server).
View source details for citation from Model Context Protocol. Opens side panel.
User Consent:
Claude prompts the user to authenticate.
The user is temporarily redirected to a browser window to log in and explicitly grant permission (e.g., "Allow Claude to access your GitHub repositories").
View source details for citations from GitHub and Model Context Protocol. Opens side panel.
+1
Token Exchange:
Claude receives a temporary authorization code via a redirect, which it silently exchanges for a long-lived access token.
View source details for citation from Upstash. Opens side panel.
Access Granted:
Claude now includes this token in the header of subsequent requests to invoke tools and read data on your behalf.
View source details for citation from Model Context Protocol. Opens side panel.
MCP OAuth in Claude Code
MCP OAuth in Claude Code
Claude Code is Anthropic's command-line interface (CLI) tool for software engineering. When you connect Claude Code to a protected remote MCP server, the OAuth flow is adapted for the terminal:
Browser Handoff:
When Claude Code hits an MCP server that requires OAuth, it will generate a secure authorization link and automatically open your default desktop web browser (or output the URL to the terminal for headless environments).
Authentication:
Once you log in and approve access in the browser, the authorization server redirects the payload back to a local loopback port managed by Claude Code, handing off the authorization code.
View source details for citation from Model Context Protocol. Opens side panel.
Alternative (Static Headers):
If setting up a full OAuth 2.1 server is too complex for a quick internal tool, Claude Code also allows you to bypass the browser flow by injecting static bearer tokens directly via the CLI.
You can do this using the
--header
flag (e.g.,
claude mcp add my-server --transport http --header "Authorization: Bearer your-token"
).
View source details for citation from TrueFoundry. Opens side panel.
MCP OAuth in Claude Chat (Claude Desktop & Web)
MCP OAuth in Claude Chat (Claude Desktop & Web)
"Claude Chat" encompasses the visual interfaces—both the Claude Desktop application and the Claude.ai web platform.
Claude.ai (Web Custom Connectors):
When you add a remote MCP server via the "Custom Connectors" menu in your settings, Claude.ai handles the OAuth flow entirely in the browser. You simply paste the MCP server URL. Claude automatically parses the 401 discovery response and redirects you to the third-party service to grant permission, before seamlessly dropping you back into your chat session.
Claude Desktop:
Claude Desktop traditionally relies on a local configuration file (
claude_desktop_config.json
). While local stdio servers often require pasting API keys into this file, remote HTTP MCP servers utilizing OAuth eliminate this security risk.
When Claude Desktop encounters an OAuth-protected server, it pops open your system's default browser to capture your consent.
It then stores the resulting token securely in your operating system's native keychain or credential manager, keeping your
config.json
free of sensitive plaintext passwords.
View source details for citation from GitHub. Opens side panel.
The Bottom Line:
You do not need to build a bespoke authentication protocol for MCP.
If your external tool already utilizes standard OpenID Connect or OAuth 2.0/2.1, Claude Code and Claude Chat are heavily standardized to automatically discover those endpoints, route the user for consent, and manage the resulting tokens behind the scenes.
View source details for citation from GitHub. Opens side panel.
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Opens side panel.","depth":24,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"+2","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Here is exactly what is required and how OAuth is handled across both environments.","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"The Underlying OAuth Flow for MCP","depth":23,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The Underlying OAuth Flow for MCP","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Whether you are using Claude Code or Claude Chat, the underlying architecture is identical.","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The Claude application acts as the","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"OAuth Client","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":", and the remote tool you are connecting to acts as the","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Resource Server","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":".","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"View source details for citation from Model Context Protocol. Opens side panel.","depth":24,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"To build or connect to an MCP server, the OAuth integration must support this standard discovery and token exchange flow:","depth":24,"bounds":{"left":0.3695146,"top":0.0,"width":0.11087101,"height":0.057861134},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Initial Handshake (Discovery):","depth":26,"bounds":{"left":0.38148272,"top":0.0,"width":0.07829122,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude attempts to connect to the MCP server without credentials. The server must reject this with a","depth":26,"bounds":{"left":0.38148272,"top":0.0,"width":0.1043883,"height":0.07861133},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"401 Unauthorized","depth":27,"bounds":{"left":0.40940824,"top":0.035115723,"width":0.044714097,"height":0.014764565},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HTTP status and include a","depth":26,"bounds":{"left":0.38148272,"top":0.033918597,"width":0.09042553,"height":0.037110932},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"WWW-Authenticate","depth":27,"bounds":{"left":0.43334442,"top":0.05586592,"width":0.044714097,"height":0.014764565},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"header pointing to a Protected Resource Metadata (PRM) document.","depth":26,"bounds":{"left":0.38148272,"top":0.054668795,"width":0.09990027,"height":0.057861134},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Metadata Fetch:","depth":26,"bounds":{"left":0.38148272,"top":0.12330407,"width":0.042220745,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude reads the PRM document to locate your specific Authorization Server (e.g., Okta, Auth0, Clerk, or a custom auth server).","depth":26,"bounds":{"left":0.38148272,"top":0.12330407,"width":0.09840426,"height":0.07861133},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"View source details for citation from Model Context Protocol. Opens side panel.","depth":26,"bounds":{"left":0.45960772,"top":0.1859537,"width":0.008643617,"height":0.015961692},"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"User Consent:","depth":26,"bounds":{"left":0.38148272,"top":0.21268955,"width":0.036402926,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude prompts the user to authenticate.","depth":26,"bounds":{"left":0.38148272,"top":0.21268955,"width":0.1043883,"height":0.037110932},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The user is temporarily redirected to a browser window to log in and explicitly grant permission (e.g., \"Allow Claude to access your GitHub repositories\").","depth":26,"bounds":{"left":0.38148272,"top":0.23343974,"width":0.1043883,"height":0.09936153},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"View source details for citations from GitHub and Model Context Protocol. Opens side panel.","depth":26,"bounds":{"left":0.4175532,"top":0.31683958,"width":0.013796543,"height":0.015961692},"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"+1","depth":28,"bounds":{"left":0.4242021,"top":0.31883478,"width":0.0038231383,"height":0.012370312},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Token Exchange:","depth":26,"bounds":{"left":0.38148272,"top":0.34357542,"width":0.043716755,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude receives a temporary authorization code via a redirect, which it silently exchanges for a long-lived access token.","depth":26,"bounds":{"left":0.38148272,"top":0.34357542,"width":0.099734046,"height":0.07861133},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"View source details for citation from Upstash. Opens side panel.","depth":26,"bounds":{"left":0.44198802,"top":0.40622506,"width":0.008643617,"height":0.015961692},"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Access Granted:","depth":26,"bounds":{"left":0.38148272,"top":0.4329609,"width":0.04255319,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude now includes this token in the header of subsequent requests to invoke tools and read data on your behalf.","depth":26,"bounds":{"left":0.38148272,"top":0.4329609,"width":0.1043883,"height":0.07861133},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"View source details for citation from Model Context Protocol. Opens side panel.","depth":26,"bounds":{"left":0.4119016,"top":0.49561054,"width":0.008643617,"height":0.015961692},"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXHeading","text":"MCP OAuth in Claude Code","depth":23,"bounds":{"left":0.3695146,"top":0.5494813,"width":0.11635638,"height":0.01915403},"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"MCP OAuth in Claude Code","depth":24,"bounds":{"left":0.3695146,"top":0.5510774,"width":0.07047872,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude Code is Anthropic's command-line interface (CLI) tool for software engineering. When you connect Claude Code to a protected remote MCP server, the OAuth flow is adapted for the terminal:","depth":24,"bounds":{"left":0.3695146,"top":0.5774142,"width":0.11469415,"height":0.09936153},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Browser Handoff:","depth":26,"bounds":{"left":0.38115028,"top":0.6875499,"width":0.04537899,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"When Claude Code hits an MCP server that requires OAuth, it will generate a secure authorization link and automatically open your default desktop web browser (or output the URL to the terminal for headless environments).","depth":26,"bounds":{"left":0.38115028,"top":0.6875499,"width":0.1043883,"height":0.12011173},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Authentication:","depth":26,"bounds":{"left":0.38115028,"top":0.8184357,"width":0.040226065,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Once you log in and approve access in the browser, the authorization server redirects the payload back to a local loopback port managed by Claude Code, handing off the authorization code.","depth":26,"bounds":{"left":0.38115028,"top":0.8184357,"width":0.10472074,"height":0.12011173},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"View source details for citation from Model Context Protocol. Opens side panel.","depth":26,"bounds":{"left":0.39660904,"top":0.9225858,"width":0.008643617,"height":0.015961692},"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Alternative (Static Headers):","depth":26,"bounds":{"left":0.38115028,"top":0.9493216,"width":0.07430186,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"If setting up a full OAuth 2.1 server is too complex for a quick internal tool, Claude Code also allows you to bypass the browser flow by injecting static bearer tokens directly via the CLI.","depth":26,"bounds":{"left":0.38115028,"top":0.9493216,"width":0.10472074,"height":0.050678372},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"You can do this using the","depth":26,"bounds":{"left":0.38115028,"top":1.0,"width":0.09690824,"height":-0.032322407},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"--header","depth":27,"bounds":{"left":0.44481382,"top":1.0,"width":0.022273935,"height":-0.05426979},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"flag (e.g.,","depth":26,"bounds":{"left":0.38115028,"top":1.0,"width":0.09990027,"height":-0.05307257},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"claude mcp add my-server --transport http --header \"Authorization: Bearer your-token\"","depth":27,"bounds":{"left":0.38115028,"top":1.0,"width":0.094913565,"height":-0.075019956},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":").","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"View source details for citation from TrueFoundry. Opens side panel.","depth":26,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXHeading","text":"MCP OAuth in Claude Chat (Claude Desktop & Web)","depth":23,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"MCP OAuth in Claude Chat (Claude Desktop & Web)","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"Claude Chat\" encompasses the visual interfaces—both the Claude Desktop application and the Claude.ai web platform.","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude.ai (Web Custom Connectors):","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"When you add a remote MCP server via the \"Custom Connectors\" menu in your settings, Claude.ai handles the OAuth flow entirely in the browser. You simply paste the MCP server URL. Claude automatically parses the 401 discovery response and redirects you to the third-party service to grant permission, before seamlessly dropping you back into your chat session.","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude Desktop:","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude Desktop traditionally relies on a local configuration file (","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"claude_desktop_config.json","depth":27,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"). While local stdio servers often require pasting API keys into this file, remote HTTP MCP servers utilizing OAuth eliminate this security risk.","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"When Claude Desktop encounters an OAuth-protected server, it pops open your system's default browser to capture your consent.","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"It then stores the resulting token securely in your operating system's native keychain or credential manager, keeping your","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"config.json","depth":27,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"free of sensitive plaintext passwords.","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"View source details for citation from GitHub. Opens side panel.","depth":26,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"The Bottom Line:","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"You do not need to build a bespoke authentication protocol for MCP.","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"If your external tool already utilizes standard OpenID Connect or OAuth 2.0/2.1, Claude Code and Claude Chat are heavily standardized to automatically discover those endpoints, route the user for consent, and manage the resulting tokens behind the scenes.","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"View source details for citation from GitHub. 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Gemini is AI and can make mistakes, including about people.","depth":17,"bounds":{"left":0.3665226,"top":0.896249,"width":0.12167553,"height":0.025139665},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Your privacy & Gemini Opens in a new window","depth":17,"bounds":{"left":0.40724733,"top":0.92178774,"width":0.040226065,"height":0.012370312},"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false}]...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
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[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
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You said what is the oauth required for mcp for clauide code and claude chat
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what is the oauth required for mcp for clauide code and claude chat
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To understand the OAuth requirements for the Model Context Protocol (MCP) in Claude Code and Claude Chat, it helps to look at how MCP handles secure connections.
MCP acts as a universal "USB port" that allows AI models to connect to external data sources and tools.
When connecting to remote, HTTP-based MCP servers, Anthropic relies on the
OAuth 2.1
authorization framework.
This ensures that Claude can access your protected tools securely using temporary tokens, rather than requiring you to copy and paste long-lived API keys, passwords, or configuration secrets.
View source details for citations from Stytch, Model Context Protocol, and Zuplo. Opens side panel.
+2
Here is exactly what is required and how OAuth is handled across both environments.
The Underlying OAuth Flow for MCP
The Underlying OAuth Flow for MCP
Whether you are using Claude Code or Claude Chat, the underlying architecture is identical.
The Claude application acts as the
OAuth Client
, and the remote tool you are connecting to acts as the
Resource Server
.
View source details for citation from Model Context Protocol. Opens side panel.
To build or connect to an MCP server, the OAuth integration must support this standard discovery and token exchange flow:
Initial Handshake (Discovery):
Claude attempts to connect to the MCP server without credentials. The server must reject this with a
401 Unauthorized
HTTP status and include a
WWW-Authenticate
header pointing to a Protected Resource Metadata (PRM) document.
Metadata Fetch:
Claude reads the PRM document to locate your specific Authorization Server (e.g., Okta, Auth0, Clerk, or a custom auth server).
View source details for citation from Model Context Protocol. Opens side panel.
User Consent:
Claude prompts the user to authenticate.
The user is temporarily redirected to a browser window to log in and explicitly grant permission (e.g., "Allow Claude to access your GitHub repositories").
View source details for citations from GitHub and Model Context Protocol. Opens side panel.
+1
Token Exchange:
Claude receives a temporary authorization code via a redirect, which it silently exchanges for a long-lived access token.
View source details for citation from Upstash. Opens side panel.
Access Granted:
Claude now includes this token in the header of subsequent requests to invoke tools and read data on your behalf.
View source details for citation from Model Context Protocol. Opens side panel.
MCP OAuth in Claude Code
MCP OAuth in Claude Code
Claude Code is Anthropic's command-line interface (CLI) tool for software engineering. When you connect Claude Code to a protected remote MCP server, the OAuth flow is adapted for the terminal:
Browser Handoff:
When Claude Code hits an MCP server that requires OAuth, it will generate a secure authorization link and automatically open your default desktop web browser (or output the URL to the terminal for headless environments).
Authentication:
Once you log in and approve access in the browser, the authorization server redirects the payload back to a local loopback port managed by Claude Code, handing off the authorization code.
View source details for citation from Model Context Protocol. Opens side panel.
Alternative (Static Headers):
If setting up a full OAuth 2.1 server is too complex for a quick internal tool, Claude Code also allows you to bypass the browser flow by injecting static bearer tokens directly via the CLI.
You can do this using the
--header
flag (e.g.,
claude mcp add my-server --transport http --header "Authorization: Bearer your-token"
).
View source details for citation from TrueFoundry. Opens side panel.
MCP OAuth in Claude Chat (Claude Desktop & Web)
MCP OAuth in Claude Chat (Claude Desktop & Web)
"Claude Chat" encompasses the visual interfaces—both the Claude Desktop application and the Claude.ai web platform.
Claude.ai (Web Custom Connectors):
When you add a remote MCP server via the "Custom Connectors" menu in your settings, Claude.ai handles the OAuth flow entirely in the browser. You simply paste the MCP server URL. Claude automatically parses the 401 discovery response and redirects you to the third-party service to grant permission, before seamlessly dropping you back into your chat session.
Claude Desktop:
Claude Desktop traditionally relies on a local configuration file (
claude_desktop_config.json
). While local stdio servers often require pasting API keys into this file, remote HTTP MCP servers utilizing OAuth eliminate this security risk.
When Claude Desktop encounters an OAuth-protected server, it pops open your system's default browser to capture your consent.
It then stores the resulting token securely in your operating system's native keychain or credential manager, keeping your
config.json
free of sensitive plaintext passwords.
View source details for citation from GitHub. Opens side panel.
The Bottom Line:
You do not need to build a bespoke authentication protocol for MCP.
If your external tool already utilizes standard OpenID Connect or OAuth 2.0/2.1, Claude Code and Claude Chat are heavily standardized to automatically discover those endpoints, route the user for consent, and manage the resulting tokens behind the scenes.
View source details for citation from GitHub. Opens side panel.
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
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You said what is the oauth required for mcp for clauide code and claude chat
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what is the oauth required for mcp for clauide code and claude chat
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To understand the OAuth requirements for the Model Context Protocol (MCP) in Claude Code and Claude Chat, it helps to look at how MCP handles secure connections.
MCP acts as a universal "USB port" that allows AI models to connect to external data sources and tools.
When connecting to remote, HTTP-based MCP servers, Anthropic relies on the
OAuth 2.1
authorization framework.
This ensures that Claude can access your protected tools securely using temporary tokens, rather than requiring you to copy and paste long-lived API keys, passwords, or configuration secrets.
View source details for citations from Stytch, Model Context Protocol, and Zuplo. Opens side panel.
+2
Here is exactly what is required and how OAuth is handled across both environments.
The Underlying OAuth Flow for MCP
The Underlying OAuth Flow for MCP
Whether you are using Claude Code or Claude Chat, the underlying architecture is identical.
The Claude application acts as the
OAuth Client
, and the remote tool you are connecting to acts as the
Resource Server
.
View source details for citation from Model Context Protocol. Opens side panel.
To build or connect to an MCP server, the OAuth integration must support this standard discovery and token exchange flow:
Initial Handshake (Discovery):
Claude attempts to connect to the MCP server without credentials. The server must reject this with a
401 Unauthorized
HTTP status and include a
WWW-Authenticate
header pointing to a Protected Resource Metadata (PRM) document.
Metadata Fetch:
Claude reads the PRM document to locate your specific Authorization Server (e.g., Okta, Auth0, Clerk, or a custom auth server).
View source details for citation from Model Context Protocol. Opens side panel.
User Consent:
Claude prompts the user to authenticate.
The user is temporarily redirected to a browser window to log in and explicitly grant permission (e.g., "Allow Claude to access your GitHub repositories").
View source details for citations from GitHub and Model Context Protocol. Opens side panel.
+1
Token Exchange:
Claude receives a temporary authorization code via a redirect, which it silently exchanges for a long-lived access token.
View source details for citation from Upstash. Opens side panel.
Access Granted:
Claude now includes this token in the header of subsequent requests to invoke tools and read data on your behalf.
View source details for citation from Model Context Protocol. Opens side panel.
MCP OAuth in Claude Code
MCP OAuth in Claude Code
Claude Code is Anthropic's command-line interface (CLI) tool for software engineering. When you connect Claude Code to a protected remote MCP server, the OAuth flow is adapted for the terminal:
Browser Handoff:
When Claude Code hits an MCP server that requires OAuth, it will generate a secure authorization link and automatically open your default desktop web browser (or output the URL to the terminal for headless environments).
Authentication:
Once you log in and approve access in the browser, the authorization server redirects the payload back to a local loopback port managed by Claude Code, handing off the authorization code.
View source details for citation from Model Context Protocol. Opens side panel.
Alternative (Static Headers):
If setting up a full OAuth 2.1 server is too complex for a quick internal tool, Claude Code also allows you to bypass the browser flow by injecting static bearer tokens directly via the CLI.
You can do this using the
--header
flag (e.g.,
claude mcp add my-server --transport http --header "Authorization: Bearer your-token"
).
View source details for citation from TrueFoundry. Opens side panel.
MCP OAuth in Claude Chat (Claude Desktop & Web)
MCP OAuth in Claude Chat (Claude Desktop & Web)
"Claude Chat" encompasses the visual interfaces—both the Claude Desktop application and the Claude.ai web platform.
Claude.ai (Web Custom Connectors):
When you add a remote MCP server via the "Custom Connectors" menu in your settings, Claude.ai handles the OAuth flow entirely in the browser. You simply paste the MCP server URL. Claude automatically parses the 401 discovery response and redirects you to the third-party service to grant permission, before seamlessly dropping you back into your chat session.
Claude Desktop:
Claude Desktop traditionally relies on a local configuration file (
claude_desktop_config.json
). While local stdio servers often require pasting API keys into this file, remote HTTP MCP servers utilizing OAuth eliminate this security risk.
When Claude Desktop encounters an OAuth-protected server, it pops open your system's default browser to capture your consent.
It then stores the resulting token securely in your operating system's native keychain or credential manager, keeping your
config.json
free of sensitive plaintext passwords.
View source details for citation from GitHub. Opens side panel.
The Bottom Line:
You do not need to build a bespoke authentication protocol for MCP.
If your external tool already utilizes standard OpenID Connect or OAuth 2.0/2.1, Claude Code and Claude Chat are heavily standardized to automatically discover those endpoints, route the user for consent, and manage the resulting tokens behind the scenes.
View source details for citation from GitHub. Opens side panel.
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tools.","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"When connecting to remote, HTTP-based MCP servers, Anthropic relies on the","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"OAuth 2.1","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"authorization framework.","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"This ensures that Claude can access your protected tools securely using temporary tokens, rather than requiring you to copy and paste long-lived API keys, passwords, or configuration secrets.","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"View source details for citations from Stytch, Model Context Protocol, and Zuplo. Opens side panel.","depth":24,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"+2","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Here is exactly what is required and how OAuth is handled across both environments.","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"The Underlying OAuth Flow for MCP","depth":23,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The Underlying OAuth Flow for MCP","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Whether you are using Claude Code or Claude Chat, the underlying architecture is identical.","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The Claude application acts as the","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"OAuth Client","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":", and the remote tool you are connecting to acts as the","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Resource Server","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":".","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"View source details for citation from Model Context Protocol. Opens side panel.","depth":24,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"To build or connect to an MCP server, the OAuth integration must support this standard discovery and token exchange flow:","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Initial Handshake (Discovery):","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude attempts to connect to the MCP server without credentials. The server must reject this with a","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"401 Unauthorized","depth":27,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HTTP status and include a","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"WWW-Authenticate","depth":27,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"header pointing to a Protected Resource Metadata (PRM) document.","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Metadata Fetch:","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude reads the PRM document to locate your specific Authorization Server (e.g., Okta, Auth0, Clerk, or a custom auth server).","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"View source details for citation from Model Context Protocol. Opens side panel.","depth":26,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"User Consent:","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude prompts the user to authenticate.","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The user is temporarily redirected to a browser window to log in and explicitly grant permission (e.g., \"Allow Claude to access your GitHub repositories\").","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"View source details for citations from GitHub and Model Context Protocol. Opens side panel.","depth":26,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"+1","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Token Exchange:","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude receives a temporary authorization code via a redirect, which it silently exchanges for a long-lived access token.","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"View source details for citation from Upstash. Opens side panel.","depth":26,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Access Granted:","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude now includes this token in the header of subsequent requests to invoke tools and read data on your behalf.","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"View source details for citation from Model Context Protocol. Opens side panel.","depth":26,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXHeading","text":"MCP OAuth in Claude Code","depth":23,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"MCP OAuth in Claude Code","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude Code is Anthropic's command-line interface (CLI) tool for software engineering. When you connect Claude Code to a protected remote MCP server, the OAuth flow is adapted for the terminal:","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Browser Handoff:","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"When Claude Code hits an MCP server that requires OAuth, it will generate a secure authorization link and automatically open your default desktop web browser (or output the URL to the terminal for headless environments).","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Authentication:","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Once you log in and approve access in the browser, the authorization server redirects the payload back to a local loopback port managed by Claude Code, handing off the authorization code.","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"View source details for citation from Model Context Protocol. Opens side panel.","depth":26,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Alternative (Static Headers):","depth":26,"bounds":{"left":0.23159721,"top":0.0,"width":0.15520833,"height":0.022777777},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"If setting up a full OAuth 2.1 server is too complex for a quick internal tool, Claude Code also allows you to bypass the browser flow by injecting static bearer tokens directly via the CLI.","depth":26,"bounds":{"left":0.23159721,"top":0.0,"width":0.21875,"height":0.13833334},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"You can do this using the","depth":26,"bounds":{"left":0.23159721,"top":0.045,"width":0.20243056,"height":0.051666666},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"--header","depth":27,"bounds":{"left":0.36458334,"top":0.075555556,"width":0.046527777,"height":0.020555556},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"flag (e.g.,","depth":26,"bounds":{"left":0.23159721,"top":0.07388889,"width":0.20868056,"height":0.051666666},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"claude mcp add my-server --transport http --header \"Authorization: Bearer your-token\"","depth":27,"bounds":{"left":0.23159721,"top":0.104444444,"width":0.19826388,"height":0.07833333},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":").","depth":26,"bounds":{"left":0.4340278,"top":0.16055556,"width":0.0069444445,"height":0.022777777},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"View source details for citation from TrueFoundry. Opens side panel.","depth":26,"bounds":{"left":0.23576389,"top":0.19,"width":0.018055556,"height":0.022222223},"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXHeading","text":"MCP OAuth in Claude Chat (Claude Desktop & Web)","depth":23,"bounds":{"left":0.20729166,"top":0.265,"width":0.24305555,"height":0.053333335},"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"MCP OAuth in Claude Chat (Claude Desktop & Web)","depth":24,"bounds":{"left":0.20729166,"top":0.26722223,"width":0.24131945,"height":0.049444444},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"Claude Chat\" encompasses the visual interfaces—both the Claude Desktop application and the Claude.ai web platform.","depth":24,"bounds":{"left":0.20729166,"top":0.33055556,"width":0.21944444,"height":0.08055556},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude.ai (Web Custom Connectors):","depth":26,"bounds":{"left":0.23159721,"top":0.4261111,"width":0.19930555,"height":0.022777777},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"When you add a remote MCP server via the \"Custom Connectors\" menu in your settings, Claude.ai handles the OAuth flow entirely in the browser. You simply paste the MCP server URL. Claude automatically parses the 401 discovery response and redirects you to the third-party service to grant permission, before seamlessly dropping you back into your chat session.","depth":26,"bounds":{"left":0.23159721,"top":0.4261111,"width":0.21875,"height":0.2827778},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude Desktop:","depth":26,"bounds":{"left":0.23159721,"top":0.7238889,"width":0.08923611,"height":0.022777777},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude Desktop traditionally relies on a local configuration file (","depth":26,"bounds":{"left":0.23159721,"top":0.7238889,"width":0.21284722,"height":0.08055556},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"claude_desktop_config.json","depth":27,"bounds":{"left":0.2579861,"top":0.78333336,"width":0.15138888,"height":0.020555556},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"). While local stdio servers often require pasting API keys into this file, remote HTTP MCP servers utilizing OAuth eliminate this security risk.","depth":26,"bounds":{"left":0.23159721,"top":0.7816667,"width":0.21736111,"height":0.13833334},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"When Claude Desktop encounters an OAuth-protected server, it pops open your system's default browser to capture your consent.","depth":26,"bounds":{"left":0.23159721,"top":0.8972222,"width":0.21076389,"height":0.10277778},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"It then stores the resulting token securely in your operating system's native keychain or credential manager, keeping your","depth":26,"bounds":{"left":0.23159721,"top":0.98388886,"width":0.21527778,"height":0.016111135},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"config.json","depth":27,"bounds":{"left":0.353125,"top":1.0,"width":0.06423611,"height":-0.07222223},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"free of sensitive plaintext passwords.","depth":26,"bounds":{"left":0.23159721,"top":1.0,"width":0.21597221,"height":-0.07055557},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"View source details for citation from GitHub. 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Gemini is AI and can make mistakes, including about people.","depth":17,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Your privacy & Gemini Opens in a new window","depth":17,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Your privacy & Gemini","depth":18,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Opens in a new window","depth":19,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Summarize page","depth":7,"bounds":{"left":0.20034721,"top":0.0,"width":0.11180556,"height":0.035555556},"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Summarize page","depth":9,"bounds":{"left":0.21215278,"top":0.0,"width":0.088194445,"height":0.02111111},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Skip to:","depth":10,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Top Bar","depth":11,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Top Bar","depth":12,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Sidebar","depth":11,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Sidebar","depth":12,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Main Content","depth":11,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Main Content","depth":12,"help_text":"","role_description":"text","subrole":"AXUnknown"}]...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
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Pull requests · jiminny/app
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[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
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Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
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You said what is the oauth required for mcp for clauide code and claude chat
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what is the oauth required for mcp for clauide code and claude chat
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Gemini said
Gemini said
To understand the OAuth requirements for the Model Context Protocol (MCP) in Claude Code and Claude Chat, it helps to look at how MCP handles secure connections.
MCP acts as a universal "USB port" that allows AI models to connect to external data sources and tools.
When connecting to remote, HTTP-based MCP servers, Anthropic relies on the
OAuth 2.1
authorization framework.
This ensures that Claude can access your protected tools securely using temporary tokens, rather than requiring you to copy and paste long-lived API keys, passwords, or configuration secrets.
View source details for citations from Stytch, Model Context Protocol, and Zuplo. Opens side panel.
+2
Here is exactly what is required and how OAuth is handled across both environments.
The Underlying OAuth Flow for MCP
The Underlying OAuth Flow for MCP
Whether you are using Claude Code or Claude Chat, the underlying architecture is identical.
The Claude application acts as the
OAuth Client
, and the remote tool you are connecting to acts as the
Resource Server
.
View source details for citation from Model Context Protocol. Opens side panel.
To build or connect to an MCP server, the OAuth integration must support this standard discovery and token exchange flow:
Initial Handshake (Discovery):
Claude attempts to connect to the MCP server without credentials. The server must reject this with a
401 Unauthorized
HTTP status and include a
WWW-Authenticate
header pointing to a Protected Resource Metadata (PRM) document.
Metadata Fetch:
Claude reads the PRM document to locate your specific Authorization Server (e.g., Okta, Auth0, Clerk, or a custom auth server).
View source details for citation from Model Context Protocol. Opens side panel.
User Consent:
Claude prompts the user to authenticate.
The user is temporarily redirected to a browser window to log in and explicitly grant permission (e.g., "Allow Claude to access your GitHub repositories").
View source details for citations from GitHub and Model Context Protocol. Opens side panel.
+1
Token Exchange:
Claude receives a temporary authorization code via a redirect, which it silently exchanges for a long-lived access token.
View source details for citation from Upstash. Opens side panel.
Access Granted:
Claude now includes this token in the header of subsequent requests to invoke tools and read data on your behalf.
View source details for citation from Model Context Protocol. Opens side panel.
MCP OAuth in Claude Code
MCP OAuth in Claude Code
Claude Code is Anthropic's command-line interface (CLI) tool for software engineering. When you connect Claude Code to a protected remote MCP server, the OAuth flow is adapted for the terminal:
Browser Handoff:
When Claude Code hits an MCP server that requires OAuth, it will generate a secure authorization link and automatically open your default desktop web browser (or output the URL to the terminal for headless environments).
Authentication:
Once you log in and approve access in the browser, the authorization server redirects the payload back to a local loopback port managed by Claude Code, handing off the authorization code.
View source details for citation from Model Context Protocol. Opens side panel.
Alternative (Static Headers):
If setting up a full OAuth 2.1 server is too complex for a quick internal tool, Claude Code also allows you to bypass the browser flow by injecting static bearer tokens directly via the CLI.
You can do this using the
--header
flag (e.g.,
claude mcp add my-server --transport http --header "Authorization: Bearer your-token"
).
View source details for citation from TrueFoundry. Opens side panel.
MCP OAuth in Claude Chat (Claude Desktop & Web)
MCP OAuth in Claude Chat (Claude Desktop & Web)
"Claude Chat" encompasses the visual interfaces—both the Claude Desktop application and the Claude.ai web platform.
Claude.ai (Web Custom Connectors):
When you add a remote MCP server via the "Custom Connectors" menu in your settings, Claude.ai handles the OAuth flow entirely in the browser. You simply paste the MCP server URL. Claude automatically parses the 401 discovery response and redirects you to the third-party service to grant permission, before seamlessly dropping you back into your chat session.
Claude Desktop:
Claude Desktop traditionally relies on a local configuration file (
claude_desktop_config.json
). While local stdio servers often require pasting API keys into this file, remote HTTP MCP servers utilizing OAuth eliminate this security risk.
When Claude Desktop encounters an OAuth-protected server, it pops open your system's default browser to capture your consent.
It then stores the resulting token securely in your operating system's native keychain or credential manager, keeping your
config.json
free of sensitive plaintext passwords.
View source details for citation from GitHub. Opens side panel.
The Bottom Line:
You do not need to build a bespoke authentication protocol for MCP.
If your external tool already utilizes standard OpenID Connect or OAuth 2.0/2.1, Claude Code and Claude Chat are heavily standardized to automatically discover those endpoints, route the user for consent, and manage the resulting tokens behind the scenes.
View source details for citation from GitHub. Opens side panel.
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
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You said what is the oauth required for mcp for clauide code and claude chat
You said
what is the oauth required for mcp for clauide code and claude chat
Listen
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Gemini said
Gemini said
To understand the OAuth requirements for the Model Context Protocol (MCP) in Claude Code and Claude Chat, it helps to look at how MCP handles secure connections.
MCP acts as a universal "USB port" that allows AI models to connect to external data sources and tools.
When connecting to remote, HTTP-based MCP servers, Anthropic relies on the
OAuth 2.1
authorization framework.
This ensures that Claude can access your protected tools securely using temporary tokens, rather than requiring you to copy and paste long-lived API keys, passwords, or configuration secrets.
View source details for citations from Stytch, Model Context Protocol, and Zuplo. Opens side panel.
+2
Here is exactly what is required and how OAuth is handled across both environments.
The Underlying OAuth Flow for MCP
The Underlying OAuth Flow for MCP
Whether you are using Claude Code or Claude Chat, the underlying architecture is identical.
The Claude application acts as the
OAuth Client
, and the remote tool you are connecting to acts as the
Resource Server
.
View source details for citation from Model Context Protocol. Opens side panel.
To build or connect to an MCP server, the OAuth integration must support this standard discovery and token exchange flow:
Initial Handshake (Discovery):
Claude attempts to connect to the MCP server without credentials. The server must reject this with a
401 Unauthorized
HTTP status and include a
WWW-Authenticate
header pointing to a Protected Resource Metadata (PRM) document.
Metadata Fetch:
Claude reads the PRM document to locate your specific Authorization Server (e.g., Okta, Auth0, Clerk, or a custom auth server).
View source details for citation from Model Context Protocol. Opens side panel.
User Consent:
Claude prompts the user to authenticate.
The user is temporarily redirected to a browser window to log in and explicitly grant permission (e.g., "Allow Claude to access your GitHub repositories").
View source details for citations from GitHub and Model Context Protocol. Opens side panel.
+1
Token Exchange:
Claude receives a temporary authorization code via a redirect, which it silently exchanges for a long-lived access token.
View source details for citation from Upstash. Opens side panel.
Access Granted:
Claude now includes this token in the header of subsequent requests to invoke tools and read data on your behalf.
View source details for citation from Model Context Protocol. Opens side panel.
MCP OAuth in Claude Code
MCP OAuth in Claude Code
Claude Code is Anthropic's command-line interface (CLI) tool for software engineering. When you connect Claude Code to a protected remote MCP server, the OAuth flow is adapted for the terminal:
Browser Handoff:
When Claude Code hits an MCP server that requires OAuth, it will generate a secure authorization link and automatically open your default desktop web browser (or output the URL to the terminal for headless environments).
Authentication:
Once you log in and approve access in the browser, the authorization server redirects the payload back to a local loopback port managed by Claude Code, handing off the authorization code.
View source details for citation from Model Context Protocol. Opens side panel.
Alternative (Static Headers):
If setting up a full OAuth 2.1 server is too complex for a quick internal tool, Claude Code also allows you to bypass the browser flow by injecting static bearer tokens directly via the CLI.
You can do this using the
--header
flag (e.g.,
claude mcp add my-server --transport http --header "Authorization: Bearer your-token"
).
View source details for citation from TrueFoundry. Opens side panel.
MCP OAuth in Claude Chat (Claude Desktop & Web)
MCP OAuth in Claude Chat (Claude Desktop & Web)
"Claude Chat" encompasses the visual interfaces—both the Claude Desktop application and the Claude.ai web platform.
Claude.ai (Web Custom Connectors):
When you add a remote MCP server via the "Custom Connectors" menu in your settings, Claude.ai handles the OAuth flow entirely in the browser. You simply paste the MCP server URL. Claude automatically parses the 401 discovery response and redirects you to the third-party service to grant permission, before seamlessly dropping you back into your chat session.
Claude Desktop:
Claude Desktop traditionally relies on a local configuration file (
claude_desktop_config.json
). While local stdio servers often require pasting API keys into this file, remote HTTP MCP servers utilizing OAuth eliminate this security risk.
When Claude Desktop encounters an OAuth-protected server, it pops open your system's default browser to capture your consent.
It then stores the resulting token securely in your operating system's native keychain or credential manager, keeping your
config.json
free of sensitive plaintext passwords.
View source details for citation from GitHub. Opens side panel.
The Bottom Line:
You do not need to build a bespoke authentication protocol for MCP.
If your external tool already utilizes standard OpenID Connect or OAuth 2.0/2.1, Claude Code and Claude Chat are heavily standardized to automatically discover those endpoints, route the user for consent, and manage the resulting tokens behind the scenes.
View source details for citation from GitHub. Opens side panel.
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
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Jiminny
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Workers | Datadog
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Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
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Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
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Jiminny MCP Connector - Product - Confluence
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You said what is the oauth required for mcp for clauide code and claude chat
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what is the oauth required for mcp for clauide code and claude chat
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Gemini said
To understand the OAuth requirements for the Model Context Protocol (MCP) in Claude Code and Claude Chat, it helps to look at how MCP handles secure connections.
MCP acts as a universal "USB port" that allows AI models to connect to external data sources and tools.
When connecting to remote, HTTP-based MCP servers, Anthropic relies on the
OAuth 2.1
authorization framework.
This ensures that Claude can access your protected tools securely using temporary tokens, rather than requiring you to copy and paste long-lived API keys, passwords, or configuration secrets.
View source details for citations from Stytch, Model Context Protocol, and Zuplo. Opens side panel.
+2
Here is exactly what is required and how OAuth is handled across both environments.
The Underlying OAuth Flow for MCP
The Underlying OAuth Flow for MCP
Whether you are using Claude Code or Claude Chat, the underlying architecture is identical.
The Claude application acts as the
OAuth Client
, and the remote tool you are connecting to acts as the
Resource Server
.
View source details for citation from Model Context Protocol. Opens side panel.
To build or connect to an MCP server, the OAuth integration must support this standard discovery and token exchange flow:
Initial Handshake (Discovery):
Claude attempts to connect to the MCP server without credentials. The server must reject this with a
401 Unauthorized
HTTP status and include a
WWW-Authenticate
header pointing to a Protected Resource Metadata (PRM) document.
Metadata Fetch:
Claude reads the PRM document to locate your specific Authorization Server (e.g., Okta, Auth0, Clerk, or a custom auth server).
View source details for citation from Model Context Protocol. Opens side panel.
User Consent:
Claude prompts the user to authenticate.
The user is temporarily redirected to a browser window to log in and explicitly grant permission (e.g., "Allow Claude to access your GitHub repositories").
View source details for citations from GitHub and Model Context Protocol. Opens side panel.
+1
Token Exchange:
Claude receives a temporary authorization code via a redirect, which it silently exchanges for a long-lived access token.
View source details for citation from Upstash. Opens side panel.
Access Granted:
Claude now includes this token in the header of subsequent requests to invoke tools and read data on your behalf.
View source details for citation from Model Context Protocol. Opens side panel.
MCP OAuth in Claude Code
MCP OAuth in Claude Code
Claude Code is Anthropic's command-line interface (CLI) tool for software engineering. When you connect Claude Code to a protected remote MCP server, the OAuth flow is adapted for the terminal:
Browser Handoff:
When Claude Code hits an MCP server that requires OAuth, it will generate a secure authorization link and automatically open your default desktop web browser (or output the URL to the terminal for headless environments).
Authentication:
Once you log in and approve access in the browser, the authorization server redirects the payload back to a local loopback port managed by Claude Code, handing off the authorization code.
View source details for citation from Model Context Protocol. Opens side panel.
Alternative (Static Headers):
If setting up a full OAuth 2.1 server is too complex for a quick internal tool, Claude Code also allows you to bypass the browser flow by injecting static bearer tokens directly via the CLI.
You can do this using the
--header
flag (e.g.,
claude mcp add my-server --transport http --header "Authorization: Bearer your-token"
).
View source details for citation from TrueFoundry. Opens side panel.
MCP OAuth in Claude Chat (Claude Desktop & Web)
MCP OAuth in Claude Chat (Claude Desktop & Web)
"Claude Chat" encompasses the visual interfaces—both the Claude Desktop application and the Claude.ai web platform.
Claude.ai (Web Custom Connectors):
When you add a remote MCP server via the "Custom Connectors" menu in your settings, Claude.ai handles the OAuth flow entirely in the browser. You simply paste the MCP server URL. Claude automatically parses the 401 discovery response and redirects you to the third-party service to grant permission, before seamlessly dropping you back into your chat session.
Claude Desktop:
Claude Desktop traditionally relies on a local configuration file (
claude_desktop_config.json
). While local stdio servers often require pasting API keys into this file, remote HTTP MCP servers utilizing OAuth eliminate this security risk.
When Claude Desktop encounters an OAuth-protected server, it pops open your system's default browser to capture your consent.
It then stores the resulting token securely in your operating system's native keychain or credential manager, keeping your
config.json
free of sensitive plaintext passwords.
View source details for citation from GitHub. Opens side panel.
The Bottom Line:
You do not need to build a bespoke authentication protocol for MCP.
If your external tool already utilizes standard OpenID Connect or OAuth 2.0/2.1, Claude Code and Claude Chat are heavily standardized to automatically discover those endpoints, route the user for consent, and manage the resulting tokens behind the scenes.
View source details for citation from GitHub. Opens side panel.
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
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[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
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Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
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You said what is the oauth required for mcp for clauide code and claude chat
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what is the oauth required for mcp for clauide code and claude chat
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To understand the OAuth requirements for the Model Context Protocol (MCP) in Claude Code and Claude Chat, it helps to look at how MCP handles secure connections.
MCP acts as a universal "USB port" that allows AI models to connect to external data sources and tools.
When connecting to remote, HTTP-based MCP servers, Anthropic relies on the
OAuth 2.1
authorization framework.
This ensures that Claude can access your protected tools securely using temporary tokens, rather than requiring you to copy and paste long-lived API keys, passwords, or configuration secrets.
View source details for citations from Stytch, Model Context Protocol, and Zuplo. Opens side panel.
+2
Here is exactly what is required and how OAuth is handled across both environments.
The Underlying OAuth Flow for MCP
The Underlying OAuth Flow for MCP
Whether you are using Claude Code or Claude Chat, the underlying architecture is identical.
The Claude application acts as the
OAuth Client
, and the remote tool you are connecting to acts as the
Resource Server
.
View source details for citation from Model Context Protocol. Opens side panel.
To build or connect to an MCP server, the OAuth integration must support this standard discovery and token exchange flow:
Initial Handshake (Discovery):
Claude attempts to connect to the MCP server without credentials. The server must reject this with a
401 Unauthorized
HTTP status and include a
WWW-Authenticate
header pointing to a Protected Resource Metadata (PRM) document.
Metadata Fetch:
Claude reads the PRM document to locate your specific Authorization Server (e.g., Okta, Auth0, Clerk, or a custom auth server).
View source details for citation from Model Context Protocol. Opens side panel.
User Consent:
Claude prompts the user to authenticate.
The user is temporarily redirected to a browser window to log in and explicitly grant permission (e.g., "Allow Claude to access your GitHub repositories").
View source details for citations from GitHub and Model Context Protocol. Opens side panel.
+1
Token Exchange:
Claude receives a temporary authorization code via a redirect, which it silently exchanges for a long-lived access token.
View source details for citation from Upstash. Opens side panel.
Access Granted:
Claude now includes this token in the header of subsequent requests to invoke tools and read data on your behalf.
View source details for citation from Model Context Protocol. Opens side panel.
MCP OAuth in Claude Code
MCP OAuth in Claude Code
Claude Code is Anthropic's command-line interface (CLI) tool for software engineering. When you connect Claude Code to a protected remote MCP server, the OAuth flow is adapted for the terminal:
Browser Handoff:
When Claude Code hits an MCP server that requires OAuth, it will generate a secure authorization link and automatically open your default desktop web browser (or output the URL to the terminal for headless environments).
Authentication:
Once you log in and approve access in the browser, the authorization server redirects the payload back to a local loopback port managed by Claude Code, handing off the authorization code.
View source details for citation from Model Context Protocol. Opens side panel.
Alternative (Static Headers):
If setting up a full OAuth 2.1 server is too complex for a quick internal tool, Claude Code also allows you to bypass the browser flow by injecting static bearer tokens directly via the CLI.
You can do this using the
--header
flag (e.g.,
claude mcp add my-server --transport http --header "Authorization: Bearer your-token"
).
View source details for citation from TrueFoundry. Opens side panel.
MCP OAuth in Claude Chat (Claude Desktop & Web)
MCP OAuth in Claude Chat (Claude Desktop & Web)
"Claude Chat" encompasses the visual interfaces—both the Claude Desktop application and the Claude.ai web platform.
Claude.ai (Web Custom Connectors):
When you add a remote MCP server via the "Custom Connectors" menu in your settings, Claude.ai handles the OAuth flow entirely in the browser. You simply paste the MCP server URL. Claude automatically parses the 401 discovery response and redirects you to the third-party service to grant permission, before seamlessly dropping you back into your chat session.
Claude Desktop:
Claude Desktop traditionally relies on a local configuration file (
claude_desktop_config.json
). While local stdio servers often require pasting API keys into this file, remote HTTP MCP servers utilizing OAuth eliminate this security risk.
When Claude Desktop encounters an OAuth-protected server, it pops open your system's default browser to capture your consent.
It then stores the resulting token securely in your operating system's native keychain or credential manager, keeping your
config.json
free of sensitive plaintext passwords.
View source details for citation from GitHub. Opens side panel.
The Bottom Line:
You do not need to build a bespoke authentication protocol for MCP.
If your external tool already utilizes standard OpenID Connect or OAuth 2.0/2.1, Claude Code and Claude Chat are heavily standardized to automatically discover those endpoints, route the user for consent, and manage the resulting tokens behind the scenes.
View source details for citation from GitHub. Opens side panel.
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
Read time 12 min
12 min
Views 14
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Epic
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Change emoji Objective
Objective
Change emoji Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a
data layer for AI-driven revenue workflows
, not just a standalone product.
👤 Target user
👤 Target user
👤 Target user
Revenue teams using AI tools (Sales,
CS
, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
🤕
🤕
Pain point or problem
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people...
|
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
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You said what is the oauth required for mcp for clauide code and claude chat
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what is the oauth required for mcp for clauide code and claude chat
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Gemini said
To understand the OAuth requirements for the Model Context Protocol (MCP) in Claude Code and Claude Chat, it helps to look at how MCP handles secure connections.
MCP acts as a universal "USB port" that allows AI models to connect to external data sources and tools.
When connecting to remote, HTTP-based MCP servers, Anthropic relies on the
OAuth 2.1
authorization framework.
This ensures that Claude can access your protected tools securely using temporary tokens, rather than requiring you to copy and paste long-lived API keys, passwords, or configuration secrets.
View source details for citations from Stytch, Model Context Protocol, and Zuplo. Opens side panel.
+2
Here is exactly what is required and how OAuth is handled across both environments.
The Underlying OAuth Flow for MCP
The Underlying OAuth Flow for MCP
Whether you are using Claude Code or Claude Chat, the underlying architecture is identical.
The Claude application acts as the
OAuth Client
, and the remote tool you are connecting to acts as the
Resource Server
.
View source details for citation from Model Context Protocol. Opens side panel.
To build or connect to an MCP server, the OAuth integration must support this standard discovery and token exchange flow:
Initial Handshake (Discovery):
Claude attempts to connect to the MCP server without credentials. The server must reject this with a
401 Unauthorized
HTTP status and include a
WWW-Authenticate
header pointing to a Protected Resource Metadata (PRM) document.
Metadata Fetch:
Claude reads the PRM document to locate your specific Authorization Server (e.g., Okta, Auth0, Clerk, or a custom auth server).
View source details for citation from Model Context Protocol. Opens side panel.
User Consent:
Claude prompts the user to authenticate.
The user is temporarily redirected to a browser window to log in and explicitly grant permission (e.g., "Allow Claude to access your GitHub repositories").
View source details for citations from GitHub and Model Context Protocol. Opens side panel.
+1
Token Exchange:
Claude receives a temporary authorization code via a redirect, which it silently exchanges for a long-lived access token.
View source details for citation from Upstash. Opens side panel.
Access Granted:
Claude now includes this token in the header of subsequent requests to invoke tools and read data on your behalf.
View source details for citation from Model Context Protocol. Opens side panel.
MCP OAuth in Claude Code
MCP OAuth in Claude Code
Claude Code is Anthropic's command-line interface (CLI) tool for software engineering. When you connect Claude Code to a protected remote MCP server, the OAuth flow is adapted for the terminal:
Browser Handoff:
When Claude Code hits an MCP server that requires OAuth, it will generate a secure authorization link and automatically open your default desktop web browser (or output the URL to the terminal for headless environments).
Authentication:
Once you log in and approve access in the browser, the authorization server redirects the payload back to a local loopback port managed by Claude Code, handing off the authorization code.
View source details for citation from Model Context Protocol. Opens side panel.
Alternative (Static Headers):
If setting up a full OAuth 2.1 server is too complex for a quick internal tool, Claude Code also allows you to bypass the browser flow by injecting static bearer tokens directly via the CLI.
You can do this using the
--header
flag (e.g.,
claude mcp add my-server --transport http --header "Authorization: Bearer your-token"
).
View source details for citation from TrueFoundry. Opens side panel.
MCP OAuth in Claude Chat (Claude Desktop & Web)
MCP OAuth in Claude Chat (Claude Desktop & Web)
"Claude Chat" encompasses the visual interfaces—both the Claude Desktop application and the Claude.ai web platform.
Claude.ai (Web Custom Connectors):
When you add a remote MCP server via the "Custom Connectors" menu in your settings, Claude.ai handles the OAuth flow entirely in the browser. You simply paste the MCP server URL. Claude automatically parses the 401 discovery response and redirects you to the third-party service to grant permission, before seamlessly dropping you back into your chat session.
Claude Desktop:
Claude Desktop traditionally relies on a local configuration file (
claude_desktop_config.json
). While local stdio servers often require pasting API keys into this file, remote HTTP MCP servers utilizing OAuth eliminate this security risk.
When Claude Desktop encounters an OAuth-protected server, it pops open your system's default browser to capture your consent.
It then stores the resulting token securely in your operating system's native keychain or credential manager, keeping your
config.json
free of sensitive plaintext passwords.
View source details for citation from GitHub. Opens side panel.
The Bottom Line:
You do not need to build a bespoke authentication protocol for MCP.
If your external tool already utilizes standard OpenID Connect or OAuth 2.0/2.1, Claude Code and Claude Chat are heavily standardized to automatically discover those endpoints, route the user for consent, and manage the resulting tokens behind the scenes.
View source details for citation from GitHub. Opens side panel.
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
Read time 12 min
12 min
Views 14
14
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Epic
Link to Epik in Jira
Document status
DRAFT
Epic
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DRAFT
Change emoji Objective
Objective
Change emoji Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a
data layer for AI-driven revenue workflows
, not just a standalone product.
👤 Target user
👤 Target user
👤 Target user
Revenue teams using AI tools (Sales,
CS
, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
🤕
🤕
Pain point or problem
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people...
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Jiminny MCP Connector - Product - Confluence — Wor Jiminny MCP Connector - Product - Confluence — Work...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Jiminny MCP Connector - Product - Confluence
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You said what is the oauth required for mcp for clauide code and claude chat
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what is the oauth required for mcp for clauide code and claude chat
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Gemini said
To understand the OAuth requirements for the Model Context Protocol (MCP) in Claude Code and Claude Chat, it helps to look at how MCP handles secure connections.
MCP acts as a universal "USB port" that allows AI models to connect to external data sources and tools.
When connecting to remote, HTTP-based MCP servers, Anthropic relies on the
OAuth 2.1
authorization framework.
This ensures that Claude can access your protected tools securely using temporary tokens, rather than requiring you to copy and paste long-lived API keys, passwords, or configuration secrets.
View source details for citations from Stytch, Model Context Protocol, and Zuplo. Opens side panel.
+2
Here is exactly what is required and how OAuth is handled across both environments.
The Underlying OAuth Flow for MCP
The Underlying OAuth Flow for MCP
Whether you are using Claude Code or Claude Chat, the underlying architecture is identical.
The Claude application acts as the
OAuth Client
, and the remote tool you are connecting to acts as the
Resource Server
.
View source details for citation from Model Context Protocol. Opens side panel.
To build or connect to an MCP server, the OAuth integration must support this standard discovery and token exchange flow:
Initial Handshake (Discovery):
Claude attempts to connect to the MCP server without credentials. The server must reject this with a
401 Unauthorized
HTTP status and include a
WWW-Authenticate
header pointing to a Protected Resource Metadata (PRM) document.
Metadata Fetch:
Claude reads the PRM document to locate your specific Authorization Server (e.g., Okta, Auth0, Clerk, or a custom auth server).
View source details for citation from Model Context Protocol. Opens side panel.
User Consent:
Claude prompts the user to authenticate.
The user is temporarily redirected to a browser window to log in and explicitly grant permission (e.g., "Allow Claude to access your GitHub repositories").
View source details for citations from GitHub and Model Context Protocol. Opens side panel.
+1
Token Exchange:
Claude receives a temporary authorization code via a redirect, which it silently exchanges for a long-lived access token.
View source details for citation from Upstash. Opens side panel.
Access Granted:
Claude now includes this token in the header of subsequent requests to invoke tools and read data on your behalf.
View source details for citation from Model Context Protocol. Opens side panel.
MCP OAuth in Claude Code
MCP OAuth in Claude Code
Claude Code is Anthropic's command-line interface (CLI) tool for software engineering. When you connect Claude Code to a protected remote MCP server, the OAuth flow is adapted for the terminal:
Browser Handoff:
When Claude Code hits an MCP server that requires OAuth, it will generate a secure authorization link and automatically open your default desktop web browser (or output the URL to the terminal for headless environments).
Authentication:
Once you log in and approve access in the browser, the authorization server redirects the payload back to a local loopback port managed by Claude Code, handing off the authorization code.
View source details for citation from Model Context Protocol. Opens side panel.
Alternative (Static Headers):
If setting up a full OAuth 2.1 server is too complex for a quick internal tool, Claude Code also allows you to bypass the browser flow by injecting static bearer tokens directly via the CLI.
You can do this using the
--header
flag (e.g.,
claude mcp add my-server --transport http --header "Authorization: Bearer your-token"
).
View source details for citation from TrueFoundry. Opens side panel.
MCP OAuth in Claude Chat (Claude Desktop & Web)
MCP OAuth in Claude Chat (Claude Desktop & Web)
"Claude Chat" encompasses the visual interfaces—both the Claude Desktop application and the Claude.ai web platform.
Claude.ai (Web Custom Connectors):
When you add a remote MCP server via the "Custom Connectors" menu in your settings, Claude.ai handles the OAuth flow entirely in the browser. You simply paste the MCP server URL. Claude automatically parses the 401 discovery response and redirects you to the third-party service to grant permission, before seamlessly dropping you back into your chat session.
Claude Desktop:
Claude Desktop traditionally relies on a local configuration file (
claude_desktop_config.json
). While local stdio servers often require pasting API keys into this file, remote HTTP MCP servers utilizing OAuth eliminate this security risk.
When Claude Desktop encounters an OAuth-protected server, it pops open your system's default browser to capture your consent.
It then stores the resulting token securely in your operating system's native keychain or credential manager, keeping your
config.json
free of sensitive plaintext passwords.
View source details for citation from GitHub. Opens side panel.
The Bottom Line:
You do not need to build a bespoke authentication protocol for MCP.
If your external tool already utilizes standard OpenID Connect or OAuth 2.0/2.1, Claude Code and Claude Chat are heavily standardized to automatically discover those endpoints, route the user for consent, and manage the resulting tokens behind the scenes.
View source details for citation from GitHub. Opens side panel.
Sources
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Ways of working - Product processes
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
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You said what is the oauth required for mcp for clauide code and claude chat
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what is the oauth required for mcp for clauide code and claude chat
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Gemini said
To understand the OAuth requirements for the Model Context Protocol (MCP) in Claude Code and Claude Chat, it helps to look at how MCP handles secure connections.
MCP acts as a universal "USB port" that allows AI models to connect to external data sources and tools.
When connecting to remote, HTTP-based MCP servers, Anthropic relies on the
OAuth 2.1
authorization framework.
This ensures that Claude can access your protected tools securely using temporary tokens, rather than requiring you to copy and paste long-lived API keys, passwords, or configuration secrets.
View source details for citations from Stytch, Model Context Protocol, and Zuplo. Opens side panel.
+2
Here is exactly what is required and how OAuth is handled across both environments.
The Underlying OAuth Flow for MCP
The Underlying OAuth Flow for MCP
Whether you are using Claude Code or Claude Chat, the underlying architecture is identical.
The Claude application acts as the
OAuth Client
, and the remote tool you are connecting to acts as the
Resource Server
.
View source details for citation from Model Context Protocol. Opens side panel.
To build or connect to an MCP server, the OAuth integration must support this standard discovery and token exchange flow:
Initial Handshake (Discovery):
Claude attempts to connect to the MCP server without credentials. The server must reject this with a
401 Unauthorized
HTTP status and include a
WWW-Authenticate
header pointing to a Protected Resource Metadata (PRM) document.
Metadata Fetch:
Claude reads the PRM document to locate your specific Authorization Server (e.g., Okta, Auth0, Clerk, or a custom auth server).
View source details for citation from Model Context Protocol. Opens side panel.
User Consent:
Claude prompts the user to authenticate.
The user is temporarily redirected to a browser window to log in and explicitly grant permission (e.g., "Allow Claude to access your GitHub repositories").
View source details for citations from GitHub and Model Context Protocol. Opens side panel.
+1
Token Exchange:
Claude receives a temporary authorization code via a redirect, which it silently exchanges for a long-lived access token.
View source details for citation from Upstash. Opens side panel.
Access Granted:
Claude now includes this token in the header of subsequent requests to invoke tools and read data on your behalf.
View source details for citation from Model Context Protocol. Opens side panel.
MCP OAuth in Claude Code
MCP OAuth in Claude Code
Claude Code is Anthropic's command-line interface (CLI) tool for software engineering. When you connect Claude Code to a protected remote MCP server, the OAuth flow is adapted for the terminal:
Browser Handoff:
When Claude Code hits an MCP server that requires OAuth, it will generate a secure authorization link and automatically open your default desktop web browser (or output the URL to the terminal for headless environments).
Authentication:
Once you log in and approve access in the browser, the authorization server redirects the payload back to a local loopback port managed by Claude Code, handing off the authorization code.
View source details for citation from Model Context Protocol. Opens side panel.
Alternative (Static Headers):
If setting up a full OAuth 2.1 server is too complex for a quick internal tool, Claude Code also allows you to bypass the browser flow by injecting static bearer tokens directly via the CLI.
You can do this using the
--header
flag (e.g.,
claude mcp add my-server --transport http --header "Authorization: Bearer your-token"
).
View source details for citation from TrueFoundry. Opens side panel.
MCP OAuth in Claude Chat (Claude Desktop & Web)
MCP OAuth in Claude Chat (Claude Desktop & Web)
"Claude Chat" encompasses the visual interfaces—both the Claude Desktop application and the Claude.ai web platform.
Claude.ai (Web Custom Connectors):
When you add a remote MCP server via the "Custom Connectors" menu in your settings, Claude.ai handles the OAuth flow entirely in the browser. You simply paste the MCP server URL. Claude automatically parses the 401 discovery response and redirects you to the third-party service to grant permission, before seamlessly dropping you back into your chat session.
Claude Desktop:
Claude Desktop traditionally relies on a local configuration file (
claude_desktop_config.json
). While local stdio servers often require pasting API keys into this file, remote HTTP MCP servers utilizing OAuth eliminate this security risk.
When Claude Desktop encounters an OAuth-protected server, it pops open your system's default browser to capture your consent.
It then stores the resulting token securely in your operating system's native keychain or credential manager, keeping your
config.json
free of sensitive plaintext passwords.
View source details for citation from GitHub. Opens side panel.
The Bottom Line:
You do not need to build a bespoke authentication protocol for MCP.
If your external tool already utilizes standard OpenID Connect or OAuth 2.0/2.1, Claude Code and Claude Chat are heavily standardized to automatically discover those endpoints, route the user for consent, and manage the resulting tokens behind the scenes.
View source details for citation from GitHub. Opens side panel.
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SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
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[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
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You said what is the oauth required for mcp for clauide code and claude chat
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what is the oauth required for mcp for clauide code and claude chat
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Gemini said
To understand the OAuth requirements for the Model Context Protocol (MCP) in Claude Code and Claude Chat, it helps to look at how MCP handles secure connections.
MCP acts as a universal "USB port" that allows AI models to connect to external data sources and tools.
When connecting to remote, HTTP-based MCP servers, Anthropic relies on the
OAuth 2.1
authorization framework.
This ensures that Claude can access your protected tools securely using temporary tokens, rather than requiring you to copy and paste long-lived API keys, passwords, or configuration secrets.
View source details for citations from Stytch, Model Context Protocol, and Zuplo. Opens side panel.
+2
Here is exactly what is required and how OAuth is handled across both environments.
The Underlying OAuth Flow for MCP
The Underlying OAuth Flow for MCP
Whether you are using Claude Code or Claude Chat, the underlying architecture is identical.
The Claude application acts as the
OAuth Client
, and the remote tool you are connecting to acts as the
Resource Server
.
View source details for citation from Model Context Protocol. Opens side panel.
To build or connect to an MCP server, the OAuth integration must support this standard discovery and token exchange flow:
Initial Handshake (Discovery):
Claude attempts to connect to the MCP server without credentials. The server must reject this with a
401 Unauthorized
HTTP status and include a
WWW-Authenticate
header pointing to a Protected Resource Metadata (PRM) document.
Metadata Fetch:
Claude reads the PRM document to locate your specific Authorization Server (e.g., Okta, Auth0, Clerk, or a custom auth server).
View source details for citation from Model Context Protocol. Opens side panel.
User Consent:
Claude prompts the user to authenticate.
The user is temporarily redirected to a browser window to log in and explicitly grant permission (e.g., "Allow Claude to access your GitHub repositories").
View source details for citations from GitHub and Model Context Protocol. Opens side panel.
+1
Token Exchange:
Claude receives a temporary authorization code via a redirect, which it silently exchanges for a long-lived access token.
View source details for citation from Upstash. Opens side panel.
Access Granted:
Claude now includes this token in the header of subsequent requests to invoke tools and read data on your behalf.
View source details for citation from Model Context Protocol. Opens side panel.
MCP OAuth in Claude Code
MCP OAuth in Claude Code
Claude Code is Anthropic's command-line interface (CLI) tool for software engineering. When you connect Claude Code to a protected remote MCP server, the OAuth flow is adapted for the terminal:
Browser Handoff:
When Claude Code hits an MCP server that requires OAuth, it will generate a secure authorization link and automatically open your default desktop web browser (or output the URL to the terminal for headless environments).
Authentication:
Once you log in and approve access in the browser, the authorization server redirects the payload back to a local loopback port managed by Claude Code, handing off the authorization code.
View source details for citation from Model Context Protocol. Opens side panel.
Alternative (Static Headers):
If setting up a full OAuth 2.1 server is too complex for a quick internal tool, Claude Code also allows you to bypass the browser flow by injecting static bearer tokens directly via the CLI.
You can do this using the
--header
flag (e.g.,
claude mcp add my-server --transport http --header "Authorization: Bearer your-token"
).
View source details for citation from TrueFoundry. Opens side panel.
MCP OAuth in Claude Chat (Claude Desktop & Web)
MCP OAuth in Claude Chat (Claude Desktop & Web)
"Claude Chat" encompasses the visual interfaces—both the Claude Desktop application and the Claude.ai web platform.
Claude.ai (Web Custom Connectors):
When you add a remote MCP server via the "Custom Connectors" menu in your settings, Claude.ai handles the OAuth flow entirely in the browser. You simply paste the MCP server URL. Claude automatically parses the 401 discovery response and redirects you to the third-party service to grant permission, before seamlessly dropping you back into your chat session.
Claude Desktop:
Claude Desktop traditionally relies on a local configuration file (
claude_desktop_config.json
). While local stdio servers often require pasting API keys into this file, remote HTTP MCP servers utilizing OAuth eliminate this security risk.
When Claude Desktop encounters an OAuth-protected server, it pops open your system's default browser to capture your consent.
It then stores the resulting token securely in your operating system's native keychain or credential manager, keeping your
config.json
free of sensitive plaintext passwords.
View source details for citation from GitHub. Opens side panel.
The Bottom Line:
You do not need to build a bespoke authentication protocol for MCP.
If your external tool already utilizes standard OpenID Connect or OAuth 2.0/2.1, Claude Code and Claude Chat are heavily standardized to automatically discover those endpoints, route the user for consent, and manage the resulting tokens behind the scenes.
View source details for citation from GitHub. Opens side panel.
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
Read time 12 min
12 min
Views 14
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Change emoji Objective
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Change emoji Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a
data layer for AI-driven revenue workflows
, not just a standalone product.
👤 Target user
👤 Target user
👤 Target user
Revenue teams using AI tools (Sales,
CS...
|
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
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You said what is the oauth required for mcp for clauide code and claude chat
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what is the oauth required for mcp for clauide code and claude chat
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Gemini said
To understand the OAuth requirements for the Model Context Protocol (MCP) in Claude Code and Claude Chat, it helps to look at how MCP handles secure connections.
MCP acts as a universal "USB port" that allows AI models to connect to external data sources and tools.
When connecting to remote, HTTP-based MCP servers, Anthropic relies on the
OAuth 2.1
authorization framework.
This ensures that Claude can access your protected tools securely using temporary tokens, rather than requiring you to copy and paste long-lived API keys, passwords, or configuration secrets.
View source details for citations from Stytch, Model Context Protocol, and Zuplo. Opens side panel.
+2
Here is exactly what is required and how OAuth is handled across both environments.
The Underlying OAuth Flow for MCP
The Underlying OAuth Flow for MCP
Whether you are using Claude Code or Claude Chat, the underlying architecture is identical.
The Claude application acts as the
OAuth Client
, and the remote tool you are connecting to acts as the
Resource Server
.
View source details for citation from Model Context Protocol. Opens side panel.
To build or connect to an MCP server, the OAuth integration must support this standard discovery and token exchange flow:
Initial Handshake (Discovery):
Claude attempts to connect to the MCP server without credentials. The server must reject this with a
401 Unauthorized
HTTP status and include a
WWW-Authenticate
header pointing to a Protected Resource Metadata (PRM) document.
Metadata Fetch:
Claude reads the PRM document to locate your specific Authorization Server (e.g., Okta, Auth0, Clerk, or a custom auth server).
View source details for citation from Model Context Protocol. Opens side panel.
User Consent:
Claude prompts the user to authenticate.
The user is temporarily redirected to a browser window to log in and explicitly grant permission (e.g., "Allow Claude to access your GitHub repositories").
View source details for citations from GitHub and Model Context Protocol. Opens side panel.
+1
Token Exchange:
Claude receives a temporary authorization code via a redirect, which it silently exchanges for a long-lived access token.
View source details for citation from Upstash. Opens side panel.
Access Granted:
Claude now includes this token in the header of subsequent requests to invoke tools and read data on your behalf.
View source details for citation from Model Context Protocol. Opens side panel.
MCP OAuth in Claude Code
MCP OAuth in Claude Code
Claude Code is Anthropic's command-line interface (CLI) tool for software engineering. When you connect Claude Code to a protected remote MCP server, the OAuth flow is adapted for the terminal:
Browser Handoff:
When Claude Code hits an MCP server that requires OAuth, it will generate a secure authorization link and automatically open your default desktop web browser (or output the URL to the terminal for headless environments).
Authentication:
Once you log in and approve access in the browser, the authorization server redirects the payload back to a local loopback port managed by Claude Code, handing off the authorization code.
View source details for citation from Model Context Protocol. Opens side panel.
Alternative (Static Headers):
If setting up a full OAuth 2.1 server is too complex for a quick internal tool, Claude Code also allows you to bypass the browser flow by injecting static bearer tokens directly via the CLI.
You can do this using the
--header
flag (e.g.,
claude mcp add my-server --transport http --header "Authorization: Bearer your-token"
).
View source details for citation from TrueFoundry. Opens side panel.
MCP OAuth in Claude Chat (Claude Desktop & Web)
MCP OAuth in Claude Chat (Claude Desktop & Web)
"Claude Chat" encompasses the visual interfaces—both the Claude Desktop application and the Claude.ai web platform.
Claude.ai (Web Custom Connectors):
When you add a remote MCP server via the "Custom Connectors" menu in your settings, Claude.ai handles the OAuth flow entirely in the browser. You simply paste the MCP server URL. Claude automatically parses the 401 discovery response and redirects you to the third-party service to grant permission, before seamlessly dropping you back into your chat session.
Claude Desktop:
Claude Desktop traditionally relies on a local configuration file (
claude_desktop_config.json
). While local stdio servers often require pasting API keys into this file, remote HTTP MCP servers utilizing OAuth eliminate this security risk.
When Claude Desktop encounters an OAuth-protected server, it pops open your system's default browser to capture your consent.
It then stores the resulting token securely in your operating system's native keychain or credential manager, keeping your
config.json
free of sensitive plaintext passwords.
View source details for citation from GitHub. Opens side panel.
The Bottom Line:
You do not need to build a bespoke authentication protocol for MCP.
If your external tool already utilizes standard OpenID Connect or OAuth 2.0/2.1, Claude Code and Claude Chat are heavily standardized to automatically discover those endpoints, route the user for consent, and manage the resulting tokens behind the scenes.
View source details for citation from GitHub. Opens side panel.
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
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Change emoji Objective
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Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a
data layer for AI-driven revenue workflows
, not just a standalone product.
👤 Target user
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
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[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
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You said what is the oauth required for mcp for clauide code and claude chat
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what is the oauth required for mcp for clauide code and claude chat
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To understand the OAuth requirements for the Model Context Protocol (MCP) in Claude Code and Claude Chat, it helps to look at how MCP handles secure connections.
MCP acts as a universal "USB port" that allows AI models to connect to external data sources and tools.
When connecting to remote, HTTP-based MCP servers, Anthropic relies on the
OAuth 2.1
authorization framework.
This ensures that Claude can access your protected tools securely using temporary tokens, rather than requiring you to copy and paste long-lived API keys, passwords, or configuration secrets.
View source details for citations from Stytch, Model Context Protocol, and Zuplo. Opens side panel.
+2
Here is exactly what is required and how OAuth is handled across both environments.
The Underlying OAuth Flow for MCP
The Underlying OAuth Flow for MCP
Whether you are using Claude Code or Claude Chat, the underlying architecture is identical.
The Claude application acts as the
OAuth Client
, and the remote tool you are connecting to acts as the
Resource Server
.
View source details for citation from Model Context Protocol. Opens side panel.
To build or connect to an MCP server, the OAuth integration must support this standard discovery and token exchange flow:
Initial Handshake (Discovery):
Claude attempts to connect to the MCP server without credentials. The server must reject this with a
401 Unauthorized
HTTP status and include a
WWW-Authenticate
header pointing to a Protected Resource Metadata (PRM) document.
Metadata Fetch:
Claude reads the PRM document to locate your specific Authorization Server (e.g., Okta, Auth0, Clerk, or a custom auth server).
View source details for citation from Model Context Protocol. Opens side panel.
User Consent:
Claude prompts the user to authenticate.
The user is temporarily redirected to a browser window to log in and explicitly grant permission (e.g., "Allow Claude to access your GitHub repositories").
View source details for citations from GitHub and Model Context Protocol. Opens side panel.
+1
Token Exchange:
Claude receives a temporary authorization code via a redirect, which it silently exchanges for a long-lived access token.
View source details for citation from Upstash. Opens side panel.
Access Granted:
Claude now includes this token in the header of subsequent requests to invoke tools and read data on your behalf.
View source details for citation from Model Context Protocol. Opens side panel.
MCP OAuth in Claude Code
MCP OAuth in Claude Code
Claude Code is Anthropic's command-line interface (CLI) tool for software engineering. When you connect Claude Code to a protected remote MCP server, the OAuth flow is adapted for the terminal:
Browser Handoff:
When Claude Code hits an MCP server that requires OAuth, it will generate a secure authorization link and automatically open your default desktop web browser (or output the URL to the terminal for headless environments).
Authentication:
Once you log in and approve access in the browser, the authorization server redirects the payload back to a local loopback port managed by Claude Code, handing off the authorization code.
View source details for citation from Model Context Protocol. Opens side panel.
Alternative (Static Headers):
If setting up a full OAuth 2.1 server is too complex for a quick internal tool, Claude Code also allows you to bypass the browser flow by injecting static bearer tokens directly via the CLI.
You can do this using the
--header
flag (e.g.,
claude mcp add my-server --transport http --header "Authorization: Bearer your-token"
).
View source details for citation from TrueFoundry. Opens side panel.
MCP OAuth in Claude Chat (Claude Desktop & Web)
MCP OAuth in Claude Chat (Claude Desktop & Web)
"Claude Chat" encompasses the visual interfaces—both the Claude Desktop application and the Claude.ai web platform.
Claude.ai (Web Custom Connectors):
When you add a remote MCP server via the "Custom Connectors" menu in your settings, Claude.ai handles the OAuth flow entirely in the browser. You simply paste the MCP server URL. Claude automatically parses the 401 discovery response and redirects you to the third-party service to grant permission, before seamlessly dropping you back into your chat session.
Claude Desktop:
Claude Desktop traditionally relies on a local configuration file (
claude_desktop_config.json
). While local stdio servers often require pasting API keys into this file, remote HTTP MCP servers utilizing OAuth eliminate this security risk.
When Claude Desktop encounters an OAuth-protected server, it pops open your system's default browser to capture your consent.
It then stores the resulting token securely in your operating system's native keychain or credential manager, keeping your
config.json
free of sensitive plaintext passwords.
View source details for citation from GitHub. Opens side panel.
The Bottom Line:
You do not need to build a bespoke authentication protocol for MCP.
If your external tool already utilizes standard OpenID Connect or OAuth 2.0/2.1, Claude Code and Claude Chat are heavily standardized to automatically discover those endpoints, route the user for consent, and manage the resulting tokens behind the scenes.
View source details for citation from GitHub. Opens side panel.
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
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[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
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Jiminny MCP Connector - Product - Confluence
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You said what is the oauth required for mcp for clauide code and claude chat
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what is the oauth required for mcp for clauide code and claude chat
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To understand the OAuth requirements for the Model Context Protocol (MCP) in Claude Code and Claude Chat, it helps to look at how MCP handles secure connections.
MCP acts as a universal "USB port" that allows AI models to connect to external data sources and tools.
When connecting to remote, HTTP-based MCP servers, Anthropic relies on the
OAuth 2.1
authorization framework.
This ensures that Claude can access your protected tools securely using temporary tokens, rather than requiring you to copy and paste long-lived API keys, passwords, or configuration secrets.
View source details for citations from Stytch, Model Context Protocol, and Zuplo. Opens side panel.
+2
Here is exactly what is required and how OAuth is handled across both environments.
The Underlying OAuth Flow for MCP
The Underlying OAuth Flow for MCP
Whether you are using Claude Code or Claude Chat, the underlying architecture is identical.
The Claude application acts as the
OAuth Client
, and the remote tool you are connecting to acts as the
Resource Server
.
View source details for citation from Model Context Protocol. Opens side panel.
To build or connect to an MCP server, the OAuth integration must support this standard discovery and token exchange flow:
Initial Handshake (Discovery):
Claude attempts to connect to the MCP server without credentials. The server must reject this with a
401 Unauthorized
HTTP status and include a
WWW-Authenticate
header pointing to a Protected Resource Metadata (PRM) document.
Metadata Fetch:
Claude reads the PRM document to locate your specific Authorization Server (e.g., Okta, Auth0, Clerk, or a custom auth server).
View source details for citation from Model Context Protocol. Opens side panel.
User Consent:
Claude prompts the user to authenticate.
The user is temporarily redirected to a browser window to log in and explicitly grant permission (e.g., "Allow Claude to access your GitHub repositories").
View source details for citations from GitHub and Model Context Protocol. Opens side panel.
+1
Token Exchange:
Claude receives a temporary authorization code via a redirect, which it silently exchanges for a long-lived access token.
View source details for citation from Upstash. Opens side panel.
Access Granted:
Claude now includes this token in the header of subsequent requests to invoke tools and read data on your behalf.
View source details for citation from Model Context Protocol. Opens side panel.
MCP OAuth in Claude Code
MCP OAuth in Claude Code
Claude Code is Anthropic's command-line interface (CLI) tool for software engineering. When you connect Claude Code to a protected remote MCP server, the OAuth flow is adapted for the terminal:
Browser Handoff:
When Claude Code hits an MCP server that requires OAuth, it will generate a secure authorization link and automatically open your default desktop web browser (or output the URL to the terminal for headless environments).
Authentication:
Once you log in and approve access in the browser, the authorization server redirects the payload back to a local loopback port managed by Claude Code, handing off the authorization code.
View source details for citation from Model Context Protocol. Opens side panel.
Alternative (Static Headers):
If setting up a full OAuth 2.1 server is too complex for a quick internal tool, Claude Code also allows you to bypass the browser flow by injecting static bearer tokens directly via the CLI.
You can do this using the
--header
flag (e.g.,
claude mcp add my-server --transport http --header "Authorization: Bearer your-token"
).
View source details for citation from TrueFoundry. Opens side panel.
MCP OAuth in Claude Chat (Claude Desktop & Web)
MCP OAuth in Claude Chat (Claude Desktop & Web)
"Claude Chat" encompasses the visual interfaces—both the Claude Desktop application and the Claude.ai web platform.
Claude.ai (Web Custom Connectors):
When you add a remote MCP server via the "Custom Connectors" menu in your settings, Claude.ai handles the OAuth flow entirely in the browser. You simply paste the MCP server URL. Claude automatically parses the 401 discovery response and redirects you to the third-party service to grant permission, before seamlessly dropping you back into your chat session.
Claude Desktop:
Claude Desktop traditionally relies on a local configuration file (
claude_desktop_config.json
). While local stdio servers often require pasting API keys into this file, remote HTTP MCP servers utilizing OAuth eliminate this security risk.
When Claude Desktop encounters an OAuth-protected server, it pops open your system's default browser to capture your consent.
It then stores the resulting token securely in your operating system's native keychain or credential manager, keeping your
config.json
free of sensitive plaintext passwords.
View source details for citation from GitHub. Opens side panel.
The Bottom Line:
You do not need to build a bespoke authentication protocol for MCP.
If your external tool already utilizes standard OpenID Connect or OAuth 2.0/2.1, Claude Code and Claude Chat are heavily standardized to automatically discover those endpoints, route the user for consent, and manage the resulting tokens behind the scenes.
View source details for citation from GitHub. Opens side panel.
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SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
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Workers | Datadog
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Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
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You said what is the oauth required for mcp for clauide code and claude chat
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what is the oauth required for mcp for clauide code and claude chat
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To understand the OAuth requirements for the Model Context Protocol (MCP) in Claude Code and Claude Chat, it helps to look at how MCP handles secure connections.
MCP acts as a universal "USB port" that allows AI models to connect to external data sources and tools.
When connecting to remote, HTTP-based MCP servers, Anthropic relies on the
OAuth 2.1
authorization framework.
This ensures that Claude can access your protected tools securely using temporary tokens, rather than requiring you to copy and paste long-lived API keys, passwords, or configuration secrets.
View source details for citations from Stytch, Model Context Protocol, and Zuplo. Opens side panel.
+2
Here is exactly what is required and how OAuth is handled across both environments.
The Underlying OAuth Flow for MCP
The Underlying OAuth Flow for MCP
Whether you are using Claude Code or Claude Chat, the underlying architecture is identical.
The Claude application acts as the
OAuth Client
, and the remote tool you are connecting to acts as the
Resource Server
.
View source details for citation from Model Context Protocol. Opens side panel.
To build or connect to an MCP server, the OAuth integration must support this standard discovery and token exchange flow:
Initial Handshake (Discovery):
Claude attempts to connect to the MCP server without credentials. The server must reject this with a
401 Unauthorized
HTTP status and include a
WWW-Authenticate
header pointing to a Protected Resource Metadata (PRM) document.
Metadata Fetch:
Claude reads the PRM document to locate your specific Authorization Server (e.g., Okta, Auth0, Clerk, or a custom auth server).
View source details for citation from Model Context Protocol. Opens side panel.
User Consent:
Claude prompts the user to authenticate.
The user is temporarily redirected to a browser window to log in and explicitly grant permission (e.g., "Allow Claude to access your GitHub repositories").
View source details for citations from GitHub and Model Context Protocol. Opens side panel.
+1
Token Exchange:
Claude receives a temporary authorization code via a redirect, which it silently exchanges for a long-lived access token.
View source details for citation from Upstash. Opens side panel.
Access Granted:
Claude now includes this token in the header of subsequent requests to invoke tools and read data on your behalf.
View source details for citation from Model Context Protocol. Opens side panel.
MCP OAuth in Claude Code
MCP OAuth in Claude Code
Claude Code is Anthropic's command-line interface (CLI) tool for software engineering. When you connect Claude Code to a protected remote MCP server, the OAuth flow is adapted for the terminal:
Browser Handoff:
When Claude Code hits an MCP server that requires OAuth, it will generate a secure authorization link and automatically open your default desktop web browser (or output the URL to the terminal for headless environments).
Authentication:
Once you log in and approve access in the browser, the authorization server redirects the payload back to a local loopback port managed by Claude Code, handing off the authorization code.
View source details for citation from Model Context Protocol. Opens side panel.
Alternative (Static Headers):
If setting up a full OAuth 2.1 server is too complex for a quick internal tool, Claude Code also allows you to bypass the browser flow by injecting static bearer tokens directly via the CLI.
You can do this using the
--header
flag (e.g.,
claude mcp add my-server --transport http --header "Authorization: Bearer your-token"
).
View source details for citation from TrueFoundry. Opens side panel.
MCP OAuth in Claude Chat (Claude Desktop & Web)
MCP OAuth in Claude Chat (Claude Desktop & Web)
"Claude Chat" encompasses the visual interfaces—both the Claude Desktop application and the Claude.ai web platform.
Claude.ai (Web Custom Connectors):
When you add a remote MCP server via the "Custom Connectors" menu in your settings, Claude.ai handles the OAuth flow entirely in the browser. You simply paste the MCP server URL. Claude automatically parses the 401 discovery response and redirects you to the third-party service to grant permission, before seamlessly dropping you back into your chat session.
Claude Desktop:
Claude Desktop traditionally relies on a local configuration file (
claude_desktop_config.json
). While local stdio servers often require pasting API keys into this file, remote HTTP MCP servers utilizing OAuth eliminate this security risk.
When Claude Desktop encounters an OAuth-protected server, it pops open your system's default browser to capture your consent.
It then stores the resulting token securely in your operating system's native keychain or credential manager, keeping your
config.json
free of sensitive plaintext passwords.
View source details for citation from GitHub. Opens side panel.
The Bottom Line:
You do not need to build a bespoke authentication protocol for MCP.
If your external tool already utilizes standard OpenID Connect or OAuth 2.0/2.1, Claude Code and Claude Chat are heavily standardized to automatically discover those endpoints, route the user for consent, and manage the resulting tokens behind the scenes.
View source details for citation from GitHub. Opens side panel.
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OAuth requirements for the Model Context Protocol (MCP) in Claude Code and Claude Chat, it helps to look at how MCP handles secure connections.","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"MCP acts as a universal \"USB port\" that allows AI models to connect to external data sources and tools.","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"When connecting to remote, HTTP-based MCP servers, Anthropic relies on the","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"OAuth 2.1","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"authorization framework.","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"This ensures that Claude can access your protected tools securely using temporary tokens, rather than requiring you to copy and paste long-lived API keys, passwords, or configuration secrets.","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"View source details for citations from Stytch, Model Context Protocol, and Zuplo. Opens side panel.","depth":24,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"+2","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Here is exactly what is required and how OAuth is handled across both environments.","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"The Underlying OAuth Flow for MCP","depth":23,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The Underlying OAuth Flow for MCP","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Whether you are using Claude Code or Claude Chat, the underlying architecture is identical.","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The Claude application acts as the","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"OAuth Client","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":", and the remote tool you are connecting to acts as the","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Resource Server","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":".","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"View source details for citation from Model Context Protocol. 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SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
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[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
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You said what is the oauth required for mcp for clauide code and claude chat
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what is the oauth required for mcp for clauide code and claude chat
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To understand the OAuth requirements for the Model Context Protocol (MCP) in Claude Code and Claude Chat, it helps to look at how MCP handles secure connections.
MCP acts as a universal "USB port" that allows AI models to connect to external data sources and tools.
When connecting to remote, HTTP-based MCP servers, Anthropic relies on the
OAuth 2.1
authorization framework.
This ensures that Claude can access your protected tools securely using temporary tokens, rather than requiring you to copy and paste long-lived API keys, passwords, or configuration secrets.
View source details for citations from Stytch, Model Context Protocol, and Zuplo. Opens side panel.
+2
Here is exactly what is required and how OAuth is handled across both environments.
The Underlying OAuth Flow for MCP
The Underlying OAuth Flow for MCP
Whether you are using Claude Code or Claude Chat, the underlying architecture is identical.
The Claude application acts as the
OAuth Client
, and the remote tool you are connecting to acts as the
Resource Server
.
View source details for citation from Model Context Protocol. Opens side panel.
To build or connect to an MCP server, the OAuth integration must support this standard discovery and token exchange flow:
Initial Handshake (Discovery):
Claude attempts to connect to the MCP server without credentials. The server must reject this with a
401 Unauthorized
HTTP status and include a
WWW-Authenticate
header pointing to a Protected Resource Metadata (PRM) document.
Metadata Fetch:
Claude reads the PRM document to locate your specific Authorization Server (e.g., Okta, Auth0, Clerk, or a custom auth server).
View source details for citation from Model Context Protocol. Opens side panel.
User Consent:
Claude prompts the user to authenticate.
The user is temporarily redirected to a browser window to log in and explicitly grant permission (e.g., "Allow Claude to access your GitHub repositories").
View source details for citations from GitHub and Model Context Protocol. Opens side panel.
+1
Token Exchange:
Claude receives a temporary authorization code via a redirect, which it silently exchanges for a long-lived access token.
View source details for citation from Upstash. Opens side panel.
Access Granted:
Claude now includes this token in the header of subsequent requests to invoke tools and read data on your behalf.
View source details for citation from Model Context Protocol. Opens side panel.
MCP OAuth in Claude Code
MCP OAuth in Claude Code
Claude Code is Anthropic's command-line interface (CLI) tool for software engineering. When you connect Claude Code to a protected remote MCP server, the OAuth flow is adapted for the terminal:
Browser Handoff:
When Claude Code hits an MCP server that requires OAuth, it will generate a secure authorization link and automatically open your default desktop web browser (or output the URL to the terminal for headless environments).
Authentication:
Once you log in and approve access in the browser, the authorization server redirects the payload back to a local loopback port managed by Claude Code, handing off the authorization code.
View source details for citation from Model Context Protocol. Opens side panel.
Alternative (Static Headers):
If setting up a full OAuth 2.1 server is too complex for a quick internal tool, Claude Code also allows you to bypass the browser flow by injecting static bearer tokens directly via the CLI.
You can do this using the
--header
flag (e.g.,
claude mcp add my-server --transport http --header "Authorization: Bearer your-token"
).
View source details for citation from TrueFoundry. Opens side panel.
MCP OAuth in Claude Chat (Claude Desktop & Web)
MCP OAuth in Claude Chat (Claude Desktop & Web)
"Claude Chat" encompasses the visual interfaces—both the Claude Desktop application and the Claude.ai web platform.
Claude.ai (Web Custom Connectors):
When you add a remote MCP server via the "Custom Connectors" menu in your settings, Claude.ai handles the OAuth flow entirely in the browser. You simply paste the MCP server URL. Claude automatically parses the 401 discovery response and redirects you to the third-party service to grant permission, before seamlessly dropping you back into your chat session.
Claude Desktop:
Claude Desktop traditionally relies on a local configuration file (
claude_desktop_config.json
). While local stdio servers often require pasting API keys into this file, remote HTTP MCP servers utilizing OAuth eliminate this security risk.
When Claude Desktop encounters an OAuth-protected server, it pops open your system's default browser to capture your consent.
It then stores the resulting token securely in your operating system's native keychain or credential manager, keeping your
config.json
free of sensitive plaintext passwords.
View source details for citation from GitHub. Opens side panel.
The Bottom Line:
You do not need to build a bespoke authentication protocol for MCP.
If your external tool already utilizes standard OpenID Connect or OAuth 2.0/2.1, Claude Code and Claude Chat are heavily standardized to automatically discover those endpoints, route the user for consent, and manage the resulting tokens behind the scenes.
View source details for citation from GitHub. Opens side panel.
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SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
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[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
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Jiminny MCP Connector
Updated 10m ago
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ActivityrireroyToolscalMistorbookmarksProtllesWindowmelpJiminny... vt3 #ai-chapter8 16MessagesAdd canvasUr Files® Channels# ai-chapter# alerts# backend# c-learning-people# confusion-clinic# curiosity_lab# deal-insights-dev# engineering# frontend# general# infra-changes# jiminny-bg8 people-with-coppilo...& people-with-zoom-...# platform-team# platform-tickets# product_launches# random# releases# sofia-office# support# thank-yous# the_people_of jimi...• Direct messages.https://modelccToday ~col.io/docs/learn/architecture?Model Context ProtocoArchitecture overview - Model ContextProtocol (21 kB) -Model Context ProtocolVasil Vasilev 5:12 PMI• LaravelLaravel MCP - Al toolling for Laravel. bv theLaravel team | Laravel - The clean stack forArhicans and agentsLaravel is a PHP web application frameworkwith expressive, elegant syntax. We'vealready laid the foundation - freeing you tocreate without sweating the small things.(2 MB) -Message #ai-chapter+ AalRenorts:tJobTest extends TeMockObject $reportsService&Mock0bject$prophetClient;ect sloggernsur lbeneracor.nockubTect sTobb1spa→ E, Side-by39 tests/Unit Alobs/Autoce Tests Unit hlohsbon\Carbon;phe logging.php configlinny\Component\Propluse Jiminny\Component\Propl© PlaybackService.php app/Services—use Jiminny\Jobs\Automatedi#report-not-generated.blade.php resources/views/emails/reports_use Jiminny\Models\Automat‹© ReportNotGenerated.php app/Mail/Reports© RequestGenerateAskJiminnyReportJob.php app/Jobs/AutomatedReports_ use Jiminny\Models |Automat:use Jiminny \Models\Team;© RequestGenerateAskJiminnyReportJobTest.php tests/Unit/Jobs/AutomatedRe— use Jiminny\Models\User;© SendReportNotGeneratedMailJob.php app/Jobs/AutomatedReportsSyncTolntercom.php app/Jobs/TeameNestsiunitvobsjautomateakeprivate AutomatedReporUnversioned Files 8 filesorzvare Ask.aminnvkedo= env.nikilocal apoprivate ProphetClientslE env.other appprivate Loqgerinterfac© CanAccessAiReportsTest.php tests/Unit/Policies© CreateMockAskJiminnyReportResultCommand.php app/Console/Commands/R=protected function set=ids.txt aodTgraw_sqL_query.sql appSthis->renortServi→ CSevenShores|Hubspot|Exceptions(UY-20372) Al Reports > Empty paZ Jiminny MCP Connector - Produc© Jiminny* Workers | DatadogPull requests • jiminny/app(JY-20728) (HubSpot) Find the roc• JY-9712 | Nuges to expire after oni9 Jiminns@ CloudWatch | us-east-2v Your 'Not enpough activities' repd* Jiminny MCP Connector - Prod X_ New Tabjiminny.atlassian.net/wiki/spaces/PROD/pages/3728244737/Jiminny+MCP+Connectorv Google GeminiGeminiPROHi LukasWhere should westart?Create image* Create musicWrite anythingBoost my dayHelp me learnIry video templates insteadof starting from scratchTry itO Enter a prongpt for GeminiFastvSummarize pageO JIMINNY$ Product> 7 Product DocunT Back to topv ? Product Briefs• Activity Export• V Autologging activities• - Deal Insiahts - Multiple currencies• E Reinvent Themes & Topics (and ACS)• V Billing Portal• • Upload Video/Audio Recordinas• = White-Label Jiminnv Instance• E Win/Loss Analysis for a Deal• • Hubspot app|• Automatically hard delete data for churned customers• V Ask Jiminny Anything on Call level |• • Ask Jiminny for Open and Closed Deals• V Ask liminny Anvthina on Deal levell• • Automatically record all calendar meetings• Product Tiering• V Recordina Consent• V Automated CRM Filling• • Automated Exec Reports• V Auto-detect Activity Tvpel• E AI Signals & Alerts• M AJA on Anything• V AI Call Scorina1 • E Jiminny MCP Connector• E Desktop app to record a meetings without visible N...> $ Feedback> 4, Research & User Feedback+ CreateSira:: Teams*** Morelf Al chapter • 24 m left100% L2Wed 22 Apr 17:36:26Q Search Confluence, Jira, Google Drive and other apps+ CreateAsk RovoE Jiminny MCP ConnectorUpdated 10m agoCAД 4(, Edit& ShareICGITI VIDININLY CHIIVIUCHICHIL DRIUUIN NE HLO UNNETIENETICYUIECIIICIIE TNE CURICIERIUIE UNACI GULEDO CUTIEUI("ensure MCP only exposes data user has access to") is doing too much work as a single line. Every MCP toolresponse (especially search_calls and deal-level aggregations) must be filtered through the same visibilityengine as the UI. Semantic search is the riskiest here; it could surface results across team boundaries if notproperiy scoped.No data minimisation or scoping controls. We're exposing transcripts, CRM fields, contact details with jobtitles, deal context - significant PII and commercial data leaving the platform. Can customers configure whichdata types are exposed? Can transcripts be PII-stripped before they leave Jiminny? Given we position GDPRconsent as a differentiator, we should be thinking about this.GDPR downstream processing is unaddressed. Piping transcripts to OpenAI/Gemini introduces new dataprocessors. Customers need to acknowledge responsibility for downstream processing during setup. We shouldalso consider whether calli particioants' recordina consent covers onward sharina to third-oartv Al - it almosticertainly doesn't by default. This is one for me to pick up.Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee'sactive kev is a data leak. Need: revocation, ontional exoirv. alerts on stale kevs. and consider whether kevs Ishould be tied to service accounts rather than personal users.Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entirecustomer's call history. Need per-key rate limits and anomaly detection on unusual access patterns.Audit logs should go deeper. Current spec (what data, which system, when) is a start. Add: identity behind thekey, specific queries/filters used, and failed access attempts (especially cross-visibility-boundary attempts).Enterprise compliance teams will expect this.Downgrade handling. Doc says Scale tier and above, but doesn't say what happens on downgrade. Keys shouldauto-deactivate.Haooy to nelo dratt a dedicated Securitv « Comoliance section it usetul..^ ReplyGalya Dimitrova 15 AprThanks for the input @James Graham . I made the product related changes in the table and for the rest I leftthem as a note in the Technical section for the team to look at when they start looking at this.I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier tofeed this data to AI and BI tools. They are also manually exporting transcripts and other information from theplatform and feeding it to AI tools. I don't get how MCP is so much different that all of these and why it willintroduce new data processors (which are already being utilised) and would require any changes to the consentprovided by participants.edited)Reply# Add a commentREQUIREMENTS...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST [URL_WITH_CREDENTIALS] Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.
Jiminny MCP Connector
Epic
Link to Epik in Jira
Document status
DRAFT
Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.
👤 Target user
Revenue teams using AI tools (Sales, CS, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
Build an AI Knowledge Connector (MCP-based) that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides structured insights (summaries, action items, scoring etc.)
Supports deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
Metric
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of MCP/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
User interaction and design
Simple setup flow:
Generate API key / connect MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus: usable by non-technical users
Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
Importance
Notes
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH
For each deal expose:
calls/meetings/emails
Identified risks
contacts related to the deal with their job title
linked account details
all CRM field information that we have in DI + fields used for CRM filling
example - get_deal_summary
request -
{
"tool": "get_deal_summary",
"arguments": {
"deal_name": "ACME"
}
}
responce:
{
"deal_name": "ACME",
"stage": "Negotiation",
"recent_activity": [
"Demo call on 2026-04-01",
"Pricing discussion on 2026-04-03"
],
"risks": [
"Pricing concerns",
"Security review pending"
], "next_steps": [
"Send follow-up to VP Sales",
"Share security documentation"
]
}
Ensure the AI can easily understand the schema of the data coming from Jiminny
HIGH
Models perform much better when tools have:
clear names
rich descriptions
predictable schemas
Provide clear, actionable error states for customers when MCP connections fail
HIGH
Error messages should guide agents toward solutions with specific suggestions and next steps.
Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”
Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”
Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”
Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”
Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”
Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”
No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”
Allow customers to securely control who and what data is accessible externally
HIGH
Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well.
Private meetings shouldn’t be exposed via MCP
HIGH
When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP.
Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows
HIGH
Ensure data is structured to be easily combined with:
CRM (accounts, deals)
external docs
Use consistent identifiers:
account IDs
deal IDs
MCP is available only for Scale tier and above
HIGH
Customers who are on Capture tier should be able to access any of their data through MCP.
This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working.
Provide visibility into how external AI tools access Jiminny data...
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learn","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXTextArea","text":"Jiminny MCP Connector\n\n\nEpic Link to Epik in JiraDocument statusDRAFT ObjectiveEnable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product. \n👤 Target userRevenue teams using AI tools (Sales, CS, RevOps)Companies already experimenting with Claude / OpenAI Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc) \n🤕 Pain point or problemJiminny data is locked in the platformAI tools lack access to high-value conversation contextCustomers must manually export/copy transcriptsNo easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales peoplePeer pressure as almost all competitors have this \n💥 Impact and benefitsFor customersBetter AI outputs using real customer conversationsAbility to build automated workflows across toolsFor JiminnyIncreased product stickiness (embedded in workflows)Competitive parity with Gong / Avoma etc. \n💡 Solution ideas Build an AI Knowledge Connector (MCP-based) that:\nExposes Jiminny data (calls, transcripts, metadata) to AI toolsProvides structured insights (summaries, action items, scoring etc.)Supports deal-level contextAllows integration into customer-owned AI stacks (Claude + Zapier, etc.) \n Success metricsList the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).Goal\n\nMetric\n\nIncrease stickiness by ensuring Jiminny is embedded in the customer’s workflows% of customers connecting external AI toolsNumber of MCP/API calls per customer - and which ones are being used the mostDecrease the manual copying of transcripts decrease by X %\n User interaction and designSimple setup flow:Generate API key / connect MCPClear instructions for Claude / Zapier setupProvide:Example prompts for ClaudePre-built workflow templates for ZapierClear documentation - with use cases👉 Focus: usable by non-technical users\n Detailed RequirementsList the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.Requirement\n\nImportance\n\nNotes\n\nAllow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)HIGHProvide MCP endpoint so customers can easily integrate Jiminny into their AI stackAllow customers to access call data through AI toolsHIGHExpose Jiminny data:\nlist calls (filter by date, user, account, deal)get callget transcript (speaker-separated)get call metadatasearch calls (keyword + semantic) Support filtering by:\naccount / contact/ lead / dealjiminny userdate rangeAllow customers to search and query calls using AI toolsHIGHSupport keyword and semantic search across conversationsProvide structured insights so customers can generate better outputsHIGHFor each call, expose:\nSummaryAction itemsKey pointsActivity typeNotesCall ScoreCRM informationAllow customers to understand deal status across multiple calls/emailsHIGHFor each deal expose:\ncalls/meetings/emailsIdentified risks contacts related to the deal with their job titlelinked account detailsall CRM field information that we have in DI + fields used for CRM fillingexample - get_deal_summaryrequest - \n{ \"tool\": \"get_deal_summary\", \"arguments\": { \"deal_name\": \"ACME\" }}responce:\n{ \"deal_name\": \"ACME\", \"stage\": \"Negotiation\", \"recent_activity\": [ \"Demo call on 2026-04-01\", \"Pricing discussion on 2026-04-03\" ], \"risks\": [ \"Pricing concerns\", \"Security review pending\" ], \"next_steps\": [ \"Send follow-up to VP Sales\", \"Share security documentation\" ]} Ensure the AI can easily understand the schema of the data coming from JiminnyHIGHModels perform much better when tools have:\nclear namesrich descriptionspredictable schemasProvide clear, actionable error states for customers when MCP connections failHIGHError messages should guide agents toward solutions with specific suggestions and next steps.\nExpired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”Allow customers to securely control who and what data is accessible externallyHIGHEnsure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well. Private meetings shouldn’t be exposed via MCPHIGHWhen a meetings is set to Private then the information about it shouldn’t be exposed through the MCP. Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflowsHIGHEnsure data is structured to be easily combined with:\nCRM (accounts, deals)external docsUse consistent identifiers:\naccount IDsdeal IDs MCP is available only for Scale tier and aboveHIGHCustomers who are on Capture tier should be able to access any of their data through MCP. This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working. Provide visibility into how external AI tools access Jiminny dataHIGHAudit logs:\nwhat data was accessed by which external system whenidentity - who made the request specific queries/filters usedfailed access attempts (especially cross-visibility-boundary attempts)Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it.example from Atlassian - Provide customers with example prompts to get started quickly HIGHMCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments. See examples in table below.Expose Jiminny as a Connector in ClaudemediumCompetitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect JiminnyProvide customers with example workflows to get started quicklymediumA workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.See examples in table below.Allow customers to use predefined Jiminny skills for common workflows mediumAnthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com See examples in table below.Jiminny UI widget for OpenAImedium \n The reason to have this is to make certain Jiminny use cases much easier than pure chat.Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.Allow customers to run long-running analyses asynchronously and retrieve progress/results lowSome requests/questions require analysis of Hugh volumes of data. For those, it may be better to:\nstart the joblet it run in the backgroundreturn a status like “in progress”let the client come back for the result later For example - User asks in Claude:\n“Analyse all calls from this quarter and tell me the top 5 objections by segment.”That might require:\nsearching lots of callspulling many summaries/transcriptsaggregating resultsInstead of making Claude wait 45+ seconds, Jiminny could:\ncreate an analysis jobreturn: job_id = 123, status = running We need this in order to support:\nlarge-scale analysismulti-call aggregation across lots of dataplaylist generation across many callsheavy recap/report generationCreating a snippet through the MCPlowCreate a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team. example request:\n{ \"tool\": \"create_snippet\", \"arguments\": { \"call_id\": \"call_123\", \"start_time\": \"00:18:24\", \"end_time\": \"00:19:10\", \"title\": \"Pricing objection\" }}example response:\n{ \"snippet_id\": \"snip_987\", \"title\": \"Pricing objection\", \"share_url\": \"https://app.jiminny.com/snippets/snip_987\"}Ability to add recordings or snippets to a playlistlowAllow customers to create libraries of calls automatically. They can use those for onboarding or other purposesAllow AI tools to read reusable Jiminny resources low fathomResources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be:\ncurrent deal snapshotaccount summaryweekly rep recapapproved talk tracks / objection handling guidancecurated playlists or snippet librariesThis is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls. Ability to write coaching notes in Jiminny through MCPlow \nWhat we want to do for prompts/worflows and skillsCapability\n\nType\n\nUser value / job to be done\nExample Jiminny use case\nInputs\n\nSteps performed\n\nOutput\n\nDraft follow-up emailPromptHelp reps quickly create tailored follow-ups after callsUse the latest customer call to draft an email with recap and next stepsCall IDoptional toneoptional recipient typeRetrieve call summary and action items from Jiminny.Pull key points, commitments, and next steps.Apply the chosen tone and structure.Draft the email in a consistent format.Customer-ready follow-up email draftSummarise callPromptHelp users quickly extract the most important points from a conversationGenerate a concise summary of one call for internal or external useCall IDoptional summary styleoptional audienceRetrieve transcript, metadata, and structured insights.Identify key topics, decisions, risks, and next steps.Format output for the chosen audience.Short or structured call summary with actions and risksGenerate deal recapPromptHelp reps and managers get a fast overview of a dealSummarise the current state of a deal based on recent calls and linked CRM contextDeal ID or account nameoptional date rangeRetrieve calls and meetings linked to the deal. Pull deal metadata and CRM-linked context. Identify recent changes, blockers, risks, and next steps.Summarise into a concise recap.Deal recap with status, blockers, risks, and recommended next stepsPrepare weekly recapPromptHelp managers or reps review what changed over a time periodWeekly recap of calls, themes, and actions for one rep, team, or accountRep IDteam ID or account IDtime periodRetrieve relevant calls and summaries for the selected time period.Aggregate themes, action items, and changes. Highlight notable risks, opportunities, and follow-ups.Weekly recap with top themes, risks, opportunities, and actionsPrepare deal reviewWorkflowHelp reps and managers review deal status using multiple sources of contextReview a deal before pipeline review or forecast meetingDeal IDoptional date rangeRetrieve all recent calls, emails, and meetings tied to the deal. Pull structured call summaries, score, action items, and key points. Pull linked CRM context such as stage, contacts, and account details.Aggregate open risks, unresolved objections, stakeholder gaps, and next steps. Format into a review-ready structure.Deal review document with current status, risks, stakeholders, blockers, and next stepsWeekly team recapWorkflowHelp managers monitor activity and risk across a group of reps or accountsEnd-of-week team summary for sales leadershipTeam IDdate rangeRetrieve calls and structured insights across the team.Group by rep, account, or deal.Identify recurring themes, major risks, strong calls, and missed follow-ups.Highlight calls or deals that need attention.Team-level recap with patterns, key risks, notable wins, and action areasObjection analysis across callsWorkflowHelp teams identify patterns across multiple conversationsAnalyse pricing or competitor objections across recent callsAccount IDdeal IDrep IDkeyword/topicdate rangeSearch calls matching the topic or keyword.Retrieve summaries, transcripts, and snippets where relevant.Identify recurring objections and how they were handled.Assess whether objections were resolved or remain open.Summarise patterns and examples.Objection analysis with themes, frequency, examples, and suggested follow-up actionsBuild onboarding playlistWorkflowHelp enablement teams curate useful learning content fasterBuild a playlist of strong discovery, demo, or pricing calls for new hiresTopiccall typerep/team filteroptional time periodSearch for calls matching the selected criteria. Rank or filter calls based on score, topics, or key moments. Identify useful snippets or full recordings. Add selected calls/snippets into a playlist structure.Generate short descriptions for each item.Curated onboarding playlist with selected calls/snippets and descriptionsDeal Inspection SkillSkillGive users a consistent, opinionated way to assess a dealClaude reviews a deal using a repeatable Jiminny methodologyDeal ID or account nameoptional review scopeDetermine whether the request is suitable for a deal inspection.Retrieve relevant calls, summaries, CRM context, stakeholders, and risks.Apply a standard Jiminny review framework to assess deal health. Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs.Present findings in a consistent structure with recommended actions.Structured deal inspection with health assessment, red flags, and recommendationsManager Coaching SkillSkillHelp managers review calls consistently and identify coaching momentsAnalyse a rep’s call and surface strengths, gaps, and examples for coachingCall IDoptional rep IDoptional coaching focusRetrieve transcript, score, summary, and key moments from the call.Evaluate the conversation against a coaching framework.Identify what went well and where the rep could improve. Suggest moments to clip as snippets. Recommend coaching actions or playlist additions.Coaching review with strengths, gaps, snippet suggestions, and recommended coaching actionsCSM / QBR Prep SkillSkillHelp CS or AM teams prepare structured customer reviews from conversationsGenerate an account review or QBR-style summary from recent customer interactionsAccount ID, optional time periodRetrieve all recent calls, emails, and account-linked context. Aggregate themes, requests, blockers, sentiment shifts, and opportunities.Apply a repeatable account review structure. Summarise outcomes, open issues, and suggested next steps.Structured QBR/account review summary with themes, risks, opportunities, and actionsSnippet Builder SkillSkillHelp users consistently capture and label useful moments from callsIdentify useful moments in a call and convert them into reusable snippetsCall ID, optional focus area such as pricing, objection, competitor, discoveryRetrieve transcript, recording timeline, and call insights.Detect moments matching the requested focus area.Propose snippet start/end times and titles. Tag snippets by type or theme. Optionally save them or add them to a playlist.One or more proposed or saved snippets with title, timing, and tags\n💡 Technical details Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate.Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users.Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns.\n Open QuestionsTrack unresolved issues or decisions that need to be made. Update as answers become available.Question\n\nAnswer\n\nDate Answered\n\n\n\n\n\n\n\n Competitor researchCompetitor\n\nMCP Availability\nCore Capabilities\n\nAdvanced Capabilities\nPositioning\n\nKey Strength\nKey Weakness\nGong✅ Mature MCPList/search calls Get transcripts get Call metadataDeal intelligence Scorecards, analyticsCRM context Enterprise data layerDeep data + ecosystem integrationsLimited opinionated AI workflowsAvoma✅ Mature MCPlist_meetings get_meetingget_transcriptkey topicsDeal fields/Deal level analysis Coaching/Scoring CRM contextpushing insights into Notion / Confluence AI-ready meeting intelligenceStrongest “outputs” layerMostly pull-based interactionsFathom✅ MCPlist/search meetings get transcript get summary/action items Team/org contextWebhooks (event-based triggers)get_weekly_recapaggregated action items across meetingscreating webhooks directly from the MCP server Lightweight meeting data accessIncludes summaries + actionsLimited analytics depthGrain✅ Basic MCPget meetings download transcriptsNotes Deals ScorecardsData access utilitySimple + accessibleLow sophistication, sometimes browser-basedOutreach⚠️ MCP (workflow-focused)Search sequences Emails Meetings (Kaia)account / prospect / opportunity lookups Workflow automation Multi-system orchestrationRevenue workflow engineEnd-to-end GTM workflowsLess depth in conversation dataAttention⚠️ Emerging MCPMeeting data Transcripts CRM-linked contextAI insights Sales assistant workflowscreate snippetsAI sales assistant + CRMStrong CRM linkageLess focus on external knowledge layer\nPlanhat - \nRelated content\nAsk Jiminny Anything on Call levelGalya Dimitrova\nHubspot appGalya Dimitrova\nProduct Vision & StrategyGalya Dimitrova\nAutomated CRM FillingSimona Georgieva (Deactivated)\nCompetitive analysisGalya Dimitrova\nAI agent ideasSteliyan Georgiev\n\n\n\n\nComments\n\nJames Graham9 Apr\nA few areas around Security & Compliance that need more depth before this moves beyond draft:Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate.Team visibility enforcement should be its own HIGH requirement. The current note under access control (\"ensure MCP only exposes data user has access to\") is doing too much work as a single line. Every MCP tool response (especially search_calls and deal-level aggregations) must be filtered through the same visibility engine as the UI. Semantic search is the riskiest here; it could surface results across team boundaries if not properly scoped.No data minimisation or scoping controls. We're exposing transcripts, CRM fields, contact details with job titles, deal context - significant PII and commercial data leaving the platform. Can customers configure which data types are exposed? Can transcripts be PII-stripped before they leave Jiminny? Given we position GDPR consent as a differentiator, we should be thinking about this.GDPR downstream processing is unaddressed. Piping transcripts to OpenAI/Gemini introduces new data processors. Customers need to acknowledge responsibility for downstream processing during setup. We should also consider whether call participants' recording consent covers onward sharing to third-party AI - it almost certainly doesn't by default. This is one for me to pick up.Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users.Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns.Audit logs should go deeper. Current spec (what data, which system, when) is a start. Add: identity behind the key, specific queries/filters used, and failed access attempts (especially cross-visibility-boundary attempts). Enterprise compliance teams will expect this.Downgrade handling. Doc says Scale tier and above, but doesn't say what happens on downgrade. Keys should auto-deactivate.Happy to help draft a dedicated Security & Compliance section if useful.\n\nGalya Dimitrova15 Apr\nThanks for the input @James Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.","depth":20,"value":"Jiminny MCP Connector\n\n\nEpic Link to Epik in JiraDocument statusDRAFT ObjectiveEnable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product. \n👤 Target userRevenue teams using AI tools (Sales, CS, RevOps)Companies already experimenting with Claude / OpenAI Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc) \n🤕 Pain point or problemJiminny data is locked in the platformAI tools lack access to high-value conversation contextCustomers must manually export/copy transcriptsNo easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales peoplePeer pressure as almost all competitors have this \n💥 Impact and benefitsFor customersBetter AI outputs using real customer conversationsAbility to build automated workflows across toolsFor JiminnyIncreased product stickiness (embedded in workflows)Competitive parity with Gong / Avoma etc. \n💡 Solution ideas Build an AI Knowledge Connector (MCP-based) that:\nExposes Jiminny data (calls, transcripts, metadata) to AI toolsProvides structured insights (summaries, action items, scoring etc.)Supports deal-level contextAllows integration into customer-owned AI stacks (Claude + Zapier, etc.) \n Success metricsList the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).Goal\n\nMetric\n\nIncrease stickiness by ensuring Jiminny is embedded in the customer’s workflows% of customers connecting external AI toolsNumber of MCP/API calls per customer - and which ones are being used the mostDecrease the manual copying of transcripts decrease by X %\n User interaction and designSimple setup flow:Generate API key / connect MCPClear instructions for Claude / Zapier setupProvide:Example prompts for ClaudePre-built workflow templates for ZapierClear documentation - with use cases👉 Focus: usable by non-technical users\n Detailed RequirementsList the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.Requirement\n\nImportance\n\nNotes\n\nAllow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)HIGHProvide MCP endpoint so customers can easily integrate Jiminny into their AI stackAllow customers to access call data through AI toolsHIGHExpose Jiminny data:\nlist calls (filter by date, user, account, deal)get callget transcript (speaker-separated)get call metadatasearch calls (keyword + semantic) Support filtering by:\naccount / contact/ lead / dealjiminny userdate rangeAllow customers to search and query calls using AI toolsHIGHSupport keyword and semantic search across conversationsProvide structured insights so customers can generate better outputsHIGHFor each call, expose:\nSummaryAction itemsKey pointsActivity typeNotesCall ScoreCRM informationAllow customers to understand deal status across multiple calls/emailsHIGHFor each deal expose:\ncalls/meetings/emailsIdentified risks contacts related to the deal with their job titlelinked account detailsall CRM field information that we have in DI + fields used for CRM fillingexample - get_deal_summaryrequest - \n{ \"tool\": \"get_deal_summary\", \"arguments\": { \"deal_name\": \"ACME\" }}responce:\n{ \"deal_name\": \"ACME\", \"stage\": \"Negotiation\", \"recent_activity\": [ \"Demo call on 2026-04-01\", \"Pricing discussion on 2026-04-03\" ], \"risks\": [ \"Pricing concerns\", \"Security review pending\" ], \"next_steps\": [ \"Send follow-up to VP Sales\", \"Share security documentation\" ]} Ensure the AI can easily understand the schema of the data coming from JiminnyHIGHModels perform much better when tools have:\nclear namesrich descriptionspredictable schemasProvide clear, actionable error states for customers when MCP connections failHIGHError messages should guide agents toward solutions with specific suggestions and next steps.\nExpired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”Allow customers to securely control who and what data is accessible externallyHIGHEnsure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well. Private meetings shouldn’t be exposed via MCPHIGHWhen a meetings is set to Private then the information about it shouldn’t be exposed through the MCP. Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflowsHIGHEnsure data is structured to be easily combined with:\nCRM (accounts, deals)external docsUse consistent identifiers:\naccount IDsdeal IDs MCP is available only for Scale tier and aboveHIGHCustomers who are on Capture tier should be able to access any of their data through MCP. This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working. Provide visibility into how external AI tools access Jiminny dataHIGHAudit logs:\nwhat data was accessed by which external system whenidentity - who made the request specific queries/filters usedfailed access attempts (especially cross-visibility-boundary attempts)Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it.example from Atlassian - Provide customers with example prompts to get started quickly HIGHMCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments. See examples in table below.Expose Jiminny as a Connector in ClaudemediumCompetitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect JiminnyProvide customers with example workflows to get started quicklymediumA workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.See examples in table below.Allow customers to use predefined Jiminny skills for common workflows mediumAnthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com See examples in table below.Jiminny UI widget for OpenAImedium \n The reason to have this is to make certain Jiminny use cases much easier than pure chat.Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.Allow customers to run long-running analyses asynchronously and retrieve progress/results lowSome requests/questions require analysis of Hugh volumes of data. For those, it may be better to:\nstart the joblet it run in the backgroundreturn a status like “in progress”let the client come back for the result later For example - User asks in Claude:\n“Analyse all calls from this quarter and tell me the top 5 objections by segment.”That might require:\nsearching lots of callspulling many summaries/transcriptsaggregating resultsInstead of making Claude wait 45+ seconds, Jiminny could:\ncreate an analysis jobreturn: job_id = 123, status = running We need this in order to support:\nlarge-scale analysismulti-call aggregation across lots of dataplaylist generation across many callsheavy recap/report generationCreating a snippet through the MCPlowCreate a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team. example request:\n{ \"tool\": \"create_snippet\", \"arguments\": { \"call_id\": \"call_123\", \"start_time\": \"00:18:24\", \"end_time\": \"00:19:10\", \"title\": \"Pricing objection\" }}example response:\n{ \"snippet_id\": \"snip_987\", \"title\": \"Pricing objection\", \"share_url\": \"https://app.jiminny.com/snippets/snip_987\"}Ability to add recordings or snippets to a playlistlowAllow customers to create libraries of calls automatically. They can use those for onboarding or other purposesAllow AI tools to read reusable Jiminny resources low fathomResources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be:\ncurrent deal snapshotaccount summaryweekly rep recapapproved talk tracks / objection handling guidancecurated playlists or snippet librariesThis is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls. Ability to write coaching notes in Jiminny through MCPlow \nWhat we want to do for prompts/worflows and skillsCapability\n\nType\n\nUser value / job to be done\nExample Jiminny use case\nInputs\n\nSteps performed\n\nOutput\n\nDraft follow-up emailPromptHelp reps quickly create tailored follow-ups after callsUse the latest customer call to draft an email with recap and next stepsCall IDoptional toneoptional recipient typeRetrieve call summary and action items from Jiminny.Pull key points, commitments, and next steps.Apply the chosen tone and structure.Draft the email in a consistent format.Customer-ready follow-up email draftSummarise callPromptHelp users quickly extract the most important points from a conversationGenerate a concise summary of one call for internal or external useCall IDoptional summary styleoptional audienceRetrieve transcript, metadata, and structured insights.Identify key topics, decisions, risks, and next steps.Format output for the chosen audience.Short or structured call summary with actions and risksGenerate deal recapPromptHelp reps and managers get a fast overview of a dealSummarise the current state of a deal based on recent calls and linked CRM contextDeal ID or account nameoptional date rangeRetrieve calls and meetings linked to the deal. Pull deal metadata and CRM-linked context. Identify recent changes, blockers, risks, and next steps.Summarise into a concise recap.Deal recap with status, blockers, risks, and recommended next stepsPrepare weekly recapPromptHelp managers or reps review what changed over a time periodWeekly recap of calls, themes, and actions for one rep, team, or accountRep IDteam ID or account IDtime periodRetrieve relevant calls and summaries for the selected time period.Aggregate themes, action items, and changes. Highlight notable risks, opportunities, and follow-ups.Weekly recap with top themes, risks, opportunities, and actionsPrepare deal reviewWorkflowHelp reps and managers review deal status using multiple sources of contextReview a deal before pipeline review or forecast meetingDeal IDoptional date rangeRetrieve all recent calls, emails, and meetings tied to the deal. Pull structured call summaries, score, action items, and key points. Pull linked CRM context such as stage, contacts, and account details.Aggregate open risks, unresolved objections, stakeholder gaps, and next steps. Format into a review-ready structure.Deal review document with current status, risks, stakeholders, blockers, and next stepsWeekly team recapWorkflowHelp managers monitor activity and risk across a group of reps or accountsEnd-of-week team summary for sales leadershipTeam IDdate rangeRetrieve calls and structured insights across the team.Group by rep, account, or deal.Identify recurring themes, major risks, strong calls, and missed follow-ups.Highlight calls or deals that need attention.Team-level recap with patterns, key risks, notable wins, and action areasObjection analysis across callsWorkflowHelp teams identify patterns across multiple conversationsAnalyse pricing or competitor objections across recent callsAccount IDdeal IDrep IDkeyword/topicdate rangeSearch calls matching the topic or keyword.Retrieve summaries, transcripts, and snippets where relevant.Identify recurring objections and how they were handled.Assess whether objections were resolved or remain open.Summarise patterns and examples.Objection analysis with themes, frequency, examples, and suggested follow-up actionsBuild onboarding playlistWorkflowHelp enablement teams curate useful learning content fasterBuild a playlist of strong discovery, demo, or pricing calls for new hiresTopiccall typerep/team filteroptional time periodSearch for calls matching the selected criteria. Rank or filter calls based on score, topics, or key moments. Identify useful snippets or full recordings. Add selected calls/snippets into a playlist structure.Generate short descriptions for each item.Curated onboarding playlist with selected calls/snippets and descriptionsDeal Inspection SkillSkillGive users a consistent, opinionated way to assess a dealClaude reviews a deal using a repeatable Jiminny methodologyDeal ID or account nameoptional review scopeDetermine whether the request is suitable for a deal inspection.Retrieve relevant calls, summaries, CRM context, stakeholders, and risks.Apply a standard Jiminny review framework to assess deal health. Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs.Present findings in a consistent structure with recommended actions.Structured deal inspection with health assessment, red flags, and recommendationsManager Coaching SkillSkillHelp managers review calls consistently and identify coaching momentsAnalyse a rep’s call and surface strengths, gaps, and examples for coachingCall IDoptional rep IDoptional coaching focusRetrieve transcript, score, summary, and key moments from the call.Evaluate the conversation against a coaching framework.Identify what went well and where the rep could improve. Suggest moments to clip as snippets. Recommend coaching actions or playlist additions.Coaching review with strengths, gaps, snippet suggestions, and recommended coaching actionsCSM / QBR Prep SkillSkillHelp CS or AM teams prepare structured customer reviews from conversationsGenerate an account review or QBR-style summary from recent customer interactionsAccount ID, optional time periodRetrieve all recent calls, emails, and account-linked context. Aggregate themes, requests, blockers, sentiment shifts, and opportunities.Apply a repeatable account review structure. Summarise outcomes, open issues, and suggested next steps.Structured QBR/account review summary with themes, risks, opportunities, and actionsSnippet Builder SkillSkillHelp users consistently capture and label useful moments from callsIdentify useful moments in a call and convert them into reusable snippetsCall ID, optional focus area such as pricing, objection, competitor, discoveryRetrieve transcript, recording timeline, and call insights.Detect moments matching the requested focus area.Propose snippet start/end times and titles. Tag snippets by type or theme. Optionally save them or add them to a playlist.One or more proposed or saved snippets with title, timing, and tags\n💡 Technical details Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate.Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users.Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns.\n Open QuestionsTrack unresolved issues or decisions that need to be made. Update as answers become available.Question\n\nAnswer\n\nDate Answered\n\n\n\n\n\n\n\n Competitor researchCompetitor\n\nMCP Availability\nCore Capabilities\n\nAdvanced Capabilities\nPositioning\n\nKey Strength\nKey Weakness\nGong✅ Mature MCPList/search calls Get transcripts get Call metadataDeal intelligence Scorecards, analyticsCRM context Enterprise data layerDeep data + ecosystem integrationsLimited opinionated AI workflowsAvoma✅ Mature MCPlist_meetings get_meetingget_transcriptkey topicsDeal fields/Deal level analysis Coaching/Scoring CRM contextpushing insights into Notion / Confluence AI-ready meeting intelligenceStrongest “outputs” layerMostly pull-based interactionsFathom✅ MCPlist/search meetings get transcript get summary/action items Team/org contextWebhooks (event-based triggers)get_weekly_recapaggregated action items across meetingscreating webhooks directly from the MCP server Lightweight meeting data accessIncludes summaries + actionsLimited analytics depthGrain✅ Basic MCPget meetings download transcriptsNotes Deals ScorecardsData access utilitySimple + accessibleLow sophistication, sometimes browser-basedOutreach⚠️ MCP (workflow-focused)Search sequences Emails Meetings (Kaia)account / prospect / opportunity lookups Workflow automation Multi-system orchestrationRevenue workflow engineEnd-to-end GTM workflowsLess depth in conversation dataAttention⚠️ Emerging MCPMeeting data Transcripts CRM-linked contextAI insights Sales assistant workflowscreate snippetsAI sales assistant + CRMStrong CRM linkageLess focus on external knowledge layer\nPlanhat - \nRelated content\nAsk Jiminny Anything on Call levelGalya Dimitrova\nHubspot appGalya Dimitrova\nProduct Vision & StrategyGalya Dimitrova\nAutomated CRM FillingSimona Georgieva (Deactivated)\nCompetitive analysisGalya Dimitrova\nAI agent ideasSteliyan Georgiev\n\n\n\n\nComments\n\nJames Graham9 Apr\nA few areas around Security & Compliance that need more depth before this moves beyond draft:Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate.Team visibility enforcement should be its own HIGH requirement. The current note under access control (\"ensure MCP only exposes data user has access to\") is doing too much work as a single line. Every MCP tool response (especially search_calls and deal-level aggregations) must be filtered through the same visibility engine as the UI. Semantic search is the riskiest here; it could surface results across team boundaries if not properly scoped.No data minimisation or scoping controls. We're exposing transcripts, CRM fields, contact details with job titles, deal context - significant PII and commercial data leaving the platform. Can customers configure which data types are exposed? Can transcripts be PII-stripped before they leave Jiminny? Given we position GDPR consent as a differentiator, we should be thinking about this.GDPR downstream processing is unaddressed. Piping transcripts to OpenAI/Gemini introduces new data processors. Customers need to acknowledge responsibility for downstream processing during setup. We should also consider whether call participants' recording consent covers onward sharing to third-party AI - it almost certainly doesn't by default. This is one for me to pick up.Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users.Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns.Audit logs should go deeper. Current spec (what data, which system, when) is a start. Add: identity behind the key, specific queries/filters used, and failed access attempts (especially cross-visibility-boundary attempts). Enterprise compliance teams will expect this.Downgrade handling. Doc says Scale tier and above, but doesn't say what happens on downgrade. Keys should auto-deactivate.Happy to help draft a dedicated Security & Compliance section if useful.\n\nGalya Dimitrova15 Apr\nThanks for the input @James Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.","help_text":"","role_description":"text entry area","subrole":"AXUnknown","is_enabled":true,"is_focused":true,"is_selected":false},{"role":"AXStaticText","text":"Jiminny MCP Connector","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Epic","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Link to Epik in Jira","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Document status","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"DRAFT","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Objective","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"👤 Target user","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Revenue teams using AI tools (Sales, CS, RevOps)","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Companies already experimenting with Claude / OpenAI","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"🤕 Pain point or problem","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny data is locked in the platform","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"AI tools lack access to high-value conversation context","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Customers must manually export/copy transcripts","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Peer pressure as almost all competitors have this","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"💥 Impact and benefits","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"For customers","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Better AI outputs using real customer conversations","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ability to build automated workflows across tools","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"For Jiminny","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Increased product stickiness (embedded in workflows)","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Competitive parity with Gong / Avoma etc.","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"💡 Solution ideas","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Build an AI Knowledge Connector (MCP-based) that:","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Exposes Jiminny data (calls, transcripts, metadata) to AI tools","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provides structured insights (summaries, action items, scoring etc.)","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Supports deal-level context","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Success metrics","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Goal","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Metric","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"% of customers connecting external AI tools","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Number of MCP/API calls per customer - and which ones are being used the most","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Decrease the manual copying of transcripts","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"decrease by X %","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"User interaction and design","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Simple setup flow:","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Generate API key / connect MCP","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Clear instructions for Claude / Zapier setup","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide:","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Example prompts for Claude","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Pre-built workflow templates for Zapier","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Clear documentation - with use cases","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"👉 Focus: usable by non-technical users","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Detailed Requirements","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Requirement","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Importance","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Notes","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to access call data through AI tools","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Expose Jiminny data:","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"list calls (filter by date, user, account, deal)","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"get call","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"get transcript (speaker-separated)","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"get call 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tools","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Support keyword and semantic search across conversations","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide structured insights so customers can generate better outputs","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"For each call, expose:","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Summary","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Action items","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Key points","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Activity type","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Notes","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Call Score","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"CRM information","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to understand deal status across multiple calls/emails","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"For each deal expose:","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"calls/meetings/emails","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Identified risks","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"contacts related to the deal with their job title","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"linked account details","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"all CRM field information that we have in DI + fields used for CRM filling","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"example - get_deal_summary","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"request -","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"{","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"tool\": \"get_deal_summary\",","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"arguments\": {","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"deal_name\": \"ACME\"","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"responce:","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"{","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"deal_name\": \"ACME\",","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"stage\": \"Negotiation\",","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"recent_activity\": [","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"Demo call on 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Sales\",","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"Share security documentation\"","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"]","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ensure the AI can easily understand the schema of the data coming from Jiminny","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Models perform much better when tools have:","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"clear names","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"rich descriptions","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"predictable schemas","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide clear, actionable error states for customers when MCP connections fail","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Error messages should guide agents toward solutions with specific suggestions and next steps.","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Expired or revoked key — “Error: Authentication failed. 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hybrid
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST [URL_WITH_CREDENTIALS] Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.
Jiminny MCP Connector
Epic
Link to Epik in Jira
Document status
DRAFT
Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.
👤 Target user
Revenue teams using AI tools (Sales, CS, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
Build an AI Knowledge Connector (MCP-based) that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides structured insights (summaries, action items, scoring etc.)
Supports deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
Metric
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of MCP/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
User interaction and design
Simple setup flow:
Generate API key / connect MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus: usable by non-technical users
Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
Importance
Notes
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH
For each deal expose:
calls/meetings/emails
Identified risks
contacts related to the deal with their job title
linked account details
all CRM field information that we have in DI + fields used for CRM filling
example - get_deal_summary
request -
{
"tool": "get_deal_summary",
"arguments": {
"deal_name": "ACME"
}
}
responce:
{
"deal_name": "ACME",
"stage": "Negotiation",
"recent_activity": [
"Demo call on 2026-04-01",
"Pricing discussion on 2026-04-03"
],
"risks": [
"Pricing concerns",
"Security review pending"
], "next_steps": [
"Send follow-up to VP Sales",
"Share security documentation"
]
}
Ensure the AI can easily understand the schema of the data coming from Jiminny
HIGH
Models perform much better when tools have:
clear names
rich descriptions
predictable schemas
Provide clear, actionable error states for customers when MCP connections fail
HIGH
Error messages should guide agents toward solutions with specific suggestions and next steps.
Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”
Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”
Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”
Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”
Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”
Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”
No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”
Allow customers to securely control who and what data is accessible externally
HIGH
Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well.
Private meetings shouldn’t be exposed via MCP
HIGH
When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP.
Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows
HIGH
Ensure data is structured to be easily combined with:
CRM (accounts, deals)
external docs
Use consistent identifiers:
account IDs
deal IDs
MCP is available only for Scale tier and above
HIGH
Customers who are on Capture tier should be able to access any of their data through MCP.
This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working.
Provide visibility into how external AI tools access Jiminny data
iTerm2ShellEditViewSessionScriptsProfilesWindowHelp• Al chapter • 24 m leftA100% <7dockerDOCKER₴81docker882-zshroot@docker_lamp_1:/home/jiminny# php artisan jiminny:debugNow: 2026-04-21 13:25:21From: 2026-04-20 00:00:00To: 2026-04-20 23:59:59root@docker_lamp_1:/home/jiminny# php artisan jiminny:debugNow:2026-04-20 13:25:31From: 2026-04-17 00:00:00To:2026-04-1723:59:59root@docker_lamp_1:/home/jiminny# php artisan jiminny:debugNow: 2026-04-1913:26:02From: 2026-04-1700:00:00To: 2026-04-1723:59:59root@docker_lamp_1:/home/jiminny# php artisan jiminny:debugNow: 2026-04-18 13:26:53From: 2026-04-17 00:00:00To: 2026-04-1723:59:59root@docker_lamp_1:/home/jiminny# php artisan jiminny:debugNow: 2026-04-1713:27:05From: 2026-04-16 00:00:00To: 2026-04-16 23:59:59root@docker_lamp_1:/home/jiminny#]* Build full day ac... • 24screenpipe"• 885-zsh86APP (-zsh)87Wed 22 Apr 17:36:26T81ec2-user@ip-10-..• *8+...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
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Product Strategy 2025+ Change emoji
Product Strategy 2025+
Product Strategy 2025+
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Competitive analysis
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Ways of working - Product processes
Ways of working - Product processes
Core Product Performance Metrics 📈
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Product Documentation
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Autologging activities
Deal Insights - Multiple currencies
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Reinvent Themes & Topics (and ACS)
Reinvent Themes & Topics (and ACS)
Change emoji Billing Portal
Billing Portal
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Upload Video/Audio Recordings
White-Label Jiminny Instance
White-Label Jiminny Instance
Win/Loss Analysis for a Deal
Win/Loss Analysis for a Deal
Change emoji Hubspot app
Hubspot app
Change emoji Automatically hard delete data for churned customers
Automatically hard delete data for churned customers
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Ask Jiminny Anything on Call level
Change emoji Ask Jiminny for Open and Closed Deals
Ask Jiminny for Open and Closed Deals
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Ask Jiminny Anything on Deal level
Change emoji Automatically record all calendar meetings
Automatically record all calendar meetings
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Product Tiering
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Recording Consent
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Automated CRM Filling
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Jiminny MCP Connector
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
Read time 12 min
12 min
Views 14
14
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DRAFT
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DRAFT
Change emoji Objective
Objective
Change emoji Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a
data layer for AI-driven revenue workflows
, not just a standalone product.
👤 Target user
👤 Target user
👤 Target user
Revenue teams using AI tools (Sales,
CS
, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
🤕
🤕
Pain point or problem
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
💥
💥
Impact and benefits
💥 Impact and benefits
For customers
For customers
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
For Jiminny
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
💡
💡
Solution ideas
💡 Solution ideas
Build an
AI Knowledge Connector (
MCP
-based)
that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides
structured insights
(summaries, action items, scoring etc.)
Supports
deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Change emoji Success metrics
Success metrics
Change emoji Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
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Metric
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Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
Goal
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
Decrease the manual copying of transcripts
Metric
No sort applied to the column
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
decrease by X %
Change emoji User interaction and design
User interaction and design
Change emoji User interaction and design
Simple setup flow:
Generate API key / connect
MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus:
usable by non-technical users
Change emoji Detailed Requirements
Detailed Requirements
Change emoji Detailed Requirements...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
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Product Strategy 2025+ Change emoji
Product Strategy 2025+
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Ways of working - Product processes Change emoji
Ways of working - Product processes
Ways of working - Product processes
Core Product Performance Metrics 📈
Core Product Performance Metrics
📈
Core Product Performance Metrics
Product Documentation Change emoji
Product Documentation
Product Documentation
Product Briefs Change emoji
Product Briefs
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Change emoji Activity Export
Activity Export
Change emoji Autologging activities
Autologging activities
Deal Insights - Multiple currencies
Deal Insights - Multiple currencies
Reinvent Themes & Topics (and ACS)
Reinvent Themes & Topics (and ACS)
Change emoji Billing Portal
Billing Portal
Change emoji Upload Video/Audio Recordings
Upload Video/Audio Recordings
White-Label Jiminny Instance
White-Label Jiminny Instance
Win/Loss Analysis for a Deal
Win/Loss Analysis for a Deal
Change emoji Hubspot app
Hubspot app
Change emoji Automatically hard delete data for churned customers
Automatically hard delete data for churned customers
Change emoji Ask Jiminny Anything on Call level
Ask Jiminny Anything on Call level
Change emoji Ask Jiminny for Open and Closed Deals
Ask Jiminny for Open and Closed Deals
Change emoji Ask Jiminny Anything on Deal level
Ask Jiminny Anything on Deal level
Change emoji Automatically record all calendar meetings
Automatically record all calendar meetings
Change emoji Product Tiering
Product Tiering
Change emoji Recording Consent
Recording Consent
Change emoji Automated CRM Filling
Automated CRM Filling
Change emoji Automated Exec Reports
Automated Exec Reports
Change emoji Auto-detect Activity Type
Auto-detect Activity Type
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Change emoji AJA on Anything
AJA on Anything
Change emoji AI Call Scoring
AI Call Scoring
Jiminny MCP Connector
Jiminny MCP Connector
Desktop app to record a meetings without visible Notetaker
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
Read time 12 min
12 min
Views 14
14
Add a reaction
Add a reaction
Epic
Link to Epik in Jira
Document status
DRAFT
Epic
Document status
Link to Epik in Jira
DRAFT
Change emoji Objective
Objective
Change emoji Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a
data layer for AI-driven revenue workflows
, not just a standalone product.
👤 Target user
👤 Target user
👤 Target user
Revenue teams using AI tools (Sales,
CS
, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
🤕
🤕
Pain point or problem
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
💥
💥
Impact and benefits
💥 Impact and benefits
For customers
For customers
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
For Jiminny
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
💡
💡
Solution ideas
💡 Solution ideas
Build an
AI Knowledge Connector (
MCP
-based)
that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides
structured insights
(summaries, action items, scoring etc.)
Supports
deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Change emoji Success metrics
Success metrics
Change emoji Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
No sort applied to the column
Metric
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
Goal
No sort applied to the column
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
Decrease the manual copying of transcripts
Metric
No sort applied to the column
% of customers connecting external AI tools
Number of
MCP
/API calls per customer - and which ones are being used the most
decrease by X %
Change emoji User interaction and design
User interaction and design
Change emoji User interaction and design
Simple setup flow:
Generate API key / connect
MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus:
usable by non-technical users
Change emoji Detailed Requirements
Detailed Requirements
Change emoji Detailed Requirements...
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2026-04-22T14:36:32.399427+00:00
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Jiminny MCP Connector - Product - Confluence — Wor Jiminny MCP Connector - Product - Confluence — Work...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
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Fast Answers quickly
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Product Strategy 2025+ Change emoji
Product Strategy 2025+
Product Strategy 2025+
Competitive analysis Change emoji
Competitive analysis
Competitive analysis
Ways of working - Product processes Change emoji
Ways of working - Product processes
Ways of working - Product processes
Core Product Performance Metrics 📈
Core Product Performance Metrics
📈
Core Product Performance Metrics
Product Documentation Change emoji
Product Documentation
Product Documentation
Product Briefs Change emoji
Product Briefs
Product Briefs
Change emoji Activity Export
Activity Export
Change emoji Autologging activities
Autologging activities
Deal Insights - Multiple currencies
Deal Insights - Multiple currencies
Reinvent Themes & Topics (and ACS)
Reinvent Themes & Topics (and ACS)
Change emoji Billing Portal
Billing Portal
Change emoji Upload Video/Audio Recordings
Upload Video/Audio Recordings
White-Label Jiminny Instance
White-Label Jiminny Instance
Win/Loss Analysis for a Deal
Win/Loss Analysis for a Deal
Change emoji Hubspot app
Hubspot app
Change emoji Automatically hard delete data for churned customers
Automatically hard delete data for churned customers
Change emoji Ask Jiminny Anything on Call level
Ask Jiminny Anything on Call level
Change emoji Ask Jiminny for Open and Closed Deals
Ask Jiminny for Open and Closed Deals
Change emoji Ask Jiminny Anything on Deal level
Ask Jiminny Anything on Deal level
Change emoji Automatically record all calendar meetings
Automatically record all calendar meetings
Change emoji Product Tiering
Product Tiering
Change emoji Recording Consent
Recording Consent
Change emoji Automated CRM Filling
Automated CRM Filling
Change emoji Automated Exec Reports
Automated Exec Reports
Change emoji Auto-detect Activity Type
Auto-detect Activity Type
AI Signals & Alerts
AI Signals & Alerts
Change emoji AJA on Anything
AJA on Anything
Change emoji AI Call Scoring
AI Call Scoring
Jiminny MCP Connector
Jiminny MCP Connector
Desktop app to record a meetings without visible Notetaker
Desktop app to record a meetings without visible Notetaker
Feedback Change emoji
Feedback
Feedback
Research & User Feedback Change emoji
Research & User Feedback
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
Read time 12 min
12 min
Views 14
14
Add a reaction
Add a reaction
Epic
Link to Epik in Jira
Document status
DRAFT
Epic
Document status
Link to Epik in Jira
DRAFT
Change emoji Objective
Objective
Change emoji Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a
data layer for AI-driven revenue workflows
, not just a standalone product.
👤 Target user
👤 Target user
👤 Target user
Revenue teams using AI tools (Sales,...
|
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a","depth":16,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"data layer for AI-driven revenue workflows","depth":17,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":", not just a standalone product.","depth":16,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"👤 Target user","depth":15,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"👤 Target user","depth":16,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"👤 Target user","depth":16,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Revenue teams using AI tools (Sales,","depth":18,"help_text":"","role_description":"text","subrole":"AXUnknown"}]...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
[JY-20372] AI Reports > Empty page design and promotion - Jira
[JY-20372] AI Reports > Empty page design and promotion - Jira
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
Jiminny
Jiminny
Workers | Datadog
Workers | Datadog
Pull requests · jiminny/app
Pull requests · jiminny/app
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
[JY-20728] [HubSpot] Find the root cause of 429 hit and tweak API client rate limiter - Jira
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app
Jiminny
Jiminny
CloudWatch | us-east-2
CloudWatch | us-east-2
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
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Jiminny MCP Connector
Jiminny MCP Connector
Jiminny MCP Connector
By Galya Dimitrova
By Galya Dimitrova
Read time 12 min
12 min
Views 14
14
Add a reaction
Add a reaction
Epic
Link to Epik in Jira
Document status
DRAFT
Epic
Document status
Link to Epik in Jira
DRAFT
Change emoji Objective
Objective
Change emoji Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a
data layer for AI-driven revenue workflows
, not just a standalone product.
👤 Target user
👤 Target user
👤 Target user
Revenue teams using AI tools (Sales,...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp• Al chapter • 24 m leftA100% <7dockerDOCKER981docker882-zshroot@docker_lamp_1:/home/jiminny# php artisan jiminny:debugNow: 2026-04-21 13:25:21From: 2026-04-20 00:00:00To: 2026-04-20 23:59:59root@docker_lamp_1:/home/jiminny# php artisan jiminny:debugNow:2026-04-20 13:25:31From: 2026-04-17 00:00:00To:2026-04-1723:59:59root@docker_lamp_1:/home/jiminny# php artisan jiminny:debugNow: 2026-04-1913:26:02From: 2026-04-1700:00:00To: 2026-04-1723:59:59root@docker_lamp_1:/home/jiminny# php artisan jiminny:debugNow: 2026-04-18 13:26:53From: 2026-04-17 00:00:00To: 2026-04-1723:59:59root@docker_lamp_1:/home/jiminny# php artisan jiminny:debugNow: 2026-04-1713:27:05From: 2026-04-16 00:00:00To: 2026-04-16 23:59:59root@docker_lamp_1:/home/jiminny#]* Build full day ac... • 24screenpipe"• 885-zsh86APP (-zsh)87Wed 22 Apr 17:36:36T81ec2-user@ip-10-..• *8+...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp• Al chapter • 24 m leftA100% <7dockerDOCKER981docker882-zshroot@docker_lamp_1:/home/jiminny# php artisan jiminny:debugNow: 2026-04-21 13:25:21From: 2026-04-20 00:00:00To: 2026-04-20 23:59:59root@docker_lamp_1:/home/jiminny# php artisan jiminny:debugNow:2026-04-20 13:25:31From: 2026-04-17 00:00:00To:2026-04-1723:59:59root@docker_lamp_1:/home/jiminny# php artisan jiminny:debugNow: 2026-04-1913:26:02From: 2026-04-1700:00:00To: 2026-04-1723:59:59root@docker_lamp_1:/home/jiminny# php artisan jiminny:debugNow: 2026-04-18 13:26:53From: 2026-04-17 00:00:00To: 2026-04-1723:59:59root@docker_lamp_1:/home/jiminny# php artisan jiminny:debugNow: 2026-04-1713:27:05From: 2026-04-16 00:00:00To: 2026-04-16 23:59:59root@docker_lamp_1:/home/jiminny#]* Build full day ac... • 24screenpipe"• 885-zsh86APP (-zsh)87Wed 22 Apr 17:36:36T81ec2-user@ip-10-..• *8+...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST [URL_WITH_CREDENTIALS] Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.
You said
Jiminny MCP Connector
Epic
Link to Epik in Jira
Document status
DRAFT
Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.
👤 Target user
Revenue teams using AI tools (Sales, CS, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
Build an AI Knowledge Connector (MCP-based) that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides structured insights (summaries, action items, scoring etc.)
Supports deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
Metric
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of MCP/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
User interaction and design
Simple setup flow:
Generate API key / connect MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus: usable by non-technical users
Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
Importance
Notes
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH
For each deal expose:
calls/meetings/emails
Identified risks
contacts related to the deal with their job title
linked account details
all CRM field information that we have in DI + fields used for CRM filling
example - get_deal_summary
request -
{
"tool": "get_deal_summary",
"arguments": {
"deal_name": "ACME"
}
}
responce:
{
"deal_name": "ACME",
"stage": "Negotiation",
"recent_activity": [
"Demo call on 2026-04-01",
"Pricing discussion on 2026-04-03"
],
"risks": [
"Pricing concerns",
"Security review pending"
], "next_steps": [
"Send follow-up to VP Sales",
"Share security documentation"
]
}
Ensure the AI can easily understand the schema of the data coming from Jiminny
HIGH
Models perform much better when tools have:
clear names
rich descriptions
predictable schemas
Provide clear, actionable error states for customers when MCP connections fail
HIGH
Error messages should guide agents toward solutions with specific suggestions and next steps.
Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”
Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”
Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”
Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”
Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”
Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”
No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”
Allow customers to securely control who and what data is accessible externally
HIGH
Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well.
Private meetings shouldn’t be exposed via MCP
HIGH
When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP.
Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows
HIGH
Ensure data is structured to be easily combined with:
CRM (accounts, deals)
external docs
Use consistent identifiers:
account IDs
deal IDs
MCP is available only for Scale tier and above
HIGH
Customers who are on Capture tier should be able to access any of their data through MCP.
This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working.
Provide visibility into how external AI tools access Jiminny data
HIGH
Audit logs:
what data was accessed by which external system when
identity - who made the request
specific queries/filters used
failed access attempts (especially cross-visibility-boundary attempts)
Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it.
example from Atlassian -
Provide customers with example prompts to get started quickly
HIGH
MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments.
See examples in table below.
Expose Jiminny as a Connector in Claude
medium
Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny
Provide customers with example workflows to get started quickly
medium
A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.
See examples in table below.
Allow customers to use predefined Jiminny skills for common workflows...
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Objective Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows. Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product. 👤 Target user Revenue teams using AI tools (Sales, CS, RevOps) Companies already experimenting with Claude / OpenAI Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc) 🤕 Pain point or problem Jiminny data is locked in the platform AI tools lack access to high-value conversation context Customers must manually export/copy transcripts No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people Peer pressure as almost all competitors have this 💥 Impact and benefits For customers Better AI outputs using real customer conversations Ability to build automated workflows across tools For Jiminny Increased product stickiness (embedded in workflows) Competitive parity with Gong / Avoma etc. 💡 Solution ideas Build an AI Knowledge Connector (MCP-based) that: Exposes Jiminny data (calls, transcripts, metadata) to AI tools Provides structured insights (summaries, action items, scoring etc.) Supports deal-level context Allows integration into customer-owned AI stacks (Claude + Zapier, etc.) Success metrics List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets). Goal Metric Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows % of customers connecting external AI tools Number of MCP/API calls per customer - and which ones are being used the most Decrease the manual copying of transcripts decrease by X % User interaction and design Simple setup flow: Generate API key / connect MCP Clear instructions for Claude / Zapier setup Provide: Example prompts for Claude Pre-built workflow templates for Zapier Clear documentation - with use cases 👉 Focus: usable by non-technical users Detailed Requirements List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification. Requirement Importance Notes Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI) HIGH Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack Allow customers to access call data through AI tools HIGH Expose Jiminny data: list calls (filter by date, user, account, deal) get call get transcript (speaker-separated) get call metadata search calls (keyword + semantic) Support filtering by: account / contact/ lead / deal jiminny user date range Allow customers to search and query calls using AI tools HIGH Support keyword and semantic search across conversations Provide structured insights so customers can generate better outputs HIGH For each call, expose: Summary Action items Key points Activity type Notes Call Score CRM information Allow customers to understand deal status across multiple calls/emails HIGH For each deal expose: calls/meetings/emails Identified risks contacts related to the deal with their job title linked account details all CRM field information that we have in DI + fields used for CRM filling example - get_deal_summary request - { \"tool\": \"get_deal_summary\", \"arguments\": { \"deal_name\": \"ACME\" } } responce: { \"deal_name\": \"ACME\", \"stage\": \"Negotiation\", \"recent_activity\": [ \"Demo call on 2026-04-01\", \"Pricing discussion on 2026-04-03\" ], \"risks\": [ \"Pricing concerns\", \"Security review pending\" ], \"next_steps\": [ \"Send follow-up to VP Sales\", \"Share security documentation\" ] } Ensure the AI can easily understand the schema of the data coming from Jiminny HIGH Models perform much better when tools have: clear names rich descriptions predictable schemas Provide clear, actionable error states for customers when MCP connections fail HIGH Error messages should guide agents toward solutions with specific suggestions and next steps. Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.” Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.” Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.” Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.” Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.” Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.” No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.” Allow customers to securely control who and what data is accessible externally HIGH Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well. Private meetings shouldn’t be exposed via MCP HIGH When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP. Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows HIGH Ensure data is structured to be easily combined with: CRM (accounts, deals) external docs Use consistent identifiers: account IDs deal IDs MCP is available only for Scale tier and above HIGH Customers who are on Capture tier should be able to access any of their data through MCP. This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working. Provide visibility into how external AI tools access Jiminny data HIGH Audit logs: what data was accessed by which external system when identity - who made the request specific queries/filters used failed access attempts (especially cross-visibility-boundary attempts) Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it. example from Atlassian - Provide customers with example prompts to get started quickly HIGH MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments. See examples in table below. Expose Jiminny as a Connector in Claude medium Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny Provide customers with example workflows to get started quickly medium A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome. See examples in table below. Allow customers to use predefined Jiminny skills for common workflows medium Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job. Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com See examples in table below. Jiminny UI widget for OpenAI medium The reason to have this is to make certain Jiminny use cases much easier than pure chat. Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it. For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface. Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section. Allow customers to run long-running analyses asynchronously and retrieve progress/results low Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to: start the job let it run in the background return a status like “in progress” let the client come back for the result later For example - User asks in Claude: “Analyse all calls from this quarter and tell me the top 5 objections by segment.” That might require: searching lots of calls pulling many summaries/transcripts aggregating results Instead of making Claude wait 45+ seconds, Jiminny could: create an analysis job return: job_id = 123, status = running We need this in order to support: large-scale analysis multi-call aggregation across lots of data playlist generation across many calls heavy recap/report generation Creating a snippet through the MCP low Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team. example request: { \"tool\": \"create_snippet\", \"arguments\": { \"call_id\": \"call_123\", \"start_time\": \"00:18:24\", \"end_time\": \"00:19:10\", \"title\": \"Pricing objection\" } } example response: { \"snippet_id\": \"snip_987\", \"title\": \"Pricing objection\", \"share_url\": \"https://app.jiminny.com/snippets/snip_987\" } Ability to add recordings or snippets to a playlist low Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes Allow AI tools to read reusable Jiminny resources low fathom Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be: current deal snapshot account summary weekly rep recap approved talk tracks / objection handling guidance curated playlists or snippet libraries This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls. Ability to write coaching notes in Jiminny through MCP low What we want to do for prompts/worflows and skills Capability Type User value / job to be done Example Jiminny use case Inputs Steps performed Output Draft follow-up email Prompt Help reps quickly create tailored follow-ups after calls Use the latest customer call to draft an email with recap and next steps Call ID optional tone optional recipient type Retrieve call summary and action items from Jiminny. Pull key points, commitments, and next steps. Apply the chosen tone and structure. Draft the email in a consistent format. Customer-ready follow-up email draft Summarise call Prompt Help users quickly extract the most important points from a conversation Generate a concise summary of one call for internal or external use Call ID optional summary style optional audience Retrieve transcript, metadata, and structured insights. Identify key topics, decisions, risks, and next steps. Format output for the chosen audience. Short or structured call summary with actions and risks Generate deal recap Prompt Help reps and managers get a fast overview of a deal Summarise the current state of a deal based on recent calls and linked CRM context Deal ID or account name optional date range Retrieve calls and meetings linked to the deal. Pull deal metadata and CRM-linked context. Identify recent changes, blockers, risks, and next steps. Summarise into a concise recap. Deal recap with status, blockers, risks, and recommended next steps Prepare weekly recap Prompt Help managers or reps review what changed over a time period Weekly recap of calls, themes, and actions for one rep, team, or account Rep ID team ID or account ID time period Retrieve relevant calls and summaries for the selected time period. Aggregate themes, action items, and changes. Highlight notable risks, opportunities, and follow-ups. Weekly recap with top themes, risks, opportunities, and actions Prepare deal review Workflow Help reps and managers review deal status using multiple sources of context Review a deal before pipeline review or forecast meeting Deal ID optional date range Retrieve all recent calls, emails, and meetings tied to the deal. Pull structured call summaries, score, action items, and key points. Pull linked CRM context such as stage, contacts, and account details. Aggregate open risks, unresolved objections, stakeholder gaps, and next steps. Format into a review-ready structure. Deal review document with current status, risks, stakeholders, blockers, and next steps Weekly team recap Workflow Help managers monitor activity and risk across a group of reps or accounts End-of-week team summary for sales leadership Team ID date range Retrieve calls and structured insights across the team. Group by rep, account, or deal. Identify recurring themes, major risks, strong calls, and missed follow-ups. Highlight calls or deals that need attention. Team-level recap with patterns, key risks, notable wins, and action areas Objection analysis across calls Workflow Help teams identify patterns across multiple conversations Analyse pricing or competitor objections across recent calls Account ID deal ID rep ID keyword/topic date range Search calls matching the topic or keyword. Retrieve summaries, transcripts, and snippets where relevant. Identify recurring objections and how they were handled. Assess whether objections were resolved or remain open. Summarise patterns and examples. Objection analysis with themes, frequency, examples, and suggested follow-up actions Build onboarding playlist Workflow Help enablement teams curate useful learning content faster Build a playlist of strong discovery, demo, or pricing calls for new hires Topic call type rep/team filter optional time period Search for calls matching the selected criteria. Rank or filter calls based on score, topics, or key moments. Identify useful snippets or full recordings. Add selected calls/snippets into a playlist structure. Generate short descriptions for each item. Curated onboarding playlist with selected calls/snippets and descriptions Deal Inspection Skill Skill Give users a consistent, opinionated way to assess a deal Claude reviews a deal using a repeatable Jiminny methodology Deal ID or account name optional review scope Determine whether the request is suitable for a deal inspection. Retrieve relevant calls, summaries, CRM context, stakeholders, and risks. Apply a standard Jiminny review framework to assess deal health. Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs. Present findings in a consistent structure with recommended actions. Structured deal inspection with health assessment, red flags, and recommendations Manager Coaching Skill Skill Help managers review calls consistently and identify coaching moments Analyse a rep’s call and surface strengths, gaps, and examples for coaching Call ID optional rep ID optional coaching focus Retrieve transcript, score, summary, and key moments from the call. Evaluate the conversation against a coaching framework. Identify what went well and where the rep could improve. Suggest moments to clip as snippets. Recommend coaching actions or playlist additions. Coaching review with strengths, gaps, snippet suggestions, and recommended coaching actions CSM / QBR Prep Skill Skill Help CS or AM teams prepare structured customer reviews from conversations Generate an account review or QBR-style summary from recent customer interactions Account ID, optional time period Retrieve all recent calls, emails, and account-linked context. Aggregate themes, requests, blockers, sentiment shifts, and opportunities. Apply a repeatable account review structure. Summarise outcomes, open issues, and suggested next steps. Structured QBR/account review summary with themes, risks, opportunities, and actions Snippet Builder Skill Skill Help users consistently capture and label useful moments from calls Identify useful moments in a call and convert them into reusable snippets Call ID, optional focus area such as pricing, objection, competitor, discovery Retrieve transcript, recording timeline, and call insights. Detect moments matching the requested focus area. Propose snippet start/end times and titles. Tag snippets by type or theme. Optionally save them or add them to a playlist. One or more proposed or saved snippets with title, timing, and tags 💡 Technical details Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate. Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users. Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns. Open Questions Track unresolved issues or decisions that need to be made. Update as answers become available. Question Answer Date Answered Competitor research Competitor MCP Availability Core Capabilities Advanced Capabilities Positioning Key Strength Key Weakness Gong ✅ Mature MCP List/search calls Get transcripts get Call metadata Deal intelligence Scorecards, analytics CRM context Enterprise data layer Deep data + ecosystem integrations Limited opinionated AI workflows Avoma ✅ Mature MCP list_meetings get_meeting get_transcript key topics Deal fields/Deal level analysis Coaching/Scoring CRM context pushing insights into Notion / Confluence AI-ready meeting intelligence Strongest “outputs” layer Mostly pull-based interactions Fathom ✅ MCP list/search meetings get transcript get summary/action items Team/org context Webhooks (event-based triggers) get_weekly_recap aggregated action items across meetings creating webhooks directly from the MCP server Lightweight meeting data access Includes summaries + actions Limited analytics depth Grain ✅ Basic MCP get meetings download transcripts Notes Deals Scorecards Data access utility Simple + accessible Low sophistication, sometimes browser-based Outreach ⚠️ MCP (workflow-focused) Search sequences Emails Meetings (Kaia) account / prospect / opportunity lookups Workflow automation Multi-system orchestration Revenue workflow engine End-to-end GTM workflows Less depth in conversation data Attention ⚠️ Emerging MCP Meeting data Transcripts CRM-linked context AI insights Sales assistant workflows create snippets AI sales assistant + CRM Strong CRM linkage Less focus on external knowledge layer Planhat - Related content Ask Jiminny Anything on Call level Galya Dimitrova Hubspot app Galya Dimitrova Product Vision & Strategy Galya Dimitrova Automated CRM Filling Simona Georgieva (Deactivated) Competitive analysis Galya Dimitrova AI agent ideas Steliyan Georgiev Comments James Graham 9 Apr A few areas around Security & Compliance that need more depth before this moves beyond draft: Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate. Team visibility enforcement should be its own HIGH requirement. The current note under access control (\"ensure MCP only exposes data user has access to\") is doing too much work as a single line. Every MCP tool response (especially search_calls and deal-level aggregations) must be filtered through the same visibility engine as the UI. Semantic search is the riskiest here; it could surface results across team boundaries if not properly scoped. No data minimisation or scoping controls. We're exposing transcripts, CRM fields, contact details with job titles, deal context - significant PII and commercial data leaving the platform. Can customers configure which data types are exposed? Can transcripts be PII-stripped before they leave Jiminny? Given we position GDPR consent as a differentiator, we should be thinking about this. GDPR downstream processing is unaddressed. Piping transcripts to OpenAI/Gemini introduces new data processors. Customers need to acknowledge responsibility for downstream processing during setup. We should also consider whether call participants' recording consent covers onward sharing to third-party AI - it almost certainly doesn't by default. This is one for me to pick up. Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users. Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns. Audit logs should go deeper. Current spec (what data, which system, when) is a start. Add: identity behind the key, specific queries/filters used, and failed access attempts (especially cross-visibility-boundary attempts). Enterprise compliance teams will expect this. Downgrade handling. Doc says Scale tier and above, but doesn't say what happens on downgrade. Keys should auto-deactivate. Happy to help draft a dedicated Security & Compliance section if useful. Galya Dimitrova 15 Apr Thanks for the input @James Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.","depth":20,"bounds":{"left":0.41805187,"top":0.19313647,"width":0.051861703,"height":0.11173184},"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"You said","depth":22,"bounds":{"left":0.36053857,"top":0.19553073,"width":0.019946808,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny MCP Connector","depth":22,"bounds":{"left":0.41805187,"top":0.1963288,"width":0.03125,"height":0.038707104},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Epic","depth":22,"bounds":{"left":0.41805187,"top":0.30806065,"width":0.010305851,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Link to Epik in Jira","depth":22,"bounds":{"left":0.41805187,"top":0.33040702,"width":0.043218084,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Document status","depth":22,"bounds":{"left":0.41805187,"top":0.3527534,"width":0.04105718,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"DRAFT","depth":22,"bounds":{"left":0.41805187,"top":0.37509975,"width":0.016289894,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Objective","depth":22,"bounds":{"left":0.41805187,"top":0.39744613,"width":0.024268618,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.","depth":22,"bounds":{"left":0.41805187,"top":0.4197925,"width":0.051529255,"height":0.17278531},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.","depth":22,"bounds":{"left":0.41805187,"top":0.59856343,"width":0.049700797,"height":0.10574621},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"👤 Target user","depth":22,"bounds":{"left":0.41805187,"top":0.75458896,"width":0.033909574,"height":0.016759777},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Revenue teams using AI tools (Sales, CS, RevOps)","depth":22,"bounds":{"left":0.41805187,"top":0.7773344,"width":0.051030584,"height":0.061053474},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Companies already experimenting with Claude / OpenAI","depth":22,"bounds":{"left":0.41805187,"top":0.8443735,"width":0.04637633,"height":0.061053474},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)","depth":22,"bounds":{"left":0.41805187,"top":0.9114126,"width":0.051363032,"height":0.0885874},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"🤕 Pain point or problem","depth":22,"bounds":{"left":0.41805187,"top":1.0,"width":0.03706782,"height":-0.0897845},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny data is locked in the platform","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"AI tools lack access to high-value conversation context","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Customers must manually export/copy transcripts","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"No easy way to combine calls with CRM + docs + other info - 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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST [URL_WITH_CREDENTIALS] Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.
You said
Jiminny MCP Connector
Epic
Link to Epik in Jira
Document status
DRAFT
Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.
👤 Target user
Revenue teams using AI tools (Sales, CS, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
Build an AI Knowledge Connector (MCP-based) that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides structured insights (summaries, action items, scoring etc.)
Supports deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
Metric
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of MCP/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
User interaction and design
Simple setup flow:
Generate API key / connect MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus: usable by non-technical users
Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
Importance
Notes
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH
For each deal expose:
calls/meetings/emails
Identified risks
contacts related to the deal with their job title
linked account details
all CRM field information that we have in DI + fields used for CRM filling
example - get_deal_summary
request -
{
"tool": "get_deal_summary",
"arguments": {
"deal_name": "ACME"
}
}
responce:
{
"deal_name": "ACME",
"stage": "Negotiation",
"recent_activity": [
"Demo call on 2026-04-01",
"Pricing discussion on 2026-04-03"
],
"risks": [
"Pricing concerns",
"Security review pending"
], "next_steps": [
"Send follow-up to VP Sales",
"Share security documentation"
]
}
Ensure the AI can easily understand the schema of the data coming from Jiminny
HIGH
Models perform much better when tools have:
clear names
rich descriptions
predictable schemas
Provide clear, actionable error states for customers when MCP connections fail
HIGH
Error messages should guide agents toward solutions with specific suggestions and next steps.
Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”
Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”
Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”
Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”
Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”
Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”
No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”
Allow customers to securely control who and what data is accessible externally
HIGH
Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well.
Private meetings shouldn’t be exposed via MCP
HIGH
When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP.
Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows
HIGH
Ensure data is structured to be easily combined with:
CRM (accounts, deals)
external docs
Use consistent identifiers:
account IDs
deal IDs
MCP is available only for Scale tier and above
HIGH
Customers who are on Capture tier should be able to access any of their data through MCP.
This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working.
Provide visibility into how external AI tools access Jiminny data
HIGH
Audit logs:
what data was accessed by which external system when
identity - who made the request
specific queries/filters used
failed access attempts (especially cross-visibility-boundary attempts)
Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it.
example from Atlassian -
Provide customers with example prompts to get started quickly
HIGH
MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments.
See examples in table below.
Expose Jiminny as a Connector in Claude
medium
Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny
Provide customers with example workflows to get started quickly
medium
A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.
See examples in table below.
Allow customers to use predefined Jiminny skills for common workflows...
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71981
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1739
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20
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2026-04-22T14:36:47.666293+00:00
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/Users/lukas/.screenpipe/data/data/2026-04-22/1776 /Users/lukas/.screenpipe/data/data/2026-04-22/1776868607666_m2.jpg...
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Firefox
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Jiminny MCP Connector - Product - Confluence — Wor Jiminny MCP Connector - Product - Confluence — Work...
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True
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jiminny.atlassian.net/wiki/spaces/PROD/pages/37282 jiminny.atlassian.net/wiki/spaces/PROD/pages/3728244737/Jiminny+MCP+Connector...
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monitor_2
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NULL
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NULL
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NULL
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NULL
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST [URL_WITH_CREDENTIALS] Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.
You said
Jiminny MCP Connector
Epic
Link to Epik in Jira
Document status
DRAFT
Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.
👤 Target user
Revenue teams using AI tools (Sales, CS, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
Build an AI Knowledge Connector (MCP-based) that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides structured insights (summaries, action items, scoring etc.)
Supports deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
Metric
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of MCP/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
User interaction and design
Simple setup flow:
Generate API key / connect MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus: usable by non-technical users
Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
Importance
Notes
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH
For each deal expose:
calls/meetings/emails
Identified risks
contacts related to the deal with their job title
linked account details
all CRM field information that we have in DI + fields used for CRM filling
example - get_deal_summary
request -
{
"tool": "get_deal_summary",
"arguments": {
"deal_name": "ACME"
}
}
responce:
{
"deal_name": "ACME",
"stage": "Negotiation",
"recent_activity": [
"Demo call on 2026-04-01",
"Pricing discussion on 2026-04-03"
],
"risks": [
"Pricing concerns",
"Security review pending"
], "next_steps": [
"Send follow-up to VP Sales",
"Share security documentation"
]
}
Ensure the AI can easily understand the schema of the data coming from Jiminny
HIGH
Models perform much better when tools have:
clear names
rich descriptions
predictable schemas
Provide clear, actionable error states for customers when MCP connections fail
HIGH
Error messages should guide agents toward solutions with specific suggestions and next steps.
Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”
Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”
Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”
Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”
Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”
Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”
No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”
Allow customers to securely control who and what data is accessible externally
HIGH
Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well.
Private meetings shouldn’t be exposed via MCP
HIGH
When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP.
Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows
HIGH
Ensure data is structured to be easily combined with:
CRM (accounts, deals)
external docs
Use consistent identifiers:
account IDs
deal IDs
MCP is available only for Scale tier and above
HIGH
Customers who are on Capture tier should be able to access any of their data through MCP.
This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working.
Provide visibility into how external AI tools access Jiminny data
HIGH
Audit logs:
what data was accessed by which external system when
identity - who made the request
specific queries/filters used
failed access attempts (especially cross-visibility-boundary attempts)
Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it.
example from Atlassian -
Provide customers with example prompts to get started quickly
HIGH
MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments.
See examples in table below.
Expose Jiminny as a Connector in Claude
medium
Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny
Provide customers with example workflows to get started quickly
medium
A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.
See examples in table below.
Allow customers to use predefined Jiminny skills for common workflows
medium
Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.
Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com
See examples in table below.
Jiminny UI widget for OpenAI
medium
The reason to have this is to make certain Jiminny use cases much easier than pure chat.
Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.
For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.
Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.
Allow customers to run long-running analyses asynchronously and retrieve progress/results
low
Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to:
start the job
let it run in the background
return a status like “in progress”
let the client come back for the result later
For example - User asks in Claude:
“Analyse all calls from this quarter and tell me the top 5 objections by segment.”
That might require:
searching lots of calls...
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Objective Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows. Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product. 👤 Target user Revenue teams using AI tools (Sales, CS, RevOps) Companies already experimenting with Claude / OpenAI Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc) 🤕 Pain point or problem Jiminny data is locked in the platform AI tools lack access to high-value conversation context Customers must manually export/copy transcripts No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people Peer pressure as almost all competitors have this 💥 Impact and benefits For customers Better AI outputs using real customer conversations Ability to build automated workflows across tools For Jiminny Increased product stickiness (embedded in workflows) Competitive parity with Gong / Avoma etc. 💡 Solution ideas Build an AI Knowledge Connector (MCP-based) that: Exposes Jiminny data (calls, transcripts, metadata) to AI tools Provides structured insights (summaries, action items, scoring etc.) Supports deal-level context Allows integration into customer-owned AI stacks (Claude + Zapier, etc.) Success metrics List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets). Goal Metric Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows % of customers connecting external AI tools Number of MCP/API calls per customer - and which ones are being used the most Decrease the manual copying of transcripts decrease by X % User interaction and design Simple setup flow: Generate API key / connect MCP Clear instructions for Claude / Zapier setup Provide: Example prompts for Claude Pre-built workflow templates for Zapier Clear documentation - with use cases 👉 Focus: usable by non-technical users Detailed Requirements List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification. Requirement Importance Notes Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI) HIGH Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack Allow customers to access call data through AI tools HIGH Expose Jiminny data: list calls (filter by date, user, account, deal) get call get transcript (speaker-separated) get call metadata search calls (keyword + semantic) Support filtering by: account / contact/ lead / deal jiminny user date range Allow customers to search and query calls using AI tools HIGH Support keyword and semantic search across conversations Provide structured insights so customers can generate better outputs HIGH For each call, expose: Summary Action items Key points Activity type Notes Call Score CRM information Allow customers to understand deal status across multiple calls/emails HIGH For each deal expose: calls/meetings/emails Identified risks contacts related to the deal with their job title linked account details all CRM field information that we have in DI + fields used for CRM filling example - get_deal_summary request - { \"tool\": \"get_deal_summary\", \"arguments\": { \"deal_name\": \"ACME\" } } responce: { \"deal_name\": \"ACME\", \"stage\": \"Negotiation\", \"recent_activity\": [ \"Demo call on 2026-04-01\", \"Pricing discussion on 2026-04-03\" ], \"risks\": [ \"Pricing concerns\", \"Security review pending\" ], \"next_steps\": [ \"Send follow-up to VP Sales\", \"Share security documentation\" ] } Ensure the AI can easily understand the schema of the data coming from Jiminny HIGH Models perform much better when tools have: clear names rich descriptions predictable schemas Provide clear, actionable error states for customers when MCP connections fail HIGH Error messages should guide agents toward solutions with specific suggestions and next steps. Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.” Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.” Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.” Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.” Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.” Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.” No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.” Allow customers to securely control who and what data is accessible externally HIGH Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well. Private meetings shouldn’t be exposed via MCP HIGH When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP. Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows HIGH Ensure data is structured to be easily combined with: CRM (accounts, deals) external docs Use consistent identifiers: account IDs deal IDs MCP is available only for Scale tier and above HIGH Customers who are on Capture tier should be able to access any of their data through MCP. This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working. Provide visibility into how external AI tools access Jiminny data HIGH Audit logs: what data was accessed by which external system when identity - who made the request specific queries/filters used failed access attempts (especially cross-visibility-boundary attempts) Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it. example from Atlassian - Provide customers with example prompts to get started quickly HIGH MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments. See examples in table below. Expose Jiminny as a Connector in Claude medium Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny Provide customers with example workflows to get started quickly medium A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome. See examples in table below. Allow customers to use predefined Jiminny skills for common workflows medium Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job. Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com See examples in table below. Jiminny UI widget for OpenAI medium The reason to have this is to make certain Jiminny use cases much easier than pure chat. Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it. For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface. Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section. Allow customers to run long-running analyses asynchronously and retrieve progress/results low Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to: start the job let it run in the background return a status like “in progress” let the client come back for the result later For example - User asks in Claude: “Analyse all calls from this quarter and tell me the top 5 objections by segment.” That might require: searching lots of calls pulling many summaries/transcripts aggregating results Instead of making Claude wait 45+ seconds, Jiminny could: create an analysis job return: job_id = 123, status = running We need this in order to support: large-scale analysis multi-call aggregation across lots of data playlist generation across many calls heavy recap/report generation Creating a snippet through the MCP low Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team. example request: { \"tool\": \"create_snippet\", \"arguments\": { \"call_id\": \"call_123\", \"start_time\": \"00:18:24\", \"end_time\": \"00:19:10\", \"title\": \"Pricing objection\" } } example response: { \"snippet_id\": \"snip_987\", \"title\": \"Pricing objection\", \"share_url\": \"https://app.jiminny.com/snippets/snip_987\" } Ability to add recordings or snippets to a playlist low Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes Allow AI tools to read reusable Jiminny resources low fathom Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be: current deal snapshot account summary weekly rep recap approved talk tracks / objection handling guidance curated playlists or snippet libraries This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls. Ability to write coaching notes in Jiminny through MCP low What we want to do for prompts/worflows and skills Capability Type User value / job to be done Example Jiminny use case Inputs Steps performed Output Draft follow-up email Prompt Help reps quickly create tailored follow-ups after calls Use the latest customer call to draft an email with recap and next steps Call ID optional tone optional recipient type Retrieve call summary and action items from Jiminny. Pull key points, commitments, and next steps. Apply the chosen tone and structure. Draft the email in a consistent format. Customer-ready follow-up email draft Summarise call Prompt Help users quickly extract the most important points from a conversation Generate a concise summary of one call for internal or external use Call ID optional summary style optional audience Retrieve transcript, metadata, and structured insights. Identify key topics, decisions, risks, and next steps. Format output for the chosen audience. Short or structured call summary with actions and risks Generate deal recap Prompt Help reps and managers get a fast overview of a deal Summarise the current state of a deal based on recent calls and linked CRM context Deal ID or account name optional date range Retrieve calls and meetings linked to the deal. Pull deal metadata and CRM-linked context. Identify recent changes, blockers, risks, and next steps. Summarise into a concise recap. Deal recap with status, blockers, risks, and recommended next steps Prepare weekly recap Prompt Help managers or reps review what changed over a time period Weekly recap of calls, themes, and actions for one rep, team, or account Rep ID team ID or account ID time period Retrieve relevant calls and summaries for the selected time period. Aggregate themes, action items, and changes. Highlight notable risks, opportunities, and follow-ups. Weekly recap with top themes, risks, opportunities, and actions Prepare deal review Workflow Help reps and managers review deal status using multiple sources of context Review a deal before pipeline review or forecast meeting Deal ID optional date range Retrieve all recent calls, emails, and meetings tied to the deal. Pull structured call summaries, score, action items, and key points. Pull linked CRM context such as stage, contacts, and account details. Aggregate open risks, unresolved objections, stakeholder gaps, and next steps. Format into a review-ready structure. Deal review document with current status, risks, stakeholders, blockers, and next steps Weekly team recap Workflow Help managers monitor activity and risk across a group of reps or accounts End-of-week team summary for sales leadership Team ID date range Retrieve calls and structured insights across the team. Group by rep, account, or deal. Identify recurring themes, major risks, strong calls, and missed follow-ups. Highlight calls or deals that need attention. Team-level recap with patterns, key risks, notable wins, and action areas Objection analysis across calls Workflow Help teams identify patterns across multiple conversations Analyse pricing or competitor objections across recent calls Account ID deal ID rep ID keyword/topic date range Search calls matching the topic or keyword. Retrieve summaries, transcripts, and snippets where relevant. Identify recurring objections and how they were handled. Assess whether objections were resolved or remain open. Summarise patterns and examples. Objection analysis with themes, frequency, examples, and suggested follow-up actions Build onboarding playlist Workflow Help enablement teams curate useful learning content faster Build a playlist of strong discovery, demo, or pricing calls for new hires Topic call type rep/team filter optional time period Search for calls matching the selected criteria. Rank or filter calls based on score, topics, or key moments. Identify useful snippets or full recordings. Add selected calls/snippets into a playlist structure. Generate short descriptions for each item. Curated onboarding playlist with selected calls/snippets and descriptions Deal Inspection Skill Skill Give users a consistent, opinionated way to assess a deal Claude reviews a deal using a repeatable Jiminny methodology Deal ID or account name optional review scope Determine whether the request is suitable for a deal inspection. Retrieve relevant calls, summaries, CRM context, stakeholders, and risks. Apply a standard Jiminny review framework to assess deal health. Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs. Present findings in a consistent structure with recommended actions. Structured deal inspection with health assessment, red flags, and recommendations Manager Coaching Skill Skill Help managers review calls consistently and identify coaching moments Analyse a rep’s call and surface strengths, gaps, and examples for coaching Call ID optional rep ID optional coaching focus Retrieve transcript, score, summary, and key moments from the call. Evaluate the conversation against a coaching framework. Identify what went well and where the rep could improve. Suggest moments to clip as snippets. Recommend coaching actions or playlist additions. Coaching review with strengths, gaps, snippet suggestions, and recommended coaching actions CSM / QBR Prep Skill Skill Help CS or AM teams prepare structured customer reviews from conversations Generate an account review or QBR-style summary from recent customer interactions Account ID, optional time period Retrieve all recent calls, emails, and account-linked context. Aggregate themes, requests, blockers, sentiment shifts, and opportunities. Apply a repeatable account review structure. Summarise outcomes, open issues, and suggested next steps. Structured QBR/account review summary with themes, risks, opportunities, and actions Snippet Builder Skill Skill Help users consistently capture and label useful moments from calls Identify useful moments in a call and convert them into reusable snippets Call ID, optional focus area such as pricing, objection, competitor, discovery Retrieve transcript, recording timeline, and call insights. Detect moments matching the requested focus area. Propose snippet start/end times and titles. Tag snippets by type or theme. Optionally save them or add them to a playlist. One or more proposed or saved snippets with title, timing, and tags 💡 Technical details Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate. Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users. Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns. Open Questions Track unresolved issues or decisions that need to be made. Update as answers become available. Question Answer Date Answered Competitor research Competitor MCP Availability Core Capabilities Advanced Capabilities Positioning Key Strength Key Weakness Gong ✅ Mature MCP List/search calls Get transcripts get Call metadata Deal intelligence Scorecards, analytics CRM context Enterprise data layer Deep data + ecosystem integrations Limited opinionated AI workflows Avoma ✅ Mature MCP list_meetings get_meeting get_transcript key topics Deal fields/Deal level analysis Coaching/Scoring CRM context pushing insights into Notion / Confluence AI-ready meeting intelligence Strongest “outputs” layer Mostly pull-based interactions Fathom ✅ MCP list/search meetings get transcript get summary/action items Team/org context Webhooks (event-based triggers) get_weekly_recap aggregated action items across meetings creating webhooks directly from the MCP server Lightweight meeting data access Includes summaries + actions Limited analytics depth Grain ✅ Basic MCP get meetings download transcripts Notes Deals Scorecards Data access utility Simple + accessible Low sophistication, sometimes browser-based Outreach ⚠️ MCP (workflow-focused) Search sequences Emails Meetings (Kaia) account / prospect / opportunity lookups Workflow automation Multi-system orchestration Revenue workflow engine End-to-end GTM workflows Less depth in conversation data Attention ⚠️ Emerging MCP Meeting data Transcripts CRM-linked context AI insights Sales assistant workflows create snippets AI sales assistant + CRM Strong CRM linkage Less focus on external knowledge layer Planhat - Related content Ask Jiminny Anything on Call level Galya Dimitrova Hubspot app Galya Dimitrova Product Vision & Strategy Galya Dimitrova Automated CRM Filling Simona Georgieva (Deactivated) Competitive analysis Galya Dimitrova AI agent ideas Steliyan Georgiev Comments James Graham 9 Apr A few areas around Security & Compliance that need more depth before this moves beyond draft: Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate. Team visibility enforcement should be its own HIGH requirement. The current note under access control (\"ensure MCP only exposes data user has access to\") is doing too much work as a single line. Every MCP tool response (especially search_calls and deal-level aggregations) must be filtered through the same visibility engine as the UI. Semantic search is the riskiest here; it could surface results across team boundaries if not properly scoped. No data minimisation or scoping controls. We're exposing transcripts, CRM fields, contact details with job titles, deal context - significant PII and commercial data leaving the platform. Can customers configure which data types are exposed? Can transcripts be PII-stripped before they leave Jiminny? Given we position GDPR consent as a differentiator, we should be thinking about this. GDPR downstream processing is unaddressed. Piping transcripts to OpenAI/Gemini introduces new data processors. Customers need to acknowledge responsibility for downstream processing during setup. We should also consider whether call participants' recording consent covers onward sharing to third-party AI - it almost certainly doesn't by default. This is one for me to pick up. Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users. Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns. Audit logs should go deeper. Current spec (what data, which system, when) is a start. Add: identity behind the key, specific queries/filters used, and failed access attempts (especially cross-visibility-boundary attempts). Enterprise compliance teams will expect this. Downgrade handling. Doc says Scale tier and above, but doesn't say what happens on downgrade. Keys should auto-deactivate. Happy to help draft a dedicated Security & Compliance section if useful. Galya Dimitrova 15 Apr Thanks for the input @James Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.","depth":21,"bounds":{"left":0.41805187,"top":0.19313647,"width":0.051861703,"height":0.11173184},"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"You said","depth":23,"bounds":{"left":0.36053857,"top":0.19553073,"width":0.019946808,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny MCP Connector","depth":23,"bounds":{"left":0.41805187,"top":0.1963288,"width":0.03125,"height":0.038707104},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Epic","depth":23,"bounds":{"left":0.41805187,"top":0.30806065,"width":0.010305851,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Link to Epik in Jira","depth":23,"bounds":{"left":0.41805187,"top":0.33040702,"width":0.043218084,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Document status","depth":23,"bounds":{"left":0.41805187,"top":0.3527534,"width":0.04105718,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"DRAFT","depth":23,"bounds":{"left":0.41805187,"top":0.37509975,"width":0.016289894,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Objective","depth":23,"bounds":{"left":0.41805187,"top":0.39744613,"width":0.024268618,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.","depth":23,"bounds":{"left":0.41805187,"top":0.4197925,"width":0.051529255,"height":0.17278531},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.","depth":23,"bounds":{"left":0.41805187,"top":0.59856343,"width":0.049700797,"height":0.10574621},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"👤 Target user","depth":23,"bounds":{"left":0.41805187,"top":0.75458896,"width":0.033909574,"height":0.016759777},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Revenue teams using AI tools (Sales, CS, RevOps)","depth":23,"bounds":{"left":0.41805187,"top":0.7773344,"width":0.051030584,"height":0.061053474},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Companies already experimenting with Claude / OpenAI","depth":23,"bounds":{"left":0.41805187,"top":0.8443735,"width":0.04637633,"height":0.061053474},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)","depth":23,"bounds":{"left":0.41805187,"top":0.9114126,"width":0.051363032,"height":0.0885874},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"🤕 Pain point or problem","depth":23,"bounds":{"left":0.41805187,"top":1.0,"width":0.03706782,"height":-0.0897845},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny data is locked in the platform","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"AI tools lack access to high-value conversation context","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Customers must manually export/copy transcripts","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"No easy way to combine calls with CRM + docs + other info - 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This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to securely control who and what data is accessible externally","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Private meetings shouldn’t be exposed via MCP","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ensure data is structured to be easily combined with:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"CRM (accounts, deals)","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"external docs","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Use consistent identifiers:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"account IDs","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"deal IDs","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"MCP is available only for Scale tier and above","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Customers who are on Capture tier should be able to access any of their data through MCP.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide visibility into how external AI tools access Jiminny data","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Audit logs:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"what data was accessed by which external system when","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"identity - who made the request","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"specific queries/filters used","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"failed access attempts (especially cross-visibility-boundary attempts)","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"example from Atlassian -","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide customers with example prompts to get started quickly","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"See examples in table below.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Expose Jiminny as a Connector in Claude","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide customers with example workflows to get started quickly","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"See examples in table below.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to use predefined Jiminny skills for common workflows","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"See examples in table below.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny UI widget for OpenAI","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The reason to have this is to make certain Jiminny use cases much easier than pure chat.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to run long-running analyses asynchronously and retrieve progress/results","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"start the job","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"let it run in the background","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"return a status like “in progress”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"let the client come back for the result later","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"For example - User asks in Claude:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"“Analyse all calls from this quarter and tell me the top 5 objections by segment.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"That might require:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"searching lots of calls","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"}]...
|
9080754131051932125
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7412220562332342809
|
click
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accessibility
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NULL
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST [URL_WITH_CREDENTIALS] Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.
You said
Jiminny MCP Connector
Epic
Link to Epik in Jira
Document status
DRAFT
Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.
👤 Target user
Revenue teams using AI tools (Sales, CS, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
Build an AI Knowledge Connector (MCP-based) that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides structured insights (summaries, action items, scoring etc.)
Supports deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
Metric
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of MCP/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
User interaction and design
Simple setup flow:
Generate API key / connect MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus: usable by non-technical users
Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
Importance
Notes
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH
For each deal expose:
calls/meetings/emails
Identified risks
contacts related to the deal with their job title
linked account details
all CRM field information that we have in DI + fields used for CRM filling
example - get_deal_summary
request -
{
"tool": "get_deal_summary",
"arguments": {
"deal_name": "ACME"
}
}
responce:
{
"deal_name": "ACME",
"stage": "Negotiation",
"recent_activity": [
"Demo call on 2026-04-01",
"Pricing discussion on 2026-04-03"
],
"risks": [
"Pricing concerns",
"Security review pending"
], "next_steps": [
"Send follow-up to VP Sales",
"Share security documentation"
]
}
Ensure the AI can easily understand the schema of the data coming from Jiminny
HIGH
Models perform much better when tools have:
clear names
rich descriptions
predictable schemas
Provide clear, actionable error states for customers when MCP connections fail
HIGH
Error messages should guide agents toward solutions with specific suggestions and next steps.
Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”
Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”
Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”
Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”
Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”
Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”
No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”
Allow customers to securely control who and what data is accessible externally
HIGH
Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well.
Private meetings shouldn’t be exposed via MCP
HIGH
When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP.
Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows
HIGH
Ensure data is structured to be easily combined with:
CRM (accounts, deals)
external docs
Use consistent identifiers:
account IDs
deal IDs
MCP is available only for Scale tier and above
HIGH
Customers who are on Capture tier should be able to access any of their data through MCP.
This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working.
Provide visibility into how external AI tools access Jiminny data
HIGH
Audit logs:
what data was accessed by which external system when
identity - who made the request
specific queries/filters used
failed access attempts (especially cross-visibility-boundary attempts)
Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it.
example from Atlassian -
Provide customers with example prompts to get started quickly
HIGH
MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments.
See examples in table below.
Expose Jiminny as a Connector in Claude
medium
Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny
Provide customers with example workflows to get started quickly
medium
A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.
See examples in table below.
Allow customers to use predefined Jiminny skills for common workflows
medium
Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.
Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com
See examples in table below.
Jiminny UI widget for OpenAI
medium
The reason to have this is to make certain Jiminny use cases much easier than pure chat.
Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.
For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.
Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.
Allow customers to run long-running analyses asynchronously and retrieve progress/results
low
Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to:
start the job
let it run in the background
return a status like “in progress”
let the client come back for the result later
For example - User asks in Claude:
“Analyse all calls from this quarter and tell me the top 5 objections by segment.”
That might require:
searching lots of calls...
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1738
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14
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2026-04-22T14:36:48.373685+00:00
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/Users/lukas/.screenpipe/data/data/2026-04-22/1776 /Users/lukas/.screenpipe/data/data/2026-04-22/1776868608373_m1.jpg...
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Firefox
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Jiminny MCP Connector - Product - Confluence — Wor Jiminny MCP Connector - Product - Confluence — Work...
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True
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jiminny.atlassian.net/wiki/spaces/PROD/pages/37282 jiminny.atlassian.net/wiki/spaces/PROD/pages/3728244737/Jiminny+MCP+Connector...
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monitor_1
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NULL
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NULL
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NULL
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NULL
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST [URL_WITH_CREDENTIALS] Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.
You said
Jiminny MCP Connector
Epic
Link to Epik in Jira
Document status
DRAFT
Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.
👤 Target user
Revenue teams using AI tools (Sales, CS, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
Build an AI Knowledge Connector (MCP-based) that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides structured insights (summaries, action items, scoring etc.)
Supports deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
Metric
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of MCP/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
User interaction and design
Simple setup flow:
Generate API key / connect MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus: usable by non-technical users
Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
Importance
Notes
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH
For each deal expose:
calls/meetings/emails
Identified risks
contacts related to the deal with their job title
linked account details
all CRM field information that we have in DI + fields used for CRM filling
example - get_deal_summary
request -
{
"tool": "get_deal_summary",
"arguments": {
"deal_name": "ACME"
}
}
responce:
{
"deal_name": "ACME",
"stage": "Negotiation",
"recent_activity": [
"Demo call on 2026-04-01",
"Pricing discussion on 2026-04-03"
],
"risks": [
"Pricing concerns",
"Security review pending"
], "next_steps": [
"Send follow-up to VP Sales",
"Share security documentation"
]
}
Ensure the AI can easily understand the schema of the data coming from Jiminny
HIGH
Models perform much better when tools have:
clear names
rich descriptions
predictable schemas
Provide clear, actionable error states for customers when MCP connections fail
HIGH
Error messages should guide agents toward solutions with specific suggestions and next steps.
Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”
Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”
Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”
Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”
Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”
Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”
No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”
Allow customers to securely control who and what data is accessible externally
HIGH
Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well.
Private meetings shouldn’t be exposed via MCP
HIGH
When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP.
Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows
HIGH
Ensure data is structured to be easily combined with:
CRM (accounts, deals)
external docs
Use consistent identifiers:
account IDs
deal IDs
MCP is available only for Scale tier and above
HIGH
Customers who are on Capture tier should be able to access any of their data through MCP.
This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working.
Provide visibility into how external AI tools access Jiminny data
HIGH
Audit logs:
what data was accessed by which external system when
identity - who made the request
specific queries/filters used
failed access attempts (especially cross-visibility-boundary attempts)
Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it.
example from Atlassian -
Provide customers with example prompts to get started quickly
HIGH
MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments.
See examples in table below.
Expose Jiminny as a Connector in Claude
medium
Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny
Provide customers with example workflows to get started quickly
medium
A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.
See examples in table below.
Allow customers to use predefined Jiminny skills for common workflows
medium
Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.
Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com
See examples in table below.
Jiminny UI widget for OpenAI
medium
The reason to have this is to make certain Jiminny use cases much easier than pure chat.
Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.
For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.
Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.
Allow customers to run long-running analyses asynchronously and retrieve progress/results
low
Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to:
start the job
let it run in the background
return a status like “in progress”
let the client come back for the result later
For example - User asks in Claude:
“Analyse all calls from this quarter and tell me the top 5 objections by segment.”
That might require:
searching lots of calls
pulling many summaries/transcripts
aggregating results
Instead of making Claude wait 45+ seconds, Jiminny could:
create an analysis job
return: job_id = 123, status = running
We need this in order to support:
large-scale analysis
multi-call aggregation across lots of data
playlist generation across many calls
heavy recap/report generation
Creating a snippet through the MCP
low
Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team.
example request:
{
"tool": "create_snippet",
"arguments": {
"call_id": "call_123",
"start_time": "00:18:24",
"end_time": "00:19:10",
"title": "Pricing objection"
}
}
example response:
{
"snippet_id": "snip_987",
"title": "Pricing objection",
"share_url": "https://app.jiminny.com/snippets/snip_987"
}
Ability to add recordings or snippets to a playlist
low
Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes
Allow AI tools to read reusable Jiminny resources
low
fathom
Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be:
current deal snapshot
account summary
weekly rep recap
approved talk tracks / objection handling guidance
curated playlists or snippet libraries
This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls.
Ability to write coaching notes in Jiminny through MCP
low
What we want to do for prompts/worflows and skills
Capability
Type
User value / job to be done
Example Jiminny use case
Inputs
Steps performed
Output
Draft follow-up email
Prompt
Help reps quickly create tailored follow-ups after calls
Use the latest customer call to draft an email with recap and next steps
Call ID
optional tone
optional recipient type
Retrieve call summary and action items from Jiminny....
|
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Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product. 👤 Target user Revenue teams using AI tools (Sales, CS, RevOps) Companies already experimenting with Claude / OpenAI Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc) 🤕 Pain point or problem Jiminny data is locked in the platform AI tools lack access to high-value conversation context Customers must manually export/copy transcripts No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people Peer pressure as almost all competitors have this 💥 Impact and benefits For customers Better AI outputs using real customer conversations Ability to build automated workflows across tools For Jiminny Increased product stickiness (embedded in workflows) Competitive parity with Gong / Avoma etc. 💡 Solution ideas Build an AI Knowledge Connector (MCP-based) that: Exposes Jiminny data (calls, transcripts, metadata) to AI tools Provides structured insights (summaries, action items, scoring etc.) Supports deal-level context Allows integration into customer-owned AI stacks (Claude + Zapier, etc.) Success metrics List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets). Goal Metric Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows % of customers connecting external AI tools Number of MCP/API calls per customer - and which ones are being used the most Decrease the manual copying of transcripts decrease by X % User interaction and design Simple setup flow: Generate API key / connect MCP Clear instructions for Claude / Zapier setup Provide: Example prompts for Claude Pre-built workflow templates for Zapier Clear documentation - with use cases 👉 Focus: usable by non-technical users Detailed Requirements List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification. Requirement Importance Notes Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI) HIGH Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack Allow customers to access call data through AI tools HIGH Expose Jiminny data: list calls (filter by date, user, account, deal) get call get transcript (speaker-separated) get call metadata search calls (keyword + semantic) Support filtering by: account / contact/ lead / deal jiminny user date range Allow customers to search and query calls using AI tools HIGH Support keyword and semantic search across conversations Provide structured insights so customers can generate better outputs HIGH For each call, expose: Summary Action items Key points Activity type Notes Call Score CRM information Allow customers to understand deal status across multiple calls/emails HIGH For each deal expose: calls/meetings/emails Identified risks contacts related to the deal with their job title linked account details all CRM field information that we have in DI + fields used for CRM filling example - get_deal_summary request - { \"tool\": \"get_deal_summary\", \"arguments\": { \"deal_name\": \"ACME\" } } responce: { \"deal_name\": \"ACME\", \"stage\": \"Negotiation\", \"recent_activity\": [ \"Demo call on 2026-04-01\", \"Pricing discussion on 2026-04-03\" ], \"risks\": [ \"Pricing concerns\", \"Security review pending\" ], \"next_steps\": [ \"Send follow-up to VP Sales\", \"Share security documentation\" ] } Ensure the AI can easily understand the schema of the data coming from Jiminny HIGH Models perform much better when tools have: clear names rich descriptions predictable schemas Provide clear, actionable error states for customers when MCP connections fail HIGH Error messages should guide agents toward solutions with specific suggestions and next steps. Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.” Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.” Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.” Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.” Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.” Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.” No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.” Allow customers to securely control who and what data is accessible externally HIGH Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well. Private meetings shouldn’t be exposed via MCP HIGH When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP. Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows HIGH Ensure data is structured to be easily combined with: CRM (accounts, deals) external docs Use consistent identifiers: account IDs deal IDs MCP is available only for Scale tier and above HIGH Customers who are on Capture tier should be able to access any of their data through MCP. This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working. Provide visibility into how external AI tools access Jiminny data HIGH Audit logs: what data was accessed by which external system when identity - who made the request specific queries/filters used failed access attempts (especially cross-visibility-boundary attempts) Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it. example from Atlassian - Provide customers with example prompts to get started quickly HIGH MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments. See examples in table below. Expose Jiminny as a Connector in Claude medium Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny Provide customers with example workflows to get started quickly medium A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome. See examples in table below. Allow customers to use predefined Jiminny skills for common workflows medium Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job. Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com See examples in table below. Jiminny UI widget for OpenAI medium The reason to have this is to make certain Jiminny use cases much easier than pure chat. Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it. For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface. Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section. Allow customers to run long-running analyses asynchronously and retrieve progress/results low Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to: start the job let it run in the background return a status like “in progress” let the client come back for the result later For example - User asks in Claude: “Analyse all calls from this quarter and tell me the top 5 objections by segment.” That might require: searching lots of calls pulling many summaries/transcripts aggregating results Instead of making Claude wait 45+ seconds, Jiminny could: create an analysis job return: job_id = 123, status = running We need this in order to support: large-scale analysis multi-call aggregation across lots of data playlist generation across many calls heavy recap/report generation Creating a snippet through the MCP low Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team. example request: { \"tool\": \"create_snippet\", \"arguments\": { \"call_id\": \"call_123\", \"start_time\": \"00:18:24\", \"end_time\": \"00:19:10\", \"title\": \"Pricing objection\" } } example response: { \"snippet_id\": \"snip_987\", \"title\": \"Pricing objection\", \"share_url\": \"https://app.jiminny.com/snippets/snip_987\" } Ability to add recordings or snippets to a playlist low Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes Allow AI tools to read reusable Jiminny resources low fathom Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be: current deal snapshot account summary weekly rep recap approved talk tracks / objection handling guidance curated playlists or snippet libraries This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls. Ability to write coaching notes in Jiminny through MCP low What we want to do for prompts/worflows and skills Capability Type User value / job to be done Example Jiminny use case Inputs Steps performed Output Draft follow-up email Prompt Help reps quickly create tailored follow-ups after calls Use the latest customer call to draft an email with recap and next steps Call ID optional tone optional recipient type Retrieve call summary and action items from Jiminny. Pull key points, commitments, and next steps. Apply the chosen tone and structure. Draft the email in a consistent format. Customer-ready follow-up email draft Summarise call Prompt Help users quickly extract the most important points from a conversation Generate a concise summary of one call for internal or external use Call ID optional summary style optional audience Retrieve transcript, metadata, and structured insights. Identify key topics, decisions, risks, and next steps. Format output for the chosen audience. Short or structured call summary with actions and risks Generate deal recap Prompt Help reps and managers get a fast overview of a deal Summarise the current state of a deal based on recent calls and linked CRM context Deal ID or account name optional date range Retrieve calls and meetings linked to the deal. Pull deal metadata and CRM-linked context. Identify recent changes, blockers, risks, and next steps. Summarise into a concise recap. Deal recap with status, blockers, risks, and recommended next steps Prepare weekly recap Prompt Help managers or reps review what changed over a time period Weekly recap of calls, themes, and actions for one rep, team, or account Rep ID team ID or account ID time period Retrieve relevant calls and summaries for the selected time period. Aggregate themes, action items, and changes. Highlight notable risks, opportunities, and follow-ups. Weekly recap with top themes, risks, opportunities, and actions Prepare deal review Workflow Help reps and managers review deal status using multiple sources of context Review a deal before pipeline review or forecast meeting Deal ID optional date range Retrieve all recent calls, emails, and meetings tied to the deal. Pull structured call summaries, score, action items, and key points. Pull linked CRM context such as stage, contacts, and account details. Aggregate open risks, unresolved objections, stakeholder gaps, and next steps. Format into a review-ready structure. Deal review document with current status, risks, stakeholders, blockers, and next steps Weekly team recap Workflow Help managers monitor activity and risk across a group of reps or accounts End-of-week team summary for sales leadership Team ID date range Retrieve calls and structured insights across the team. Group by rep, account, or deal. Identify recurring themes, major risks, strong calls, and missed follow-ups. Highlight calls or deals that need attention. Team-level recap with patterns, key risks, notable wins, and action areas Objection analysis across calls Workflow Help teams identify patterns across multiple conversations Analyse pricing or competitor objections across recent calls Account ID deal ID rep ID keyword/topic date range Search calls matching the topic or keyword. Retrieve summaries, transcripts, and snippets where relevant. Identify recurring objections and how they were handled. Assess whether objections were resolved or remain open. Summarise patterns and examples. Objection analysis with themes, frequency, examples, and suggested follow-up actions Build onboarding playlist Workflow Help enablement teams curate useful learning content faster Build a playlist of strong discovery, demo, or pricing calls for new hires Topic call type rep/team filter optional time period Search for calls matching the selected criteria. Rank or filter calls based on score, topics, or key moments. Identify useful snippets or full recordings. Add selected calls/snippets into a playlist structure. Generate short descriptions for each item. Curated onboarding playlist with selected calls/snippets and descriptions Deal Inspection Skill Skill Give users a consistent, opinionated way to assess a deal Claude reviews a deal using a repeatable Jiminny methodology Deal ID or account name optional review scope Determine whether the request is suitable for a deal inspection. Retrieve relevant calls, summaries, CRM context, stakeholders, and risks. Apply a standard Jiminny review framework to assess deal health. Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs. Present findings in a consistent structure with recommended actions. Structured deal inspection with health assessment, red flags, and recommendations Manager Coaching Skill Skill Help managers review calls consistently and identify coaching moments Analyse a rep’s call and surface strengths, gaps, and examples for coaching Call ID optional rep ID optional coaching focus Retrieve transcript, score, summary, and key moments from the call. Evaluate the conversation against a coaching framework. Identify what went well and where the rep could improve. Suggest moments to clip as snippets. Recommend coaching actions or playlist additions. Coaching review with strengths, gaps, snippet suggestions, and recommended coaching actions CSM / QBR Prep Skill Skill Help CS or AM teams prepare structured customer reviews from conversations Generate an account review or QBR-style summary from recent customer interactions Account ID, optional time period Retrieve all recent calls, emails, and account-linked context. Aggregate themes, requests, blockers, sentiment shifts, and opportunities. Apply a repeatable account review structure. Summarise outcomes, open issues, and suggested next steps. Structured QBR/account review summary with themes, risks, opportunities, and actions Snippet Builder Skill Skill Help users consistently capture and label useful moments from calls Identify useful moments in a call and convert them into reusable snippets Call ID, optional focus area such as pricing, objection, competitor, discovery Retrieve transcript, recording timeline, and call insights. Detect moments matching the requested focus area. Propose snippet start/end times and titles. Tag snippets by type or theme. Optionally save them or add them to a playlist. One or more proposed or saved snippets with title, timing, and tags 💡 Technical details Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate. Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users. Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns. Open Questions Track unresolved issues or decisions that need to be made. Update as answers become available. Question Answer Date Answered Competitor research Competitor MCP Availability Core Capabilities Advanced Capabilities Positioning Key Strength Key Weakness Gong ✅ Mature MCP List/search calls Get transcripts get Call metadata Deal intelligence Scorecards, analytics CRM context Enterprise data layer Deep data + ecosystem integrations Limited opinionated AI workflows Avoma ✅ Mature MCP list_meetings get_meeting get_transcript key topics Deal fields/Deal level analysis Coaching/Scoring CRM context pushing insights into Notion / Confluence AI-ready meeting intelligence Strongest “outputs” layer Mostly pull-based interactions Fathom ✅ MCP list/search meetings get transcript get summary/action items Team/org context Webhooks (event-based triggers) get_weekly_recap aggregated action items across meetings creating webhooks directly from the MCP server Lightweight meeting data access Includes summaries + actions Limited analytics depth Grain ✅ Basic MCP get meetings download transcripts Notes Deals Scorecards Data access utility Simple + accessible Low sophistication, sometimes browser-based Outreach ⚠️ MCP (workflow-focused) Search sequences Emails Meetings (Kaia) account / prospect / opportunity lookups Workflow automation Multi-system orchestration Revenue workflow engine End-to-end GTM workflows Less depth in conversation data Attention ⚠️ Emerging MCP Meeting data Transcripts CRM-linked context AI insights Sales assistant workflows create snippets AI sales assistant + CRM Strong CRM linkage Less focus on external knowledge layer Planhat - Related content Ask Jiminny Anything on Call level Galya Dimitrova Hubspot app Galya Dimitrova Product Vision & Strategy Galya Dimitrova Automated CRM Filling Simona Georgieva (Deactivated) Competitive analysis Galya Dimitrova AI agent ideas Steliyan Georgiev Comments James Graham 9 Apr A few areas around Security & Compliance that need more depth before this moves beyond draft: Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate. Team visibility enforcement should be its own HIGH requirement. The current note under access control (\"ensure MCP only exposes data user has access to\") is doing too much work as a single line. Every MCP tool response (especially search_calls and deal-level aggregations) must be filtered through the same visibility engine as the UI. Semantic search is the riskiest here; it could surface results across team boundaries if not properly scoped. No data minimisation or scoping controls. We're exposing transcripts, CRM fields, contact details with job titles, deal context - significant PII and commercial data leaving the platform. Can customers configure which data types are exposed? Can transcripts be PII-stripped before they leave Jiminny? Given we position GDPR consent as a differentiator, we should be thinking about this. GDPR downstream processing is unaddressed. Piping transcripts to OpenAI/Gemini introduces new data processors. Customers need to acknowledge responsibility for downstream processing during setup. We should also consider whether call participants' recording consent covers onward sharing to third-party AI - it almost certainly doesn't by default. This is one for me to pick up. Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users. Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns. Audit logs should go deeper. Current spec (what data, which system, when) is a start. Add: identity behind the key, specific queries/filters used, and failed access attempts (especially cross-visibility-boundary attempts). Enterprise compliance teams will expect this. Downgrade handling. Doc says Scale tier and above, but doesn't say what happens on downgrade. Keys should auto-deactivate. Happy to help draft a dedicated Security & Compliance section if useful. Galya Dimitrova 15 Apr Thanks for the input @James Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. 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This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to securely control who and what data is accessible externally","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Private meetings shouldn’t be exposed via MCP","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ensure data is structured to be easily combined with:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"CRM (accounts, deals)","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"external docs","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Use consistent identifiers:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"account IDs","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"deal IDs","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"MCP is available only for Scale tier and above","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Customers who are on Capture tier should be able to access any of their data through MCP.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide visibility into how external AI tools access Jiminny data","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Audit logs:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"what data was accessed by which external system when","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"identity - who made the request","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"specific queries/filters used","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"failed access attempts (especially cross-visibility-boundary attempts)","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"example from Atlassian -","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide customers with example prompts to get started quickly","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"See examples in table below.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Expose Jiminny as a Connector in Claude","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide customers with example workflows to get started quickly","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"See examples in table below.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to use predefined Jiminny skills for common workflows","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"See examples in table below.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny UI widget for OpenAI","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The reason to have this is to make certain Jiminny use cases much easier than pure chat.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to run long-running analyses asynchronously and retrieve progress/results","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"start the job","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"let it run in the background","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"return a status like “in progress”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"let the client come back for the result later","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"For example - User asks in Claude:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"“Analyse all calls from this quarter and tell me the top 5 objections by segment.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"That might require:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"searching lots of calls","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"pulling many summaries/transcripts","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"aggregating results","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Instead of making Claude wait 45+ seconds, Jiminny could:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"create an analysis job","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"return: job_id = 123, status = running","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"We need this in order to support:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"large-scale analysis","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"multi-call aggregation across lots of data","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"playlist generation across many calls","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"heavy recap/report generation","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Creating a snippet through the MCP","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"example request:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"{","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"tool\": \"create_snippet\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"arguments\": {","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"call_id\": \"call_123\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"start_time\": \"00:18:24\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"end_time\": \"00:19:10\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"title\": \"Pricing objection\"","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"example response:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"{","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"snippet_id\": \"snip_987\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"title\": \"Pricing objection\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"share_url\": \"https://app.jiminny.com/snippets/snip_987\"","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ability to add recordings or snippets to a playlist","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow AI tools to read reusable Jiminny resources","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"fathom","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"current deal snapshot","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"account summary","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"weekly rep recap","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"approved talk tracks / objection handling guidance","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"curated playlists or snippet libraries","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ability to write coaching notes in Jiminny through MCP","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"What we want to do for prompts/worflows and skills","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Capability","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Type","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"User value / job to be done","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Example Jiminny use case","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Inputs","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Steps performed","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Output","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Draft follow-up email","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Prompt","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Help reps quickly create tailored follow-ups after calls","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Use the latest customer call to draft an email with recap and next steps","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Call ID","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"optional tone","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"optional recipient type","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Retrieve call summary and action items from Jiminny.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"}]...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST [URL_WITH_CREDENTIALS] Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.
You said
Jiminny MCP Connector
Epic
Link to Epik in Jira
Document status
DRAFT
Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.
👤 Target user
Revenue teams using AI tools (Sales, CS, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
Build an AI Knowledge Connector (MCP-based) that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides structured insights (summaries, action items, scoring etc.)
Supports deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
Metric
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of MCP/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
User interaction and design
Simple setup flow:
Generate API key / connect MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus: usable by non-technical users
Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
Importance
Notes
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH
For each deal expose:
calls/meetings/emails
Identified risks
contacts related to the deal with their job title
linked account details
all CRM field information that we have in DI + fields used for CRM filling
example - get_deal_summary
request -
{
"tool": "get_deal_summary",
"arguments": {
"deal_name": "ACME"
}
}
responce:
{
"deal_name": "ACME",
"stage": "Negotiation",
"recent_activity": [
"Demo call on 2026-04-01",
"Pricing discussion on 2026-04-03"
],
"risks": [
"Pricing concerns",
"Security review pending"
], "next_steps": [
"Send follow-up to VP Sales",
"Share security documentation"
]
}
Ensure the AI can easily understand the schema of the data coming from Jiminny
HIGH
Models perform much better when tools have:
clear names
rich descriptions
predictable schemas
Provide clear, actionable error states for customers when MCP connections fail
HIGH
Error messages should guide agents toward solutions with specific suggestions and next steps.
Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”
Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”
Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”
Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”
Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”
Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”
No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”
Allow customers to securely control who and what data is accessible externally
HIGH
Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well.
Private meetings shouldn’t be exposed via MCP
HIGH
When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP.
Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows
HIGH
Ensure data is structured to be easily combined with:
CRM (accounts, deals)
external docs
Use consistent identifiers:
account IDs
deal IDs
MCP is available only for Scale tier and above
HIGH
Customers who are on Capture tier should be able to access any of their data through MCP.
This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working.
Provide visibility into how external AI tools access Jiminny data
HIGH
Audit logs:
what data was accessed by which external system when
identity - who made the request
specific queries/filters used
failed access attempts (especially cross-visibility-boundary attempts)
Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it.
example from Atlassian -
Provide customers with example prompts to get started quickly
HIGH
MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments.
See examples in table below.
Expose Jiminny as a Connector in Claude
medium
Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny
Provide customers with example workflows to get started quickly
medium
A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.
See examples in table below.
Allow customers to use predefined Jiminny skills for common workflows
medium
Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.
Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com
See examples in table below.
Jiminny UI widget for OpenAI
medium
The reason to have this is to make certain Jiminny use cases much easier than pure chat.
Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.
For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.
Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.
Allow customers to run long-running analyses asynchronously and retrieve progress/results
low
Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to:
start the job
let it run in the background
return a status like “in progress”
let the client come back for the result later
For example - User asks in Claude:
“Analyse all calls from this quarter and tell me the top 5 objections by segment.”
That might require:
searching lots of calls
pulling many summaries/transcripts
aggregating results
Instead of making Claude wait 45+ seconds, Jiminny could:
create an analysis job
return: job_id = 123, status = running
We need this in order to support:
large-scale analysis
multi-call aggregation across lots of data
playlist generation across many calls
heavy recap/report generation
Creating a snippet through the MCP
low
Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team.
example request:
{
"tool": "create_snippet",
"arguments": {
"call_id": "call_123",
"start_time": "00:18:24",
"end_time": "00:19:10",
"title": "Pricing objection"
}
}
example response:
{
"snippet_id": "snip_987",
"title": "Pricing objection",
"share_url": "https://app.jiminny.com/snippets/snip_987"
}
Ability to add recordings or snippets to a playlist
low
Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes
Allow AI tools to read reusable Jiminny resources
low
fathom
Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be:
current deal snapshot
account summary
weekly rep recap
approved talk tracks / objection handling guidance
curated playlists or snippet libraries
This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls.
Ability to write coaching notes in Jiminny through MCP
low
What we want to do for prompts/worflows and skills
Capability
Type
User value / job to be done
Example Jiminny use case
Inputs
Steps performed
Output
Draft follow-up email
Prompt
Help reps quickly create tailored follow-ups after calls
Use the latest customer call to draft an email with recap and next steps
Call ID
optional tone
optional recipient type
Retrieve call summary and action items from Jiminny....
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Jiminny MCP Connector - Product - Confluence — Wor Jiminny MCP Connector - Product - Confluence — Work...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST [URL_WITH_CREDENTIALS] Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.
You said
Jiminny MCP Connector
Epic
Link to Epik in Jira
Document status
DRAFT
Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.
👤 Target user
Revenue teams using AI tools (Sales, CS, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
Build an AI Knowledge Connector (MCP-based) that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides structured insights (summaries, action items, scoring etc.)
Supports deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
Metric
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of MCP/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
User interaction and design
Simple setup flow:
Generate API key / connect MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus: usable by non-technical users
Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
Importance
Notes
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH
For each deal expose:
calls/meetings/emails
Identified risks
contacts related to the deal with their job title
linked account details
all CRM field information that we have in DI + fields used for CRM filling
example - get_deal_summary
request -
{
"tool": "get_deal_summary",
"arguments": {
"deal_name": "ACME"
}
}
responce:
{
"deal_name": "ACME",
"stage": "Negotiation",
"recent_activity": [
"Demo call on 2026-04-01",
"Pricing discussion on 2026-04-03"
],
"risks": [
"Pricing concerns",
"Security review pending"
], "next_steps": [
"Send follow-up to VP Sales",
"Share security documentation"
]
}
Ensure the AI can easily understand the schema of the data coming from Jiminny
HIGH
Models perform much better when tools have:
clear names
rich descriptions
predictable schemas
Provide clear, actionable error states for customers when MCP connections fail
HIGH
Error messages should guide agents toward solutions with specific suggestions and next steps.
Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”
Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”
Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”
Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”
Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”
Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”
No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”
Allow customers to securely control who and what data is accessible externally
HIGH
Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well.
Private meetings shouldn’t be exposed via MCP
HIGH
When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP.
Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows
HIGH
Ensure data is structured to be easily combined with:
CRM (accounts, deals)
external docs
Use consistent identifiers:
account IDs
deal IDs
MCP is available only for Scale tier and above
HIGH
Customers who are on Capture tier should be able to access any of their data through MCP.
This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working.
Provide visibility into how external AI tools access Jiminny data
HIGH
Audit logs:
what data was accessed by which external system when
identity - who made the request
specific queries/filters used
failed access attempts (especially cross-visibility-boundary attempts)
Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it.
example from Atlassian -
Provide customers with example prompts to get started quickly
HIGH
MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments.
See examples in table below.
Expose Jiminny as a Connector in Claude
medium
Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny
Provide customers with example workflows to get started quickly
medium
A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.
See examples in table below.
Allow customers to use predefined Jiminny skills for common workflows
medium
Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.
Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com
See examples in table below.
Jiminny UI widget for OpenAI
medium
The reason to have this is to make certain Jiminny use cases much easier than pure chat.
Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.
For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.
Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.
Allow customers to run long-running analyses asynchronously and retrieve progress/results
low
Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to:
start the job
let it run in the background
return a status like “in progress”
let the client come back for the result later
For example - User asks in Claude:
“Analyse all calls from this quarter and tell me the top 5 objections by segment.”
That might require:
searching lots of calls
pulling many summaries/transcripts
aggregating results
Instead of making Claude wait 45+ seconds, Jiminny could:
create an analysis job
return: job_id = 123, status = running
We need this in order to support:
large-scale analysis
multi-call aggregation across lots of data
playlist generation across many calls
heavy recap/report generation
Creating a snippet through the MCP
low
Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team.
example request:
{
"tool": "create_snippet",
"arguments": {
"call_id": "call_123",
"start_time": "00:18:24",
"end_time": "00:19:10",
"title": "Pricing objection"
}
}
example response:
{
"snippet_id": "snip_987",
"title": "Pricing objection",
"share_url": "https://app.jiminny.com/snippets/snip_987"
}
Ability to add recordings or snippets to a playlist
low
Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes
Allow AI tools to read reusable Jiminny resources
low
fathom
Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be:
current deal snapshot
account summary
weekly rep recap
approved talk tracks / objection handling guidance
curated playlists or snippet libraries
This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls.
Ability to write coaching notes in Jiminny through MCP
low
What we want to do for prompts/worflows and skills
Capability
Type
User value / job to be done
Example Jiminny use case
Inputs
Steps performed
Output
Draft follow-up email
Prompt
Help reps quickly create tailored follow-ups after calls
Use the latest customer call to draft an email with recap and next steps
Call ID
optional tone
optional recipient type
Retrieve call summary and action items from Jiminny.
Pull key points, commitments, and next steps.
Apply the chosen tone and structure.
Draft the email in a consistent format.
Customer-ready follow-up email draft
Summarise call
Prompt
Help users quickly extract the most important points from a conversation
Generate a concise summary of one call for internal or external use
Call ID
optional summary style
optional audience
Retrieve transcript, metadata, and structured insights.
Identify key topics, decisions, risks, and next steps.
Format output for the chosen audience.
Short or structured call summary with actions and risks
Generate deal recap
Prompt
Help reps and managers get a fast overview of a deal
Summarise the current state of a deal based on recent calls and linked CRM context
Deal ID or account name
optional date range
Retrieve calls and meetings linked to the deal.
Pull deal metadata and CRM-linked context.
Identify recent changes, blockers, risks, and next steps.
Summarise into a concise recap.
Deal recap with status, blockers, risks, and recommended next steps
Prepare weekly recap
Prompt
Help managers or reps review what changed over a time period
Weekly recap of calls, themes, and actions for one rep, team, or account
Rep ID
team ID or account ID
time period
Retrieve relevant calls and summaries for the selected time period.
Aggregate themes, action items, and changes.
Highlight notable risks, opportunities, and follow-ups.
Weekly recap with top themes, risks, opportunities, and actions
Prepare deal review
Workflow
Help reps and managers review deal status using multiple sources of context
Review a deal before pipeline review or forecast meeting
Deal ID
optional date range
Retrieve all recent calls, emails, and meetings tied to the deal.
Pull structured call summaries, score, action items, and key points.
Pull linked CRM context such as stage, contacts, and account details.
Aggregate open risks, unresolved objections, stakeholder gaps, and next steps.
Format into a review-ready structure.
Deal review document with current status, risks, stakeholders, blockers, and next steps
Weekly team recap
Workflow
Help managers monitor activity and risk across a group of reps or accounts
End-of-week team summary for sales leadership
Team ID
date range
Retrieve calls and structured insights across the team.
Group by rep, account, or deal.
Identify recurring themes, major risks, strong calls, and missed follow-ups.
Highlight calls or deals that need attention.
Team-level recap with patterns, key risks, notable wins, and action areas
Objection analysis across calls
Workflow
Help teams identify patterns across multiple conversations
Analyse pricing or competitor objections across recent calls
Account ID
deal ID
rep ID
keyword/topic
date range
Search calls matching the topic or keyword.
Retrieve summaries, transcripts, and snippets where relevant.
Identify recurring objections and how they were handled.
Assess whether objections were resolved or remain open.
Summarise patterns and examples.
Objection analysis with themes, frequency, examples, and suggested follow-up actions
Build onboarding playlist
Workflow
Help enablement teams curate useful learning content faster
Build a playlist of strong discovery, demo, or pricing calls for new hires
Topic
call type
rep/team filter
optional time period
Search for calls matching the selected criteria.
Rank or filter calls based on score, topics, or key moments.
Identify useful snippets or full recordings.
Add selected calls/snippets into a playlist structure.
Generate short descriptions for each item.
Curated onboarding playlist with selected calls/snippets and descriptions
Deal Inspection Skill
Skill
Give users a consistent, opinionated way to assess a deal
Claude reviews a deal using a repeatable Jiminny methodology
Deal ID or account name
optional review scope
Determine whether the request is suitable for a deal inspection.
Retrieve relevant calls, summaries, CRM context, stakeholders, and risks.
Apply a standard Jiminny review framework to assess deal health.
Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs.
Present findings in a consistent structure with recommended actions.
Structured deal inspection with health assessment, red flags, and recommendations
Manager Coaching Skill
Skill
Help managers review calls consistently and identify coaching moments
Analyse a rep’s call and surface strengths, gaps, and examples for coaching
Call ID
optional rep ID
optional coaching focus
Retrieve transcript, score, summary, and key moments from the call.
Evaluate the conversation against a coaching framework.
Identify what went well and where the rep could improve.
Suggest moments to clip as snippets.
Recommend coaching actions or playlist additions.
Coaching review with strengths, gaps, snippet suggestions, and recommended coaching actions
CSM / QBR Prep Skill
Skill
Help CS or AM teams prepare structured customer reviews from conversations
Generate an account review or QBR-style summary from recent customer interactions
Account ID, optional time period
Retrieve all recent calls, emails, and account-linked context.
Aggregate themes, requests, blockers, sentiment shifts, and opportunities.
Apply a repeatable account review structure.
Summarise outcomes, open issues, and suggested next steps.
Structured QBR/account review summary with themes, risks, opportunities, and actions
Snippet Builder Skill
Skill
Help users consistently capture and label useful moments from calls
Identify useful moments in a call and convert them into reusable snippets
Call ID, optional focus area such as pricing, objection, competitor, discovery
Retrieve transcript, recording timeline, and call insights.
Detect moments matching the requested focus area.
Propose snippet start/end times and titles.
Tag snippets by type or theme.
Optionally save them or add them to a playlist.
One or more proposed or saved snippets with title, timing, and tags...
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Objective Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows. Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product. 👤 Target user Revenue teams using AI tools (Sales, CS, RevOps) Companies already experimenting with Claude / OpenAI Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc) 🤕 Pain point or problem Jiminny data is locked in the platform AI tools lack access to high-value conversation context Customers must manually export/copy transcripts No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people Peer pressure as almost all competitors have this 💥 Impact and benefits For customers Better AI outputs using real customer conversations Ability to build automated workflows across tools For Jiminny Increased product stickiness (embedded in workflows) Competitive parity with Gong / Avoma etc. 💡 Solution ideas Build an AI Knowledge Connector (MCP-based) that: Exposes Jiminny data (calls, transcripts, metadata) to AI tools Provides structured insights (summaries, action items, scoring etc.) Supports deal-level context Allows integration into customer-owned AI stacks (Claude + Zapier, etc.) Success metrics List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets). Goal Metric Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows % of customers connecting external AI tools Number of MCP/API calls per customer - and which ones are being used the most Decrease the manual copying of transcripts decrease by X % User interaction and design Simple setup flow: Generate API key / connect MCP Clear instructions for Claude / Zapier setup Provide: Example prompts for Claude Pre-built workflow templates for Zapier Clear documentation - with use cases 👉 Focus: usable by non-technical users Detailed Requirements List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification. Requirement Importance Notes Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI) HIGH Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack Allow customers to access call data through AI tools HIGH Expose Jiminny data: list calls (filter by date, user, account, deal) get call get transcript (speaker-separated) get call metadata search calls (keyword + semantic) Support filtering by: account / contact/ lead / deal jiminny user date range Allow customers to search and query calls using AI tools HIGH Support keyword and semantic search across conversations Provide structured insights so customers can generate better outputs HIGH For each call, expose: Summary Action items Key points Activity type Notes Call Score CRM information Allow customers to understand deal status across multiple calls/emails HIGH For each deal expose: calls/meetings/emails Identified risks contacts related to the deal with their job title linked account details all CRM field information that we have in DI + fields used for CRM filling example - get_deal_summary request - { \"tool\": \"get_deal_summary\", \"arguments\": { \"deal_name\": \"ACME\" } } responce: { \"deal_name\": \"ACME\", \"stage\": \"Negotiation\", \"recent_activity\": [ \"Demo call on 2026-04-01\", \"Pricing discussion on 2026-04-03\" ], \"risks\": [ \"Pricing concerns\", \"Security review pending\" ], \"next_steps\": [ \"Send follow-up to VP Sales\", \"Share security documentation\" ] } Ensure the AI can easily understand the schema of the data coming from Jiminny HIGH Models perform much better when tools have: clear names rich descriptions predictable schemas Provide clear, actionable error states for customers when MCP connections fail HIGH Error messages should guide agents toward solutions with specific suggestions and next steps. Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.” Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.” Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.” Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.” Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.” Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.” No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.” Allow customers to securely control who and what data is accessible externally HIGH Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well. Private meetings shouldn’t be exposed via MCP HIGH When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP. Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows HIGH Ensure data is structured to be easily combined with: CRM (accounts, deals) external docs Use consistent identifiers: account IDs deal IDs MCP is available only for Scale tier and above HIGH Customers who are on Capture tier should be able to access any of their data through MCP. This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working. Provide visibility into how external AI tools access Jiminny data HIGH Audit logs: what data was accessed by which external system when identity - who made the request specific queries/filters used failed access attempts (especially cross-visibility-boundary attempts) Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it. example from Atlassian - Provide customers with example prompts to get started quickly HIGH MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments. See examples in table below. Expose Jiminny as a Connector in Claude medium Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny Provide customers with example workflows to get started quickly medium A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome. See examples in table below. Allow customers to use predefined Jiminny skills for common workflows medium Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job. Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com See examples in table below. Jiminny UI widget for OpenAI medium The reason to have this is to make certain Jiminny use cases much easier than pure chat. Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it. For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface. Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section. Allow customers to run long-running analyses asynchronously and retrieve progress/results low Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to: start the job let it run in the background return a status like “in progress” let the client come back for the result later For example - User asks in Claude: “Analyse all calls from this quarter and tell me the top 5 objections by segment.” That might require: searching lots of calls pulling many summaries/transcripts aggregating results Instead of making Claude wait 45+ seconds, Jiminny could: create an analysis job return: job_id = 123, status = running We need this in order to support: large-scale analysis multi-call aggregation across lots of data playlist generation across many calls heavy recap/report generation Creating a snippet through the MCP low Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team. example request: { \"tool\": \"create_snippet\", \"arguments\": { \"call_id\": \"call_123\", \"start_time\": \"00:18:24\", \"end_time\": \"00:19:10\", \"title\": \"Pricing objection\" } } example response: { \"snippet_id\": \"snip_987\", \"title\": \"Pricing objection\", \"share_url\": \"https://app.jiminny.com/snippets/snip_987\" } Ability to add recordings or snippets to a playlist low Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes Allow AI tools to read reusable Jiminny resources low fathom Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be: current deal snapshot account summary weekly rep recap approved talk tracks / objection handling guidance curated playlists or snippet libraries This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls. Ability to write coaching notes in Jiminny through MCP low What we want to do for prompts/worflows and skills Capability Type User value / job to be done Example Jiminny use case Inputs Steps performed Output Draft follow-up email Prompt Help reps quickly create tailored follow-ups after calls Use the latest customer call to draft an email with recap and next steps Call ID optional tone optional recipient type Retrieve call summary and action items from Jiminny. Pull key points, commitments, and next steps. Apply the chosen tone and structure. Draft the email in a consistent format. Customer-ready follow-up email draft Summarise call Prompt Help users quickly extract the most important points from a conversation Generate a concise summary of one call for internal or external use Call ID optional summary style optional audience Retrieve transcript, metadata, and structured insights. Identify key topics, decisions, risks, and next steps. Format output for the chosen audience. Short or structured call summary with actions and risks Generate deal recap Prompt Help reps and managers get a fast overview of a deal Summarise the current state of a deal based on recent calls and linked CRM context Deal ID or account name optional date range Retrieve calls and meetings linked to the deal. Pull deal metadata and CRM-linked context. Identify recent changes, blockers, risks, and next steps. Summarise into a concise recap. Deal recap with status, blockers, risks, and recommended next steps Prepare weekly recap Prompt Help managers or reps review what changed over a time period Weekly recap of calls, themes, and actions for one rep, team, or account Rep ID team ID or account ID time period Retrieve relevant calls and summaries for the selected time period. Aggregate themes, action items, and changes. Highlight notable risks, opportunities, and follow-ups. Weekly recap with top themes, risks, opportunities, and actions Prepare deal review Workflow Help reps and managers review deal status using multiple sources of context Review a deal before pipeline review or forecast meeting Deal ID optional date range Retrieve all recent calls, emails, and meetings tied to the deal. Pull structured call summaries, score, action items, and key points. Pull linked CRM context such as stage, contacts, and account details. Aggregate open risks, unresolved objections, stakeholder gaps, and next steps. Format into a review-ready structure. Deal review document with current status, risks, stakeholders, blockers, and next steps Weekly team recap Workflow Help managers monitor activity and risk across a group of reps or accounts End-of-week team summary for sales leadership Team ID date range Retrieve calls and structured insights across the team. Group by rep, account, or deal. Identify recurring themes, major risks, strong calls, and missed follow-ups. Highlight calls or deals that need attention. Team-level recap with patterns, key risks, notable wins, and action areas Objection analysis across calls Workflow Help teams identify patterns across multiple conversations Analyse pricing or competitor objections across recent calls Account ID deal ID rep ID keyword/topic date range Search calls matching the topic or keyword. Retrieve summaries, transcripts, and snippets where relevant. Identify recurring objections and how they were handled. Assess whether objections were resolved or remain open. Summarise patterns and examples. Objection analysis with themes, frequency, examples, and suggested follow-up actions Build onboarding playlist Workflow Help enablement teams curate useful learning content faster Build a playlist of strong discovery, demo, or pricing calls for new hires Topic call type rep/team filter optional time period Search for calls matching the selected criteria. Rank or filter calls based on score, topics, or key moments. Identify useful snippets or full recordings. Add selected calls/snippets into a playlist structure. Generate short descriptions for each item. Curated onboarding playlist with selected calls/snippets and descriptions Deal Inspection Skill Skill Give users a consistent, opinionated way to assess a deal Claude reviews a deal using a repeatable Jiminny methodology Deal ID or account name optional review scope Determine whether the request is suitable for a deal inspection. Retrieve relevant calls, summaries, CRM context, stakeholders, and risks. Apply a standard Jiminny review framework to assess deal health. Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs. Present findings in a consistent structure with recommended actions. Structured deal inspection with health assessment, red flags, and recommendations Manager Coaching Skill Skill Help managers review calls consistently and identify coaching moments Analyse a rep’s call and surface strengths, gaps, and examples for coaching Call ID optional rep ID optional coaching focus Retrieve transcript, score, summary, and key moments from the call. Evaluate the conversation against a coaching framework. Identify what went well and where the rep could improve. Suggest moments to clip as snippets. Recommend coaching actions or playlist additions. Coaching review with strengths, gaps, snippet suggestions, and recommended coaching actions CSM / QBR Prep Skill Skill Help CS or AM teams prepare structured customer reviews from conversations Generate an account review or QBR-style summary from recent customer interactions Account ID, optional time period Retrieve all recent calls, emails, and account-linked context. Aggregate themes, requests, blockers, sentiment shifts, and opportunities. Apply a repeatable account review structure. Summarise outcomes, open issues, and suggested next steps. Structured QBR/account review summary with themes, risks, opportunities, and actions Snippet Builder Skill Skill Help users consistently capture and label useful moments from calls Identify useful moments in a call and convert them into reusable snippets Call ID, optional focus area such as pricing, objection, competitor, discovery Retrieve transcript, recording timeline, and call insights. Detect moments matching the requested focus area. Propose snippet start/end times and titles. Tag snippets by type or theme. Optionally save them or add them to a playlist. One or more proposed or saved snippets with title, timing, and tags 💡 Technical details Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate. Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users. Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns. Open Questions Track unresolved issues or decisions that need to be made. Update as answers become available. Question Answer Date Answered Competitor research Competitor MCP Availability Core Capabilities Advanced Capabilities Positioning Key Strength Key Weakness Gong ✅ Mature MCP List/search calls Get transcripts get Call metadata Deal intelligence Scorecards, analytics CRM context Enterprise data layer Deep data + ecosystem integrations Limited opinionated AI workflows Avoma ✅ Mature MCP list_meetings get_meeting get_transcript key topics Deal fields/Deal level analysis Coaching/Scoring CRM context pushing insights into Notion / Confluence AI-ready meeting intelligence Strongest “outputs” layer Mostly pull-based interactions Fathom ✅ MCP list/search meetings get transcript get summary/action items Team/org context Webhooks (event-based triggers) get_weekly_recap aggregated action items across meetings creating webhooks directly from the MCP server Lightweight meeting data access Includes summaries + actions Limited analytics depth Grain ✅ Basic MCP get meetings download transcripts Notes Deals Scorecards Data access utility Simple + accessible Low sophistication, sometimes browser-based Outreach ⚠️ MCP (workflow-focused) Search sequences Emails Meetings (Kaia) account / prospect / opportunity lookups Workflow automation Multi-system orchestration Revenue workflow engine End-to-end GTM workflows Less depth in conversation data Attention ⚠️ Emerging MCP Meeting data Transcripts CRM-linked context AI insights Sales assistant workflows create snippets AI sales assistant + CRM Strong CRM linkage Less focus on external knowledge layer Planhat - Related content Ask Jiminny Anything on Call level Galya Dimitrova Hubspot app Galya Dimitrova Product Vision & Strategy Galya Dimitrova Automated CRM Filling Simona Georgieva (Deactivated) Competitive analysis Galya Dimitrova AI agent ideas Steliyan Georgiev Comments James Graham 9 Apr A few areas around Security & Compliance that need more depth before this moves beyond draft: Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate. Team visibility enforcement should be its own HIGH requirement. The current note under access control (\"ensure MCP only exposes data user has access to\") is doing too much work as a single line. Every MCP tool response (especially search_calls and deal-level aggregations) must be filtered through the same visibility engine as the UI. Semantic search is the riskiest here; it could surface results across team boundaries if not properly scoped. No data minimisation or scoping controls. We're exposing transcripts, CRM fields, contact details with job titles, deal context - significant PII and commercial data leaving the platform. Can customers configure which data types are exposed? Can transcripts be PII-stripped before they leave Jiminny? Given we position GDPR consent as a differentiator, we should be thinking about this. GDPR downstream processing is unaddressed. Piping transcripts to OpenAI/Gemini introduces new data processors. Customers need to acknowledge responsibility for downstream processing during setup. We should also consider whether call participants' recording consent covers onward sharing to third-party AI - it almost certainly doesn't by default. This is one for me to pick up. Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users. Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns. Audit logs should go deeper. Current spec (what data, which system, when) is a start. Add: identity behind the key, specific queries/filters used, and failed access attempts (especially cross-visibility-boundary attempts). Enterprise compliance teams will expect this. Downgrade handling. Doc says Scale tier and above, but doesn't say what happens on downgrade. Keys should auto-deactivate. Happy to help draft a dedicated Security & Compliance section if useful. Galya Dimitrova 15 Apr Thanks for the input @James Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.","depth":21,"bounds":{"left":0.41805187,"top":0.19313647,"width":0.051861703,"height":0.11173184},"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"You said","depth":23,"bounds":{"left":0.36053857,"top":0.19553073,"width":0.019946808,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny MCP Connector","depth":23,"bounds":{"left":0.41805187,"top":0.1963288,"width":0.03125,"height":0.038707104},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Epic","depth":23,"bounds":{"left":0.41805187,"top":0.30806065,"width":0.010305851,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Link to Epik in Jira","depth":23,"bounds":{"left":0.41805187,"top":0.33040702,"width":0.043218084,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Document status","depth":23,"bounds":{"left":0.41805187,"top":0.3527534,"width":0.04105718,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"DRAFT","depth":23,"bounds":{"left":0.41805187,"top":0.37509975,"width":0.016289894,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Objective","depth":23,"bounds":{"left":0.41805187,"top":0.39744613,"width":0.024268618,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.","depth":23,"bounds":{"left":0.41805187,"top":0.4197925,"width":0.051529255,"height":0.17278531},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.","depth":23,"bounds":{"left":0.41805187,"top":0.59856343,"width":0.049700797,"height":0.10574621},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"👤 Target user","depth":23,"bounds":{"left":0.41805187,"top":0.75458896,"width":0.033909574,"height":0.016759777},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Revenue teams using AI tools (Sales, CS, RevOps)","depth":23,"bounds":{"left":0.41805187,"top":0.7773344,"width":0.051030584,"height":0.061053474},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Companies already experimenting with Claude / OpenAI","depth":23,"bounds":{"left":0.41805187,"top":0.8443735,"width":0.04637633,"height":0.061053474},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)","depth":23,"bounds":{"left":0.41805187,"top":0.9114126,"width":0.051363032,"height":0.0885874},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"🤕 Pain point or problem","depth":23,"bounds":{"left":0.41805187,"top":1.0,"width":0.03706782,"height":-0.0897845},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny data is locked in the platform","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"AI tools lack access to high-value conversation context","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Customers must manually export/copy transcripts","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"No easy way to combine calls with CRM + docs + other info - 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This is not a data or permissions issue — please retry in a few minutes.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to securely control who and what data is accessible externally","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Private meetings shouldn’t be exposed via MCP","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ensure data is structured to be easily combined with:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"CRM (accounts, deals)","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"external docs","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Use consistent identifiers:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"account IDs","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"deal IDs","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"MCP is available only for Scale tier and above","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Customers who are on Capture tier should be able to access any of their data through MCP.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide visibility into how external AI tools access Jiminny data","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Audit logs:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"what data was accessed by which external system when","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"identity - who made the request","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"specific queries/filters used","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"failed access attempts (especially cross-visibility-boundary attempts)","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"example from Atlassian -","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide customers with example prompts to get started quickly","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"See examples in table below.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Expose Jiminny as a Connector in Claude","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide customers with example workflows to get started quickly","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"See examples in table below.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to use predefined Jiminny skills for common workflows","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"See examples in table below.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny UI widget for OpenAI","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The reason to have this is to make certain Jiminny use cases much easier than pure chat.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to run long-running analyses asynchronously and retrieve progress/results","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"start the job","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"let it run in the background","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"return a status like “in progress”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"let the client come back for the result later","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"For example - User asks in Claude:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"“Analyse all calls from this quarter and tell me the top 5 objections by segment.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"That might require:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"searching lots of calls","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"pulling many summaries/transcripts","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"aggregating results","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Instead of making Claude wait 45+ seconds, Jiminny could:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"create an analysis job","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"return: job_id = 123, status = running","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"We need this in order to support:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"large-scale analysis","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"multi-call aggregation across lots of data","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"playlist generation across many calls","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"heavy recap/report generation","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Creating a snippet through the MCP","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"example request:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"{","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"tool\": \"create_snippet\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"arguments\": {","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"call_id\": \"call_123\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"start_time\": \"00:18:24\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"end_time\": \"00:19:10\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"title\": \"Pricing objection\"","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"example response:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"{","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"snippet_id\": \"snip_987\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"title\": \"Pricing objection\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"share_url\": \"https://app.jiminny.com/snippets/snip_987\"","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ability to add recordings or snippets to a playlist","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow AI tools to read reusable Jiminny resources","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"fathom","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"current deal snapshot","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"account summary","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"weekly rep recap","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"approved talk tracks / objection handling guidance","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"curated playlists or snippet libraries","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ability to write coaching notes in Jiminny through MCP","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"What we want to do for prompts/worflows and skills","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Capability","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Type","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"User value / job to be done","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Example Jiminny use 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hires","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Topic","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"call type","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"rep/team filter","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"optional time period","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Search for calls matching the selected criteria.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Rank or filter calls based on score, topics, or key moments.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Identify useful snippets or full recordings.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Add selected calls/snippets into a playlist structure.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Generate short descriptions for each item.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Curated onboarding playlist with selected calls/snippets and descriptions","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Deal Inspection Skill","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Skill","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Give users a consistent, opinionated way to assess a deal","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude reviews a deal using a repeatable Jiminny methodology","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Deal ID or account name","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"optional review scope","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Determine whether the request is suitable for a deal inspection.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Retrieve relevant calls, summaries, CRM context, stakeholders, and risks.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Apply a standard Jiminny review framework to assess deal health.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Present findings in a consistent structure with recommended actions.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Structured deal inspection with health assessment, red flags, and recommendations","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Manager Coaching Skill","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Skill","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Help managers review calls consistently and identify coaching moments","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Analyse a rep’s call and surface strengths, gaps, and examples for coaching","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Call ID","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"optional rep ID","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"optional coaching focus","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Retrieve transcript, score, summary, and key moments from the call.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Evaluate the conversation against a coaching framework.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Identify what went well and where the rep could improve.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Suggest moments to clip as snippets.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Recommend coaching actions or playlist additions.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Coaching review with strengths, gaps, snippet suggestions, and recommended coaching actions","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"CSM / QBR Prep Skill","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Skill","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Help CS or AM teams prepare structured customer reviews from conversations","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Generate an account review or QBR-style summary from recent customer interactions","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Account ID, optional time period","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Retrieve all recent calls, emails, and account-linked context.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Aggregate themes, requests, blockers, sentiment shifts, and opportunities.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Apply a repeatable account review structure.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Summarise outcomes, open issues, and suggested next steps.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Structured QBR/account review summary with themes, risks, opportunities, and actions","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Snippet Builder Skill","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Skill","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Help users consistently capture and label useful moments from calls","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Identify useful moments in a call and convert them into reusable snippets","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Call ID, optional focus area such as pricing, objection, competitor, discovery","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Retrieve transcript, recording timeline, and call insights.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Detect moments matching the requested focus area.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Propose snippet start/end times and titles.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Tag snippets by type or theme.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Optionally save them or add them to a playlist.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"One or more proposed or saved snippets with title, timing, and tags","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"}]...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST [URL_WITH_CREDENTIALS] Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.
You said
Jiminny MCP Connector
Epic
Link to Epik in Jira
Document status
DRAFT
Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.
👤 Target user
Revenue teams using AI tools (Sales, CS, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
Build an AI Knowledge Connector (MCP-based) that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides structured insights (summaries, action items, scoring etc.)
Supports deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
Metric
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of MCP/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
User interaction and design
Simple setup flow:
Generate API key / connect MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus: usable by non-technical users
Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
Importance
Notes
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH
For each deal expose:
calls/meetings/emails
Identified risks
contacts related to the deal with their job title
linked account details
all CRM field information that we have in DI + fields used for CRM filling
example - get_deal_summary
request -
{
"tool": "get_deal_summary",
"arguments": {
"deal_name": "ACME"
}
}
responce:
{
"deal_name": "ACME",
"stage": "Negotiation",
"recent_activity": [
"Demo call on 2026-04-01",
"Pricing discussion on 2026-04-03"
],
"risks": [
"Pricing concerns",
"Security review pending"
], "next_steps": [
"Send follow-up to VP Sales",
"Share security documentation"
]
}
Ensure the AI can easily understand the schema of the data coming from Jiminny
HIGH
Models perform much better when tools have:
clear names
rich descriptions
predictable schemas
Provide clear, actionable error states for customers when MCP connections fail
HIGH
Error messages should guide agents toward solutions with specific suggestions and next steps.
Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”
Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”
Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”
Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”
Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”
Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”
No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”
Allow customers to securely control who and what data is accessible externally
HIGH
Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well.
Private meetings shouldn’t be exposed via MCP
HIGH
When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP.
Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows
HIGH
Ensure data is structured to be easily combined with:
CRM (accounts, deals)
external docs
Use consistent identifiers:
account IDs
deal IDs
MCP is available only for Scale tier and above
HIGH
Customers who are on Capture tier should be able to access any of their data through MCP.
This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working.
Provide visibility into how external AI tools access Jiminny data
HIGH
Audit logs:
what data was accessed by which external system when
identity - who made the request
specific queries/filters used
failed access attempts (especially cross-visibility-boundary attempts)
Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it.
example from Atlassian -
Provide customers with example prompts to get started quickly
HIGH
MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments.
See examples in table below.
Expose Jiminny as a Connector in Claude
medium
Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny
Provide customers with example workflows to get started quickly
medium
A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.
See examples in table below.
Allow customers to use predefined Jiminny skills for common workflows
medium
Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.
Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com
See examples in table below.
Jiminny UI widget for OpenAI
medium
The reason to have this is to make certain Jiminny use cases much easier than pure chat.
Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.
For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.
Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.
Allow customers to run long-running analyses asynchronously and retrieve progress/results
low
Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to:
start the job
let it run in the background
return a status like “in progress”
let the client come back for the result later
For example - User asks in Claude:
“Analyse all calls from this quarter and tell me the top 5 objections by segment.”
That might require:
searching lots of calls
pulling many summaries/transcripts
aggregating results
Instead of making Claude wait 45+ seconds, Jiminny could:
create an analysis job
return: job_id = 123, status = running
We need this in order to support:
large-scale analysis
multi-call aggregation across lots of data
playlist generation across many calls
heavy recap/report generation
Creating a snippet through the MCP
low
Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team.
example request:
{
"tool": "create_snippet",
"arguments": {
"call_id": "call_123",
"start_time": "00:18:24",
"end_time": "00:19:10",
"title": "Pricing objection"
}
}
example response:
{
"snippet_id": "snip_987",
"title": "Pricing objection",
"share_url": "https://app.jiminny.com/snippets/snip_987"
}
Ability to add recordings or snippets to a playlist
low
Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes
Allow AI tools to read reusable Jiminny resources
low
fathom
Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be:
current deal snapshot
account summary
weekly rep recap
approved talk tracks / objection handling guidance
curated playlists or snippet libraries
This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls.
Ability to write coaching notes in Jiminny through MCP
low
What we want to do for prompts/worflows and skills
Capability
Type
User value / job to be done
Example Jiminny use case
Inputs
Steps performed
Output
Draft follow-up email
Prompt
Help reps quickly create tailored follow-ups after calls
Use the latest customer call to draft an email with recap and next steps
Call ID
optional tone
optional recipient type
Retrieve call summary and action items from Jiminny.
Pull key points, commitments, and next steps.
Apply the chosen tone and structure.
Draft the email in a consistent format.
Customer-ready follow-up email draft
Summarise call
Prompt
Help users quickly extract the most important points from a conversation
Generate a concise summary of one call for internal or external use
Call ID
optional summary style
optional audience
Retrieve transcript, metadata, and structured insights.
Identify key topics, decisions, risks, and next steps.
Format output for the chosen audience.
Short or structured call summary with actions and risks
Generate deal recap
Prompt
Help reps and managers get a fast overview of a deal
Summarise the current state of a deal based on recent calls and linked CRM context
Deal ID or account name
optional date range
Retrieve calls and meetings linked to the deal.
Pull deal metadata and CRM-linked context.
Identify recent changes, blockers, risks, and next steps.
Summarise into a concise recap.
Deal recap with status, blockers, risks, and recommended next steps
Prepare weekly recap
Prompt
Help managers or reps review what changed over a time period
Weekly recap of calls, themes, and actions for one rep, team, or account
Rep ID
team ID or account ID
time period
Retrieve relevant calls and summaries for the selected time period.
Aggregate themes, action items, and changes.
Highlight notable risks, opportunities, and follow-ups.
Weekly recap with top themes, risks, opportunities, and actions
Prepare deal review
Workflow
Help reps and managers review deal status using multiple sources of context
Review a deal before pipeline review or forecast meeting
Deal ID
optional date range
Retrieve all recent calls, emails, and meetings tied to the deal.
Pull structured call summaries, score, action items, and key points.
Pull linked CRM context such as stage, contacts, and account details.
Aggregate open risks, unresolved objections, stakeholder gaps, and next steps.
Format into a review-ready structure.
Deal review document with current status, risks, stakeholders, blockers, and next steps
Weekly team recap
Workflow
Help managers monitor activity and risk across a group of reps or accounts
End-of-week team summary for sales leadership
Team ID
date range
Retrieve calls and structured insights across the team.
Group by rep, account, or deal.
Identify recurring themes, major risks, strong calls, and missed follow-ups.
Highlight calls or deals that need attention.
Team-level recap with patterns, key risks, notable wins, and action areas
Objection analysis across calls
Workflow
Help teams identify patterns across multiple conversations
Analyse pricing or competitor objections across recent calls
Account ID
deal ID
rep ID
keyword/topic
date range
Search calls matching the topic or keyword.
Retrieve summaries, transcripts, and snippets where relevant.
Identify recurring objections and how they were handled.
Assess whether objections were resolved or remain open.
Summarise patterns and examples.
Objection analysis with themes, frequency, examples, and suggested follow-up actions
Build onboarding playlist
Workflow
Help enablement teams curate useful learning content faster
Build a playlist of strong discovery, demo, or pricing calls for new hires
Topic
call type
rep/team filter
optional time period
Search for calls matching the selected criteria.
Rank or filter calls based on score, topics, or key moments.
Identify useful snippets or full recordings.
Add selected calls/snippets into a playlist structure.
Generate short descriptions for each item.
Curated onboarding playlist with selected calls/snippets and descriptions
Deal Inspection Skill
Skill
Give users a consistent, opinionated way to assess a deal
Claude reviews a deal using a repeatable Jiminny methodology
Deal ID or account name
optional review scope
Determine whether the request is suitable for a deal inspection.
Retrieve relevant calls, summaries, CRM context, stakeholders, and risks.
Apply a standard Jiminny review framework to assess deal health.
Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs.
Present findings in a consistent structure with recommended actions.
Structured deal inspection with health assessment, red flags, and recommendations
Manager Coaching Skill
Skill
Help managers review calls consistently and identify coaching moments
Analyse a rep’s call and surface strengths, gaps, and examples for coaching
Call ID
optional rep ID
optional coaching focus
Retrieve transcript, score, summary, and key moments from the call.
Evaluate the conversation against a coaching framework.
Identify what went well and where the rep could improve.
Suggest moments to clip as snippets.
Recommend coaching actions or playlist additions.
Coaching review with strengths, gaps, snippet suggestions, and recommended coaching actions
CSM / QBR Prep Skill
Skill
Help CS or AM teams prepare structured customer reviews from conversations
Generate an account review or QBR-style summary from recent customer interactions
Account ID, optional time period
Retrieve all recent calls, emails, and account-linked context.
Aggregate themes, requests, blockers, sentiment shifts, and opportunities.
Apply a repeatable account review structure.
Summarise outcomes, open issues, and suggested next steps.
Structured QBR/account review summary with themes, risks, opportunities, and actions
Snippet Builder Skill
Skill
Help users consistently capture and label useful moments from calls
Identify useful moments in a call and convert them into reusable snippets
Call ID, optional focus area such as pricing, objection, competitor, discovery
Retrieve transcript, recording timeline, and call insights.
Detect moments matching the requested focus area.
Propose snippet start/end times and titles.
Tag snippets by type or theme.
Optionally save them or add them to a playlist.
One or more proposed or saved snippets with title, timing, and tags...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST [URL_WITH_CREDENTIALS] Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.
You said
Jiminny MCP Connector
Epic
Link to Epik in Jira
Document status
DRAFT
Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.
👤 Target user
Revenue teams using AI tools (Sales, CS, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
Build an AI Knowledge Connector (MCP-based) that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides structured insights (summaries, action items, scoring etc.)
Supports deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
Metric
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of MCP/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
User interaction and design
Simple setup flow:
Generate API key / connect MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus: usable by non-technical users
Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
Importance
Notes
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH
For each deal expose:
calls/meetings/emails
Identified risks
contacts related to the deal with their job title
linked account details
all CRM field information that we have in DI + fields used for CRM filling
example - get_deal_summary
request -
{
"tool": "get_deal_summary",
"arguments": {
"deal_name": "ACME"
}
}
responce:
{
"deal_name": "ACME",
"stage": "Negotiation",
"recent_activity": [
"Demo call on 2026-04-01",
"Pricing discussion on 2026-04-03"
],
"risks": [
"Pricing concerns",
"Security review pending"
], "next_steps": [
"Send follow-up to VP Sales",
"Share security documentation"
]
}
Ensure the AI can easily understand the schema of the data coming from Jiminny
HIGH
Models perform much better when tools have:
clear names
rich descriptions
predictable schemas
Provide clear, actionable error states for customers when MCP connections fail
HIGH
Error messages should guide agents toward solutions with specific suggestions and next steps.
Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”
Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”
Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”
Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”
Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”
Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”
No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”
Allow customers to securely control who and what data is accessible externally
HIGH
Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well.
Private meetings shouldn’t be exposed via MCP
HIGH
When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP.
Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows
HIGH
Ensure data is structured to be easily combined with:
CRM (accounts, deals)
external docs
Use consistent identifiers:
account IDs
deal IDs
MCP is available only for Scale tier and above
HIGH
Customers who are on Capture tier should be able to access any of their data through MCP.
This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working.
Provide visibility into how external AI tools access Jiminny data
HIGH
Audit logs:
what data was accessed by which external system when
identity - who made the request
specific queries/filters used
failed access attempts (especially cross-visibility-boundary attempts)
Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it.
example from Atlassian -
Provide customers with example prompts to get started quickly
HIGH
MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments.
See examples in table below.
Expose Jiminny as a Connector in Claude
medium
Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny
Provide customers with example workflows to get started quickly
medium
A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.
See examples in table below.
Allow customers to use predefined Jiminny skills for common workflows
medium
Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.
Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com
See examples in table below.
Jiminny UI widget for OpenAI
medium
The reason to have this is to make certain Jiminny use cases much easier than pure chat.
Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.
For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.
Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.
Allow customers to run long-running analyses asynchronously and retrieve progress/results
low
Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to:
start the job
let it run in the background
return a status like “in progress”
let the client come back for the result later
For example - User asks in Claude:
“Analyse all calls from this quarter and tell me the top 5 objections by segment.”
That might require:
searching lots of calls
pulling many summaries/transcripts
aggregating results
Instead of making Claude wait 45+ seconds, Jiminny could:
create an analysis job
return: job_id = 123, status = running
We need this in order to support:
large-scale analysis
multi-call aggregation across lots of data
playlist generation across many calls
heavy recap/report generation
Creating a snippet through the MCP
low
Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team.
example request:
{
"tool": "create_snippet",
"arguments": {
"call_id": "call_123",
"start_time": "00:18:24",
"end_time": "00:19:10",
"title": "Pricing objection"
}
}
example response:
{
"snippet_id": "snip_987",
"title": "Pricing objection",
"share_url": "https://app.jiminny.com/snippets/snip_987"
}
Ability to add recordings or snippets to a playlist
low
Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes
Allow AI tools to read reusable Jiminny resources
low
fathom
Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be:
current deal snapshot
account summary
weekly rep recap
approved talk tracks / objection handling guidance
curated playlists or snippet libraries
This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls.
Ability to write coaching notes in Jiminny through MCP
low
What we want to do for prompts/worflows and skills
Capability
Type
User value / job to be done
Example Jiminny use case
Inputs
Steps performed
Output
Draft follow-up email
Prompt
Help reps quickly create tailored follow-ups after calls
Use the latest customer call to draft an email with recap and next steps
Call ID
optional tone
optional recipient type
Retrieve call summary and action items from Jiminny.
Pull key points, commitments, and next steps.
Apply the chosen tone and structure.
Draft the email in a consistent format.
Customer-ready follow-up email draft
Summarise call
Prompt
Help users quickly extract the most important points from a conversation
Generate a concise summary of one call for internal or external use
Call ID
optional summary style
optional audience
Retrieve transcript, metadata, and structured insights.
Identify key topics, decisions, risks, and next steps.
Format output for the chosen audience.
Short or structured call summary with actions and risks
Generate deal recap
Prompt
Help reps and managers get a fast overview of a deal
Summarise the current state of a deal based on recent calls and linked CRM context
Deal ID or account name
optional date range
Retrieve calls and meetings linked to the deal.
Pull deal metadata and CRM-linked context.
Identify recent changes, blockers, risks, and next steps.
Summarise into a concise recap.
Deal recap with status, blockers, risks, and recommended next steps
Prepare weekly recap
Prompt
Help managers or reps review what changed over a time period
Weekly recap of calls, themes, and actions for one rep, team, or account
Rep ID
team ID or account ID
time period
Retrieve relevant calls and summaries for the selected time period.
Aggregate themes, action items, and changes.
Highlight notable risks, opportunities, and follow-ups.
Weekly recap with top themes, risks, opportunities, and actions
Prepare deal review
Workflow
Help reps and managers review deal status using multiple sources of context
Review a deal before pipeline review or forecast meeting
Deal ID
optional date range
Retrieve all recent calls, emails, and meetings tied to the deal.
Pull structured call summaries, score, action items, and key points.
Pull linked CRM context such as stage, contacts, and account details.
Aggregate open risks, unresolved objections, stakeholder gaps, and next steps.
Format into a review-ready structure.
Deal review document with current status, risks, stakeholders, blockers, and next steps
Weekly team recap
Workflow
Help managers monitor activity and risk across a group of reps or accounts
End-of-week team summary for sales leadership
Team ID
date range
Retrieve calls and structured insights across the team.
Group by rep, account, or deal.
Identify recurring themes, major risks, strong calls, and missed follow-ups.
Highlight calls or deals that need attention.
Team-level recap with patterns, key risks, notable wins, and action areas
Objection analysis across calls
Workflow
Help teams identify patterns across multiple conversations
Analyse pricing or competitor objections across recent calls
Account ID
deal ID
rep ID
keyword/topic
date range
Search calls matching the topic or keyword.
Retrieve summaries, transcripts, and snippets where relevant.
Identify recurring objections and how they were handled.
Assess whether objections were resolved or remain open.
Summarise patterns and examples.
Objection analysis with themes, frequency, examples, and suggested follow-up actions
Build onboarding playlist
Workflow
Help enablement teams curate useful learning content faster
Build a playlist of strong discovery, demo, or pricing calls for new hires
Topic
call type
rep/team filter
optional time period
Search for calls matching the selected criteria.
Rank or filter calls based on score, topics, or key moments.
Identify useful snippets or full recordings.
Add selected calls/snippets into a playlist structure.
Generate short descriptions for each item.
Curated onboarding playlist with selected calls/snippets and descriptions
Deal Inspection Skill
Skill
Give users a consistent, opinionated way to assess a deal
Claude reviews a deal using a repeatable Jiminny methodology
Deal ID or account name
optional review scope
Determine whether the request is suitable for a deal inspection.
Retrieve relevant calls, summaries, CRM context, stakeholders, and risks.
Apply a standard Jiminny review framework to assess deal health.
Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs.
Present findings in a consistent structure with recommended actions.
Structured deal inspection with health assessment, red flags, and recommendations
Manager Coaching Skill
Skill
Help managers review calls consistently and identify coaching moments
Analyse a rep’s call and surface strengths, gaps, and examples for coaching
Call ID
optional rep ID
optional coaching focus
Retrieve transcript, score, summary, and key moments from the call.
Evaluate the conversation against a coaching framework.
Identify what went well and where the rep could improve.
Suggest moments to clip as snippets.
Recommend coaching actions or playlist additions.
Coaching review with strengths, gaps, snippet suggestions, and recommended coaching actions
CSM / QBR Prep Skill
Skill
Help CS or AM teams prepare structured customer reviews from conversations...
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Objective Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows. Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product. 👤 Target user Revenue teams using AI tools (Sales, CS, RevOps) Companies already experimenting with Claude / OpenAI Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc) 🤕 Pain point or problem Jiminny data is locked in the platform AI tools lack access to high-value conversation context Customers must manually export/copy transcripts No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people Peer pressure as almost all competitors have this 💥 Impact and benefits For customers Better AI outputs using real customer conversations Ability to build automated workflows across tools For Jiminny Increased product stickiness (embedded in workflows) Competitive parity with Gong / Avoma etc. 💡 Solution ideas Build an AI Knowledge Connector (MCP-based) that: Exposes Jiminny data (calls, transcripts, metadata) to AI tools Provides structured insights (summaries, action items, scoring etc.) Supports deal-level context Allows integration into customer-owned AI stacks (Claude + Zapier, etc.) Success metrics List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets). Goal Metric Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows % of customers connecting external AI tools Number of MCP/API calls per customer - and which ones are being used the most Decrease the manual copying of transcripts decrease by X % User interaction and design Simple setup flow: Generate API key / connect MCP Clear instructions for Claude / Zapier setup Provide: Example prompts for Claude Pre-built workflow templates for Zapier Clear documentation - with use cases 👉 Focus: usable by non-technical users Detailed Requirements List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification. Requirement Importance Notes Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI) HIGH Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack Allow customers to access call data through AI tools HIGH Expose Jiminny data: list calls (filter by date, user, account, deal) get call get transcript (speaker-separated) get call metadata search calls (keyword + semantic) Support filtering by: account / contact/ lead / deal jiminny user date range Allow customers to search and query calls using AI tools HIGH Support keyword and semantic search across conversations Provide structured insights so customers can generate better outputs HIGH For each call, expose: Summary Action items Key points Activity type Notes Call Score CRM information Allow customers to understand deal status across multiple calls/emails HIGH For each deal expose: calls/meetings/emails Identified risks contacts related to the deal with their job title linked account details all CRM field information that we have in DI + fields used for CRM filling example - get_deal_summary request - { \"tool\": \"get_deal_summary\", \"arguments\": { \"deal_name\": \"ACME\" } } responce: { \"deal_name\": \"ACME\", \"stage\": \"Negotiation\", \"recent_activity\": [ \"Demo call on 2026-04-01\", \"Pricing discussion on 2026-04-03\" ], \"risks\": [ \"Pricing concerns\", \"Security review pending\" ], \"next_steps\": [ \"Send follow-up to VP Sales\", \"Share security documentation\" ] } Ensure the AI can easily understand the schema of the data coming from Jiminny HIGH Models perform much better when tools have: clear names rich descriptions predictable schemas Provide clear, actionable error states for customers when MCP connections fail HIGH Error messages should guide agents toward solutions with specific suggestions and next steps. Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.” Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.” Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.” Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.” Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.” Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.” No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.” Allow customers to securely control who and what data is accessible externally HIGH Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well. Private meetings shouldn’t be exposed via MCP HIGH When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP. Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows HIGH Ensure data is structured to be easily combined with: CRM (accounts, deals) external docs Use consistent identifiers: account IDs deal IDs MCP is available only for Scale tier and above HIGH Customers who are on Capture tier should be able to access any of their data through MCP. This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working. Provide visibility into how external AI tools access Jiminny data HIGH Audit logs: what data was accessed by which external system when identity - who made the request specific queries/filters used failed access attempts (especially cross-visibility-boundary attempts) Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it. example from Atlassian - Provide customers with example prompts to get started quickly HIGH MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments. See examples in table below. Expose Jiminny as a Connector in Claude medium Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny Provide customers with example workflows to get started quickly medium A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome. See examples in table below. Allow customers to use predefined Jiminny skills for common workflows medium Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job. Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com See examples in table below. Jiminny UI widget for OpenAI medium The reason to have this is to make certain Jiminny use cases much easier than pure chat. Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it. For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface. Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section. Allow customers to run long-running analyses asynchronously and retrieve progress/results low Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to: start the job let it run in the background return a status like “in progress” let the client come back for the result later For example - User asks in Claude: “Analyse all calls from this quarter and tell me the top 5 objections by segment.” That might require: searching lots of calls pulling many summaries/transcripts aggregating results Instead of making Claude wait 45+ seconds, Jiminny could: create an analysis job return: job_id = 123, status = running We need this in order to support: large-scale analysis multi-call aggregation across lots of data playlist generation across many calls heavy recap/report generation Creating a snippet through the MCP low Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team. example request: { \"tool\": \"create_snippet\", \"arguments\": { \"call_id\": \"call_123\", \"start_time\": \"00:18:24\", \"end_time\": \"00:19:10\", \"title\": \"Pricing objection\" } } example response: { \"snippet_id\": \"snip_987\", \"title\": \"Pricing objection\", \"share_url\": \"https://app.jiminny.com/snippets/snip_987\" } Ability to add recordings or snippets to a playlist low Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes Allow AI tools to read reusable Jiminny resources low fathom Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be: current deal snapshot account summary weekly rep recap approved talk tracks / objection handling guidance curated playlists or snippet libraries This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls. Ability to write coaching notes in Jiminny through MCP low What we want to do for prompts/worflows and skills Capability Type User value / job to be done Example Jiminny use case Inputs Steps performed Output Draft follow-up email Prompt Help reps quickly create tailored follow-ups after calls Use the latest customer call to draft an email with recap and next steps Call ID optional tone optional recipient type Retrieve call summary and action items from Jiminny. Pull key points, commitments, and next steps. Apply the chosen tone and structure. Draft the email in a consistent format. Customer-ready follow-up email draft Summarise call Prompt Help users quickly extract the most important points from a conversation Generate a concise summary of one call for internal or external use Call ID optional summary style optional audience Retrieve transcript, metadata, and structured insights. Identify key topics, decisions, risks, and next steps. Format output for the chosen audience. Short or structured call summary with actions and risks Generate deal recap Prompt Help reps and managers get a fast overview of a deal Summarise the current state of a deal based on recent calls and linked CRM context Deal ID or account name optional date range Retrieve calls and meetings linked to the deal. Pull deal metadata and CRM-linked context. Identify recent changes, blockers, risks, and next steps. Summarise into a concise recap. Deal recap with status, blockers, risks, and recommended next steps Prepare weekly recap Prompt Help managers or reps review what changed over a time period Weekly recap of calls, themes, and actions for one rep, team, or account Rep ID team ID or account ID time period Retrieve relevant calls and summaries for the selected time period. Aggregate themes, action items, and changes. Highlight notable risks, opportunities, and follow-ups. Weekly recap with top themes, risks, opportunities, and actions Prepare deal review Workflow Help reps and managers review deal status using multiple sources of context Review a deal before pipeline review or forecast meeting Deal ID optional date range Retrieve all recent calls, emails, and meetings tied to the deal. Pull structured call summaries, score, action items, and key points. Pull linked CRM context such as stage, contacts, and account details. Aggregate open risks, unresolved objections, stakeholder gaps, and next steps. Format into a review-ready structure. Deal review document with current status, risks, stakeholders, blockers, and next steps Weekly team recap Workflow Help managers monitor activity and risk across a group of reps or accounts End-of-week team summary for sales leadership Team ID date range Retrieve calls and structured insights across the team. Group by rep, account, or deal. Identify recurring themes, major risks, strong calls, and missed follow-ups. Highlight calls or deals that need attention. Team-level recap with patterns, key risks, notable wins, and action areas Objection analysis across calls Workflow Help teams identify patterns across multiple conversations Analyse pricing or competitor objections across recent calls Account ID deal ID rep ID keyword/topic date range Search calls matching the topic or keyword. Retrieve summaries, transcripts, and snippets where relevant. Identify recurring objections and how they were handled. Assess whether objections were resolved or remain open. Summarise patterns and examples. Objection analysis with themes, frequency, examples, and suggested follow-up actions Build onboarding playlist Workflow Help enablement teams curate useful learning content faster Build a playlist of strong discovery, demo, or pricing calls for new hires Topic call type rep/team filter optional time period Search for calls matching the selected criteria. Rank or filter calls based on score, topics, or key moments. Identify useful snippets or full recordings. Add selected calls/snippets into a playlist structure. Generate short descriptions for each item. Curated onboarding playlist with selected calls/snippets and descriptions Deal Inspection Skill Skill Give users a consistent, opinionated way to assess a deal Claude reviews a deal using a repeatable Jiminny methodology Deal ID or account name optional review scope Determine whether the request is suitable for a deal inspection. Retrieve relevant calls, summaries, CRM context, stakeholders, and risks. Apply a standard Jiminny review framework to assess deal health. Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs. Present findings in a consistent structure with recommended actions. Structured deal inspection with health assessment, red flags, and recommendations Manager Coaching Skill Skill Help managers review calls consistently and identify coaching moments Analyse a rep’s call and surface strengths, gaps, and examples for coaching Call ID optional rep ID optional coaching focus Retrieve transcript, score, summary, and key moments from the call. Evaluate the conversation against a coaching framework. Identify what went well and where the rep could improve. Suggest moments to clip as snippets. Recommend coaching actions or playlist additions. Coaching review with strengths, gaps, snippet suggestions, and recommended coaching actions CSM / QBR Prep Skill Skill Help CS or AM teams prepare structured customer reviews from conversations Generate an account review or QBR-style summary from recent customer interactions Account ID, optional time period Retrieve all recent calls, emails, and account-linked context. Aggregate themes, requests, blockers, sentiment shifts, and opportunities. Apply a repeatable account review structure. Summarise outcomes, open issues, and suggested next steps. Structured QBR/account review summary with themes, risks, opportunities, and actions Snippet Builder Skill Skill Help users consistently capture and label useful moments from calls Identify useful moments in a call and convert them into reusable snippets Call ID, optional focus area such as pricing, objection, competitor, discovery Retrieve transcript, recording timeline, and call insights. Detect moments matching the requested focus area. Propose snippet start/end times and titles. Tag snippets by type or theme. Optionally save them or add them to a playlist. One or more proposed or saved snippets with title, timing, and tags 💡 Technical details Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate. Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users. Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns. Open Questions Track unresolved issues or decisions that need to be made. Update as answers become available. Question Answer Date Answered Competitor research Competitor MCP Availability Core Capabilities Advanced Capabilities Positioning Key Strength Key Weakness Gong ✅ Mature MCP List/search calls Get transcripts get Call metadata Deal intelligence Scorecards, analytics CRM context Enterprise data layer Deep data + ecosystem integrations Limited opinionated AI workflows Avoma ✅ Mature MCP list_meetings get_meeting get_transcript key topics Deal fields/Deal level analysis Coaching/Scoring CRM context pushing insights into Notion / Confluence AI-ready meeting intelligence Strongest “outputs” layer Mostly pull-based interactions Fathom ✅ MCP list/search meetings get transcript get summary/action items Team/org context Webhooks (event-based triggers) get_weekly_recap aggregated action items across meetings creating webhooks directly from the MCP server Lightweight meeting data access Includes summaries + actions Limited analytics depth Grain ✅ Basic MCP get meetings download transcripts Notes Deals Scorecards Data access utility Simple + accessible Low sophistication, sometimes browser-based Outreach ⚠️ MCP (workflow-focused) Search sequences Emails Meetings (Kaia) account / prospect / opportunity lookups Workflow automation Multi-system orchestration Revenue workflow engine End-to-end GTM workflows Less depth in conversation data Attention ⚠️ Emerging MCP Meeting data Transcripts CRM-linked context AI insights Sales assistant workflows create snippets AI sales assistant + CRM Strong CRM linkage Less focus on external knowledge layer Planhat - Related content Ask Jiminny Anything on Call level Galya Dimitrova Hubspot app Galya Dimitrova Product Vision & Strategy Galya Dimitrova Automated CRM Filling Simona Georgieva (Deactivated) Competitive analysis Galya Dimitrova AI agent ideas Steliyan Georgiev Comments James Graham 9 Apr A few areas around Security & Compliance that need more depth before this moves beyond draft: Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate. Team visibility enforcement should be its own HIGH requirement. The current note under access control (\"ensure MCP only exposes data user has access to\") is doing too much work as a single line. Every MCP tool response (especially search_calls and deal-level aggregations) must be filtered through the same visibility engine as the UI. Semantic search is the riskiest here; it could surface results across team boundaries if not properly scoped. No data minimisation or scoping controls. We're exposing transcripts, CRM fields, contact details with job titles, deal context - significant PII and commercial data leaving the platform. Can customers configure which data types are exposed? Can transcripts be PII-stripped before they leave Jiminny? Given we position GDPR consent as a differentiator, we should be thinking about this. GDPR downstream processing is unaddressed. Piping transcripts to OpenAI/Gemini introduces new data processors. Customers need to acknowledge responsibility for downstream processing during setup. We should also consider whether call participants' recording consent covers onward sharing to third-party AI - it almost certainly doesn't by default. This is one for me to pick up. Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users. Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns. Audit logs should go deeper. Current spec (what data, which system, when) is a start. Add: identity behind the key, specific queries/filters used, and failed access attempts (especially cross-visibility-boundary attempts). Enterprise compliance teams will expect this. Downgrade handling. Doc says Scale tier and above, but doesn't say what happens on downgrade. Keys should auto-deactivate. Happy to help draft a dedicated Security & Compliance section if useful. Galya Dimitrova 15 Apr Thanks for the input @James Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.","depth":21,"bounds":{"left":0.41805187,"top":0.19313647,"width":0.051861703,"height":0.11173184},"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"You said","depth":23,"bounds":{"left":0.36053857,"top":0.19553073,"width":0.019946808,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny MCP Connector","depth":23,"bounds":{"left":0.41805187,"top":0.1963288,"width":0.03125,"height":0.038707104},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Epic","depth":23,"bounds":{"left":0.41805187,"top":0.30806065,"width":0.010305851,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Link to Epik in Jira","depth":23,"bounds":{"left":0.41805187,"top":0.33040702,"width":0.043218084,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Document status","depth":23,"bounds":{"left":0.41805187,"top":0.3527534,"width":0.04105718,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"DRAFT","depth":23,"bounds":{"left":0.41805187,"top":0.37509975,"width":0.016289894,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Objective","depth":23,"bounds":{"left":0.41805187,"top":0.39744613,"width":0.024268618,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.","depth":23,"bounds":{"left":0.41805187,"top":0.4197925,"width":0.051529255,"height":0.17278531},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.","depth":23,"bounds":{"left":0.41805187,"top":0.59856343,"width":0.049700797,"height":0.10574621},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"👤 Target user","depth":23,"bounds":{"left":0.41805187,"top":0.75458896,"width":0.033909574,"height":0.016759777},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Revenue teams using AI tools (Sales, CS, RevOps)","depth":23,"bounds":{"left":0.41805187,"top":0.7773344,"width":0.051030584,"height":0.061053474},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Companies already experimenting with Claude / OpenAI","depth":23,"bounds":{"left":0.41805187,"top":0.8443735,"width":0.04637633,"height":0.061053474},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)","depth":23,"bounds":{"left":0.41805187,"top":0.9114126,"width":0.051363032,"height":0.0885874},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"🤕 Pain point or problem","depth":23,"bounds":{"left":0.41805187,"top":1.0,"width":0.03706782,"height":-0.0897845},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny data is locked in the platform","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"AI tools lack access to high-value conversation context","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Customers must manually export/copy transcripts","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Peer pressure as almost all competitors have this","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"💥 Impact and benefits","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"For customers","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Better AI outputs using real customer conversations","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ability to build automated workflows across tools","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"For Jiminny","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Increased product stickiness (embedded in workflows)","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Competitive parity with Gong / Avoma etc.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"💡 Solution ideas","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Build an AI Knowledge Connector (MCP-based) that:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Exposes Jiminny data (calls, transcripts, metadata) to AI tools","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provides structured insights (summaries, action items, scoring etc.)","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Supports deal-level context","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Success metrics","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"List the key goals and how you will measure success. 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The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Trying to access private meeting - “Error: Access denied. 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This is not a data or permissions issue — please retry in a few minutes.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to securely control who and what data is accessible externally","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ensure MCP only exposes data user has access to - based on team visibility settings. 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This will make it easier for customers to find us and to connect Jiminny","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide customers with example workflows to get started quickly","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"See examples in table below.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to use predefined Jiminny skills for common workflows","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"See examples in table below.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny UI widget for OpenAI","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The reason to have this is to make certain Jiminny use cases much easier than pure chat.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to run long-running analyses asynchronously and retrieve progress/results","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Some requests/questions require analysis of Hugh volumes of data. 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Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"example request:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"{","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"tool\": \"create_snippet\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"arguments\": {","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"call_id\": \"call_123\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"start_time\": \"00:18:24\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"end_time\": \"00:19:10\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"title\": \"Pricing objection\"","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"example response:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"{","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"snippet_id\": \"snip_987\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"title\": \"Pricing objection\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"share_url\": \"https://app.jiminny.com/snippets/snip_987\"","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ability to add recordings or snippets to a playlist","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow AI tools to read reusable Jiminny resources","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"fathom","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"current deal snapshot","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"account summary","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"weekly rep recap","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"approved talk tracks / objection handling guidance","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"curated playlists or snippet libraries","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ability to write coaching notes in Jiminny through MCP","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"What we want to do for prompts/worflows and skills","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Capability","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Type","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"User value / job to be done","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Example Jiminny use 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health.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Present findings in a consistent structure with recommended actions.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Structured deal inspection with health assessment, red flags, and recommendations","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Manager Coaching Skill","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Skill","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Help managers review calls consistently and identify coaching 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framework.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Identify what went well and where the rep could improve.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Suggest moments to clip as snippets.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Recommend coaching actions or playlist additions.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Coaching review with strengths, gaps, snippet suggestions, and recommended coaching actions","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"CSM / QBR Prep Skill","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Skill","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Help CS or AM teams prepare structured customer reviews from conversations","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"}]...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST [URL_WITH_CREDENTIALS] Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.
You said
Jiminny MCP Connector
Epic
Link to Epik in Jira
Document status
DRAFT
Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.
👤 Target user
Revenue teams using AI tools (Sales, CS, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
Build an AI Knowledge Connector (MCP-based) that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides structured insights (summaries, action items, scoring etc.)
Supports deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
Metric
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of MCP/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
User interaction and design
Simple setup flow:
Generate API key / connect MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus: usable by non-technical users
Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
Importance
Notes
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH
For each deal expose:
calls/meetings/emails
Identified risks
contacts related to the deal with their job title
linked account details
all CRM field information that we have in DI + fields used for CRM filling
example - get_deal_summary
request -
{
"tool": "get_deal_summary",
"arguments": {
"deal_name": "ACME"
}
}
responce:
{
"deal_name": "ACME",
"stage": "Negotiation",
"recent_activity": [
"Demo call on 2026-04-01",
"Pricing discussion on 2026-04-03"
],
"risks": [
"Pricing concerns",
"Security review pending"
], "next_steps": [
"Send follow-up to VP Sales",
"Share security documentation"
]
}
Ensure the AI can easily understand the schema of the data coming from Jiminny
HIGH
Models perform much better when tools have:
clear names
rich descriptions
predictable schemas
Provide clear, actionable error states for customers when MCP connections fail
HIGH
Error messages should guide agents toward solutions with specific suggestions and next steps.
Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”
Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”
Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”
Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”
Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”
Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”
No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”
Allow customers to securely control who and what data is accessible externally
HIGH
Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well.
Private meetings shouldn’t be exposed via MCP
HIGH
When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP.
Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows
HIGH
Ensure data is structured to be easily combined with:
CRM (accounts, deals)
external docs
Use consistent identifiers:
account IDs
deal IDs
MCP is available only for Scale tier and above
HIGH
Customers who are on Capture tier should be able to access any of their data through MCP.
This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working.
Provide visibility into how external AI tools access Jiminny data
HIGH
Audit logs:
what data was accessed by which external system when
identity - who made the request
specific queries/filters used
failed access attempts (especially cross-visibility-boundary attempts)
Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it.
example from Atlassian -
Provide customers with example prompts to get started quickly
HIGH
MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments.
See examples in table below.
Expose Jiminny as a Connector in Claude
medium
Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny
Provide customers with example workflows to get started quickly
medium
A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.
See examples in table below.
Allow customers to use predefined Jiminny skills for common workflows
medium
Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.
Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com
See examples in table below.
Jiminny UI widget for OpenAI
medium
The reason to have this is to make certain Jiminny use cases much easier than pure chat.
Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.
For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.
Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.
Allow customers to run long-running analyses asynchronously and retrieve progress/results
low
Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to:
start the job
let it run in the background
return a status like “in progress”
let the client come back for the result later
For example - User asks in Claude:
“Analyse all calls from this quarter and tell me the top 5 objections by segment.”
That might require:
searching lots of calls
pulling many summaries/transcripts
aggregating results
Instead of making Claude wait 45+ seconds, Jiminny could:
create an analysis job
return: job_id = 123, status = running
We need this in order to support:
large-scale analysis
multi-call aggregation across lots of data
playlist generation across many calls
heavy recap/report generation
Creating a snippet through the MCP
low
Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team.
example request:
{
"tool": "create_snippet",
"arguments": {
"call_id": "call_123",
"start_time": "00:18:24",
"end_time": "00:19:10",
"title": "Pricing objection"
}
}
example response:
{
"snippet_id": "snip_987",
"title": "Pricing objection",
"share_url": "https://app.jiminny.com/snippets/snip_987"
}
Ability to add recordings or snippets to a playlist
low
Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes
Allow AI tools to read reusable Jiminny resources
low
fathom
Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be:
current deal snapshot
account summary
weekly rep recap
approved talk tracks / objection handling guidance
curated playlists or snippet libraries
This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls.
Ability to write coaching notes in Jiminny through MCP
low
What we want to do for prompts/worflows and skills
Capability
Type
User value / job to be done
Example Jiminny use case
Inputs
Steps performed
Output
Draft follow-up email
Prompt
Help reps quickly create tailored follow-ups after calls
Use the latest customer call to draft an email with recap and next steps
Call ID
optional tone
optional recipient type
Retrieve call summary and action items from Jiminny.
Pull key points, commitments, and next steps.
Apply the chosen tone and structure.
Draft the email in a consistent format.
Customer-ready follow-up email draft
Summarise call
Prompt
Help users quickly extract the most important points from a conversation
Generate a concise summary of one call for internal or external use
Call ID
optional summary style
optional audience
Retrieve transcript, metadata, and structured insights.
Identify key topics, decisions, risks, and next steps.
Format output for the chosen audience.
Short or structured call summary with actions and risks
Generate deal recap
Prompt
Help reps and managers get a fast overview of a deal
Summarise the current state of a deal based on recent calls and linked CRM context
Deal ID or account name
optional date range
Retrieve calls and meetings linked to the deal.
Pull deal metadata and CRM-linked context.
Identify recent changes, blockers, risks, and next steps.
Summarise into a concise recap.
Deal recap with status, blockers, risks, and recommended next steps
Prepare weekly recap
Prompt
Help managers or reps review what changed over a time period
Weekly recap of calls, themes, and actions for one rep, team, or account
Rep ID
team ID or account ID
time period
Retrieve relevant calls and summaries for the selected time period.
Aggregate themes, action items, and changes.
Highlight notable risks, opportunities, and follow-ups.
Weekly recap with top themes, risks, opportunities, and actions
Prepare deal review
Workflow
Help reps and managers review deal status using multiple sources of context
Review a deal before pipeline review or forecast meeting
Deal ID
optional date range
Retrieve all recent calls, emails, and meetings tied to the deal.
Pull structured call summaries, score, action items, and key points.
Pull linked CRM context such as stage, contacts, and account details.
Aggregate open risks, unresolved objections, stakeholder gaps, and next steps.
Format into a review-ready structure.
Deal review document with current status, risks, stakeholders, blockers, and next steps
Weekly team recap
Workflow
Help managers monitor activity and risk across a group of reps or accounts
End-of-week team summary for sales leadership
Team ID
date range
Retrieve calls and structured insights across the team.
Group by rep, account, or deal.
Identify recurring themes, major risks, strong calls, and missed follow-ups.
Highlight calls or deals that need attention.
Team-level recap with patterns, key risks, notable wins, and action areas
Objection analysis across calls
Workflow
Help teams identify patterns across multiple conversations
Analyse pricing or competitor objections across recent calls
Account ID
deal ID
rep ID
keyword/topic
date range
Search calls matching the topic or keyword.
Retrieve summaries, transcripts, and snippets where relevant.
Identify recurring objections and how they were handled.
Assess whether objections were resolved or remain open.
Summarise patterns and examples.
Objection analysis with themes, frequency, examples, and suggested follow-up actions
Build onboarding playlist
Workflow
Help enablement teams curate useful learning content faster
Build a playlist of strong discovery, demo, or pricing calls for new hires
Topic
call type
rep/team filter
optional time period
Search for calls matching the selected criteria.
Rank or filter calls based on score, topics, or key moments.
Identify useful snippets or full recordings.
Add selected calls/snippets into a playlist structure.
Generate short descriptions for each item.
Curated onboarding playlist with selected calls/snippets and descriptions
Deal Inspection Skill
Skill
Give users a consistent, opinionated way to assess a deal
Claude reviews a deal using a repeatable Jiminny methodology
Deal ID or account name
optional review scope
Determine whether the request is suitable for a deal inspection.
Retrieve relevant calls, summaries, CRM context, stakeholders, and risks.
Apply a standard Jiminny review framework to assess deal health.
Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs.
Present findings in a consistent structure with recommended actions.
Structured deal inspection with health assessment, red flags, and recommendations
Manager Coaching Skill
Skill
Help managers review calls consistently and identify coaching moments
Analyse a rep’s call and surface strengths, gaps, and examples for coaching
Call ID
optional rep ID
optional coaching focus
Retrieve transcript, score, summary, and key moments from the call.
Evaluate the conversation against a coaching framework.
Identify what went well and where the rep could improve.
Suggest moments to clip as snippets.
Recommend coaching actions or playlist additions.
Coaching review with strengths, gaps, snippet suggestions, and recommended coaching actions
CSM / QBR Prep Skill
Skill
Help CS or AM teams prepare structured customer reviews from conversations...
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2026-04-22T14:37:20.543322+00:00
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jiminny.atlassian.net/wiki/spaces/PROD/pages/37282 jiminny.atlassian.net/wiki/spaces/PROD/pages/3728244737/Jiminny+MCP+Connector...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST [URL_WITH_CREDENTIALS] Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.
You said
Jiminny MCP Connector
Epic
Link to Epik in Jira
Document status
DRAFT
Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.
👤 Target user
Revenue teams using AI tools (Sales, CS, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
Build an AI Knowledge Connector (MCP-based) that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides structured insights (summaries, action items, scoring etc.)
Supports deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
Metric
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of MCP/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
User interaction and design
Simple setup flow:
Generate API key / connect MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus: usable by non-technical users
Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
Importance
Notes
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH
For each deal expose:
calls/meetings/emails
Identified risks
contacts related to the deal with their job title
linked account details
all CRM field information that we have in DI + fields used for CRM filling
example - get_deal_summary
request -
{
"tool": "get_deal_summary",
"arguments": {
"deal_name": "ACME"
}
}
responce:
{
"deal_name": "ACME",
"stage": "Negotiation",
"recent_activity": [
"Demo call on 2026-04-01",
"Pricing discussion on 2026-04-03"
],
"risks": [
"Pricing concerns",
"Security review pending"
], "next_steps": [
"Send follow-up to VP Sales",
"Share security documentation"
]
}
Ensure the AI can easily understand the schema of the data coming from Jiminny
HIGH
Models perform much better when tools have:
clear names
rich descriptions
predictable schemas
Provide clear, actionable error states for customers when MCP connections fail
HIGH
Error messages should guide agents toward solutions with specific suggestions and next steps.
Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”
Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”
Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”
Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”
Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”
Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”
No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”
Allow customers to securely control who and what data is accessible externally
HIGH
Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well.
Private meetings shouldn’t be exposed via MCP
HIGH
When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP.
Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows
HIGH
Ensure data is structured to be easily combined with:
CRM (accounts, deals)
external docs
Use consistent identifiers:
account IDs
deal IDs
MCP is available only for Scale tier and above
HIGH
Customers who are on Capture tier should be able to access any of their data through MCP.
This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working.
Provide visibility into how external AI tools access Jiminny data
HIGH
Audit logs:
what data was accessed by which external system when
identity - who made the request
specific queries/filters used
failed access attempts (especially cross-visibility-boundary attempts)
Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it.
example from Atlassian -
Provide customers with example prompts to get started quickly
HIGH
MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments.
See examples in table below.
Expose Jiminny as a Connector in Claude
medium
Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny
Provide customers with example workflows to get started quickly
medium
A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.
See examples in table below.
Allow customers to use predefined Jiminny skills for common workflows
medium
Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.
Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com
See examples in table below.
Jiminny UI widget for OpenAI
medium
The reason to have this is to make certain Jiminny use cases much easier than pure chat.
Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.
For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.
Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.
Allow customers to run long-running analyses asynchronously and retrieve progress/results
low
Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to:
start the job
let it run in the background
return a status like “in progress”
let the client come back for the result later
For example - User asks in Claude:
“Analyse all calls from this quarter and tell me the top 5 objections by segment.”
That might require:
searching lots of calls
pulling many summaries/transcripts
aggregating results
Instead of making Claude wait 45+ seconds, Jiminny could:
create an analysis job
return: job_id = 123, status = running
We need this in order to support:
large-scale analysis
multi-call aggregation across lots of data
playlist generation across many calls
heavy recap/report generation
Creating a snippet through the MCP
low
Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team.
example request:
{
"tool": "create_snippet",
"arguments": {
"call_id": "call_123",
"start_time": "00:18:24",
"end_time": "00:19:10",
"title": "Pricing objection"
}
}
example response:
{
"snippet_id": "snip_987",
"title": "Pricing objection",
"share_url": "https://app.jiminny.com/snippets/snip_987"
}
Ability to add recordings or snippets to a playlist
low
Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes
Allow AI tools to read reusable Jiminny resources
low
fathom
Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be:
current deal snapshot
account summary
weekly rep recap
approved talk tracks / objection handling guidance
curated playlists or snippet libraries
This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls.
Ability to write coaching notes in Jiminny through MCP
low
What we want to do for prompts/worflows and skills
Capability
Type
User value / job to be done
Example Jiminny use case
Inputs
Steps performed
Output
Draft follow-up email
Prompt
Help reps quickly create tailored follow-ups after calls
Use the latest customer call to draft an email with recap and next steps
Call ID
optional tone
optional recipient type
Retrieve call summary and action items from Jiminny.
Pull key points, commitments, and next steps.
Apply the chosen tone and structure.
Draft the email in a consistent format.
Customer-ready follow-up email draft
Summarise call
Prompt
Help users quickly extract the most important points from a conversation
Generate a concise summary of one call for internal or external use
Call ID
optional summary style
optional audience
Retrieve transcript, metadata, and structured insights.
Identify key topics, decisions, risks, and next steps.
Format output for the chosen audience.
Short or structured call summary with actions and risks
Generate deal recap
Prompt
Help reps and managers get a fast overview of a deal
Summarise the current state of a deal based on recent calls and linked CRM context
Deal ID or account name
optional date range
Retrieve calls and meetings linked to the deal.
Pull deal metadata and CRM-linked context.
Identify recent changes, blockers, risks, and next steps.
Summarise into a concise recap.
Deal recap with status, blockers, risks, and recommended next steps
Prepare weekly recap
Prompt
Help managers or reps review what changed over a time period
Weekly recap of calls, themes, and actions for one rep, team, or account
Rep ID
team ID or account ID
time period
Retrieve relevant calls and summaries for the selected time period.
Aggregate themes, action items, and changes.
Highlight notable risks, opportunities, and follow-ups.
Weekly recap with top themes, risks, opportunities, and actions
Prepare deal review
Workflow
Help reps and managers review deal status using multiple sources of context
Review a deal before pipeline review or forecast meeting
Deal ID
optional date range
Retrieve all recent calls, emails, and meetings tied to the deal.
Pull structured call summaries, score, action items, and key points.
Pull linked CRM context such as stage, contacts, and account details.
Aggregate open risks, unresolved objections, stakeholder gaps, and next steps.
Format into a review-ready structure.
Deal review document with current status, risks, stakeholders, blockers, and next steps
Weekly team recap
Workflow
Help managers monitor activity and risk across a group of reps or accounts
End-of-week team summary for sales leadership
Team ID
date range
Retrieve calls and structured insights across the team.
Group by rep, account, or deal.
Identify recurring themes, major risks, strong calls, and missed follow-ups.
Highlight calls or deals that need attention.
Team-level recap with patterns, key risks, notable wins, and action areas
Objection analysis across calls
Workflow
Help teams identify patterns across multiple conversations
Analyse pricing or competitor objections across recent calls
Account ID
deal ID
rep ID
keyword/topic
date range
Search calls matching the topic or keyword.
Retrieve summaries, transcripts, and snippets where relevant.
Identify recurring objections and how they were handled.
Assess whether objections were resolved or remain open.
Summarise patterns and examples.
Objection analysis with themes, frequency, examples, and suggested follow-up actions
Build onboarding playlist
Workflow
Help enablement teams curate useful learning content faster
Build a playlist of strong discovery, demo, or pricing calls for new hires
Topic
call type
rep/team filter
optional time period
Search for calls matching the selected criteria.
Rank or filter calls based on score, topics, or key moments.
Identify useful snippets or full recordings.
Add selected calls/snippets into a playlist structure.
Generate short descriptions for each item.
Curated onboarding playlist with selected calls/snippets and descriptions
Deal Inspection Skill
Skill
Give users a consistent, opinionated way to assess a deal
Claude reviews a deal using a repeatable Jiminny methodology
Deal ID or account name
optional review scope
Determine whether the request is suitable for a deal inspection.
Retrieve relevant calls, summaries, CRM context, stakeholders, and risks.
Apply a standard Jiminny review framework to assess deal health.
Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs.
Present findings in a consistent structure with recommended actions.
Structured deal inspection with health assessment, red flags, and recommendations
Manager Coaching Skill
Skill
Help managers review calls consistently and identify coaching moments
Analyse a rep’s call and surface strengths, gaps, and examples for coaching
Call ID
optional rep ID
optional coaching focus
Retrieve transcript, score, summary, and key moments from the call.
Evaluate the conversation against a coaching framework.
Identify what went well and where the rep could improve.
Suggest moments to clip as snippets.
Recommend coaching actions or playlist additions.
Coaching review with strengths, gaps, snippet suggestions, and recommended coaching actions
CSM / QBR Prep Skill
Skill
Help CS or AM teams prepare structured customer reviews from conversations
Generate an account review or QBR-style summary from recent customer interactions
Account ID, optional time period
Retrieve all recent calls, emails, and account-linked context.
Aggregate themes, requests, blockers, sentiment shifts, and opportunities.
Apply a repeatable account review structure.
Summarise outcomes, open issues, and suggested next steps.
Structured QBR/account review summary with themes, risks, opportunities, and actions
Snippet Builder Skill
Skill...
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Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product. 👤 Target user Revenue teams using AI tools (Sales, CS, RevOps) Companies already experimenting with Claude / OpenAI Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc) 🤕 Pain point or problem Jiminny data is locked in the platform AI tools lack access to high-value conversation context Customers must manually export/copy transcripts No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people Peer pressure as almost all competitors have this 💥 Impact and benefits For customers Better AI outputs using real customer conversations Ability to build automated workflows across tools For Jiminny Increased product stickiness (embedded in workflows) Competitive parity with Gong / Avoma etc. 💡 Solution ideas Build an AI Knowledge Connector (MCP-based) that: Exposes Jiminny data (calls, transcripts, metadata) to AI tools Provides structured insights (summaries, action items, scoring etc.) Supports deal-level context Allows integration into customer-owned AI stacks (Claude + Zapier, etc.) Success metrics List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets). Goal Metric Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows % of customers connecting external AI tools Number of MCP/API calls per customer - and which ones are being used the most Decrease the manual copying of transcripts decrease by X % User interaction and design Simple setup flow: Generate API key / connect MCP Clear instructions for Claude / Zapier setup Provide: Example prompts for Claude Pre-built workflow templates for Zapier Clear documentation - with use cases 👉 Focus: usable by non-technical users Detailed Requirements List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification. Requirement Importance Notes Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI) HIGH Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack Allow customers to access call data through AI tools HIGH Expose Jiminny data: list calls (filter by date, user, account, deal) get call get transcript (speaker-separated) get call metadata search calls (keyword + semantic) Support filtering by: account / contact/ lead / deal jiminny user date range Allow customers to search and query calls using AI tools HIGH Support keyword and semantic search across conversations Provide structured insights so customers can generate better outputs HIGH For each call, expose: Summary Action items Key points Activity type Notes Call Score CRM information Allow customers to understand deal status across multiple calls/emails HIGH For each deal expose: calls/meetings/emails Identified risks contacts related to the deal with their job title linked account details all CRM field information that we have in DI + fields used for CRM filling example - get_deal_summary request - { \"tool\": \"get_deal_summary\", \"arguments\": { \"deal_name\": \"ACME\" } } responce: { \"deal_name\": \"ACME\", \"stage\": \"Negotiation\", \"recent_activity\": [ \"Demo call on 2026-04-01\", \"Pricing discussion on 2026-04-03\" ], \"risks\": [ \"Pricing concerns\", \"Security review pending\" ], \"next_steps\": [ \"Send follow-up to VP Sales\", \"Share security documentation\" ] } Ensure the AI can easily understand the schema of the data coming from Jiminny HIGH Models perform much better when tools have: clear names rich descriptions predictable schemas Provide clear, actionable error states for customers when MCP connections fail HIGH Error messages should guide agents toward solutions with specific suggestions and next steps. Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.” Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.” Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.” Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.” Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.” Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.” No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.” Allow customers to securely control who and what data is accessible externally HIGH Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well. Private meetings shouldn’t be exposed via MCP HIGH When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP. Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows HIGH Ensure data is structured to be easily combined with: CRM (accounts, deals) external docs Use consistent identifiers: account IDs deal IDs MCP is available only for Scale tier and above HIGH Customers who are on Capture tier should be able to access any of their data through MCP. This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working. Provide visibility into how external AI tools access Jiminny data HIGH Audit logs: what data was accessed by which external system when identity - who made the request specific queries/filters used failed access attempts (especially cross-visibility-boundary attempts) Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it. example from Atlassian - Provide customers with example prompts to get started quickly HIGH MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments. See examples in table below. Expose Jiminny as a Connector in Claude medium Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny Provide customers with example workflows to get started quickly medium A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome. See examples in table below. Allow customers to use predefined Jiminny skills for common workflows medium Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job. Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com See examples in table below. Jiminny UI widget for OpenAI medium The reason to have this is to make certain Jiminny use cases much easier than pure chat. Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it. For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface. Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section. Allow customers to run long-running analyses asynchronously and retrieve progress/results low Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to: start the job let it run in the background return a status like “in progress” let the client come back for the result later For example - User asks in Claude: “Analyse all calls from this quarter and tell me the top 5 objections by segment.” That might require: searching lots of calls pulling many summaries/transcripts aggregating results Instead of making Claude wait 45+ seconds, Jiminny could: create an analysis job return: job_id = 123, status = running We need this in order to support: large-scale analysis multi-call aggregation across lots of data playlist generation across many calls heavy recap/report generation Creating a snippet through the MCP low Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team. example request: { \"tool\": \"create_snippet\", \"arguments\": { \"call_id\": \"call_123\", \"start_time\": \"00:18:24\", \"end_time\": \"00:19:10\", \"title\": \"Pricing objection\" } } example response: { \"snippet_id\": \"snip_987\", \"title\": \"Pricing objection\", \"share_url\": \"https://app.jiminny.com/snippets/snip_987\" } Ability to add recordings or snippets to a playlist low Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes Allow AI tools to read reusable Jiminny resources low fathom Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be: current deal snapshot account summary weekly rep recap approved talk tracks / objection handling guidance curated playlists or snippet libraries This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls. Ability to write coaching notes in Jiminny through MCP low What we want to do for prompts/worflows and skills Capability Type User value / job to be done Example Jiminny use case Inputs Steps performed Output Draft follow-up email Prompt Help reps quickly create tailored follow-ups after calls Use the latest customer call to draft an email with recap and next steps Call ID optional tone optional recipient type Retrieve call summary and action items from Jiminny. Pull key points, commitments, and next steps. Apply the chosen tone and structure. Draft the email in a consistent format. Customer-ready follow-up email draft Summarise call Prompt Help users quickly extract the most important points from a conversation Generate a concise summary of one call for internal or external use Call ID optional summary style optional audience Retrieve transcript, metadata, and structured insights. Identify key topics, decisions, risks, and next steps. Format output for the chosen audience. Short or structured call summary with actions and risks Generate deal recap Prompt Help reps and managers get a fast overview of a deal Summarise the current state of a deal based on recent calls and linked CRM context Deal ID or account name optional date range Retrieve calls and meetings linked to the deal. Pull deal metadata and CRM-linked context. Identify recent changes, blockers, risks, and next steps. Summarise into a concise recap. Deal recap with status, blockers, risks, and recommended next steps Prepare weekly recap Prompt Help managers or reps review what changed over a time period Weekly recap of calls, themes, and actions for one rep, team, or account Rep ID team ID or account ID time period Retrieve relevant calls and summaries for the selected time period. Aggregate themes, action items, and changes. Highlight notable risks, opportunities, and follow-ups. Weekly recap with top themes, risks, opportunities, and actions Prepare deal review Workflow Help reps and managers review deal status using multiple sources of context Review a deal before pipeline review or forecast meeting Deal ID optional date range Retrieve all recent calls, emails, and meetings tied to the deal. Pull structured call summaries, score, action items, and key points. Pull linked CRM context such as stage, contacts, and account details. Aggregate open risks, unresolved objections, stakeholder gaps, and next steps. Format into a review-ready structure. Deal review document with current status, risks, stakeholders, blockers, and next steps Weekly team recap Workflow Help managers monitor activity and risk across a group of reps or accounts End-of-week team summary for sales leadership Team ID date range Retrieve calls and structured insights across the team. Group by rep, account, or deal. Identify recurring themes, major risks, strong calls, and missed follow-ups. Highlight calls or deals that need attention. Team-level recap with patterns, key risks, notable wins, and action areas Objection analysis across calls Workflow Help teams identify patterns across multiple conversations Analyse pricing or competitor objections across recent calls Account ID deal ID rep ID keyword/topic date range Search calls matching the topic or keyword. Retrieve summaries, transcripts, and snippets where relevant. Identify recurring objections and how they were handled. Assess whether objections were resolved or remain open. Summarise patterns and examples. Objection analysis with themes, frequency, examples, and suggested follow-up actions Build onboarding playlist Workflow Help enablement teams curate useful learning content faster Build a playlist of strong discovery, demo, or pricing calls for new hires Topic call type rep/team filter optional time period Search for calls matching the selected criteria. Rank or filter calls based on score, topics, or key moments. Identify useful snippets or full recordings. Add selected calls/snippets into a playlist structure. Generate short descriptions for each item. Curated onboarding playlist with selected calls/snippets and descriptions Deal Inspection Skill Skill Give users a consistent, opinionated way to assess a deal Claude reviews a deal using a repeatable Jiminny methodology Deal ID or account name optional review scope Determine whether the request is suitable for a deal inspection. Retrieve relevant calls, summaries, CRM context, stakeholders, and risks. Apply a standard Jiminny review framework to assess deal health. Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs. Present findings in a consistent structure with recommended actions. Structured deal inspection with health assessment, red flags, and recommendations Manager Coaching Skill Skill Help managers review calls consistently and identify coaching moments Analyse a rep’s call and surface strengths, gaps, and examples for coaching Call ID optional rep ID optional coaching focus Retrieve transcript, score, summary, and key moments from the call. Evaluate the conversation against a coaching framework. Identify what went well and where the rep could improve. Suggest moments to clip as snippets. Recommend coaching actions or playlist additions. Coaching review with strengths, gaps, snippet suggestions, and recommended coaching actions CSM / QBR Prep Skill Skill Help CS or AM teams prepare structured customer reviews from conversations Generate an account review or QBR-style summary from recent customer interactions Account ID, optional time period Retrieve all recent calls, emails, and account-linked context. Aggregate themes, requests, blockers, sentiment shifts, and opportunities. Apply a repeatable account review structure. Summarise outcomes, open issues, and suggested next steps. Structured QBR/account review summary with themes, risks, opportunities, and actions Snippet Builder Skill Skill Help users consistently capture and label useful moments from calls Identify useful moments in a call and convert them into reusable snippets Call ID, optional focus area such as pricing, objection, competitor, discovery Retrieve transcript, recording timeline, and call insights. Detect moments matching the requested focus area. Propose snippet start/end times and titles. Tag snippets by type or theme. Optionally save them or add them to a playlist. One or more proposed or saved snippets with title, timing, and tags 💡 Technical details Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate. Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users. Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns. Open Questions Track unresolved issues or decisions that need to be made. Update as answers become available. Question Answer Date Answered Competitor research Competitor MCP Availability Core Capabilities Advanced Capabilities Positioning Key Strength Key Weakness Gong ✅ Mature MCP List/search calls Get transcripts get Call metadata Deal intelligence Scorecards, analytics CRM context Enterprise data layer Deep data + ecosystem integrations Limited opinionated AI workflows Avoma ✅ Mature MCP list_meetings get_meeting get_transcript key topics Deal fields/Deal level analysis Coaching/Scoring CRM context pushing insights into Notion / Confluence AI-ready meeting intelligence Strongest “outputs” layer Mostly pull-based interactions Fathom ✅ MCP list/search meetings get transcript get summary/action items Team/org context Webhooks (event-based triggers) get_weekly_recap aggregated action items across meetings creating webhooks directly from the MCP server Lightweight meeting data access Includes summaries + actions Limited analytics depth Grain ✅ Basic MCP get meetings download transcripts Notes Deals Scorecards Data access utility Simple + accessible Low sophistication, sometimes browser-based Outreach ⚠️ MCP (workflow-focused) Search sequences Emails Meetings (Kaia) account / prospect / opportunity lookups Workflow automation Multi-system orchestration Revenue workflow engine End-to-end GTM workflows Less depth in conversation data Attention ⚠️ Emerging MCP Meeting data Transcripts CRM-linked context AI insights Sales assistant workflows create snippets AI sales assistant + CRM Strong CRM linkage Less focus on external knowledge layer Planhat - Related content Ask Jiminny Anything on Call level Galya Dimitrova Hubspot app Galya Dimitrova Product Vision & Strategy Galya Dimitrova Automated CRM Filling Simona Georgieva (Deactivated) Competitive analysis Galya Dimitrova AI agent ideas Steliyan Georgiev Comments James Graham 9 Apr A few areas around Security & Compliance that need more depth before this moves beyond draft: Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate. Team visibility enforcement should be its own HIGH requirement. The current note under access control (\"ensure MCP only exposes data user has access to\") is doing too much work as a single line. Every MCP tool response (especially search_calls and deal-level aggregations) must be filtered through the same visibility engine as the UI. Semantic search is the riskiest here; it could surface results across team boundaries if not properly scoped. No data minimisation or scoping controls. We're exposing transcripts, CRM fields, contact details with job titles, deal context - significant PII and commercial data leaving the platform. Can customers configure which data types are exposed? Can transcripts be PII-stripped before they leave Jiminny? Given we position GDPR consent as a differentiator, we should be thinking about this. GDPR downstream processing is unaddressed. Piping transcripts to OpenAI/Gemini introduces new data processors. Customers need to acknowledge responsibility for downstream processing during setup. We should also consider whether call participants' recording consent covers onward sharing to third-party AI - it almost certainly doesn't by default. This is one for me to pick up. Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users. Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns. Audit logs should go deeper. Current spec (what data, which system, when) is a start. Add: identity behind the key, specific queries/filters used, and failed access attempts (especially cross-visibility-boundary attempts). Enterprise compliance teams will expect this. Downgrade handling. Doc says Scale tier and above, but doesn't say what happens on downgrade. Keys should auto-deactivate. Happy to help draft a dedicated Security & Compliance section if useful. Galya Dimitrova 15 Apr Thanks for the input @James Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.","depth":21,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"You said","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny MCP Connector","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Epic","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Link to Epik in Jira","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Document status","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"DRAFT","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Objective","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"👤 Target user","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Revenue teams using AI tools (Sales, CS, RevOps)","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Companies already experimenting with Claude / OpenAI","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"🤕 Pain point or problem","depth":23,"bounds":{"left":0.30868056,"top":0.125,"width":0.077430554,"height":0.054444443},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny data is locked in the platform","depth":23,"bounds":{"left":0.30868056,"top":0.18777777,"width":0.074652776,"height":0.085},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"AI tools lack access to high-value conversation context","depth":23,"bounds":{"left":0.30868056,"top":0.28111112,"width":0.10555556,"height":0.085},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Customers must manually export/copy transcripts","depth":23,"bounds":{"left":0.30868056,"top":0.37444445,"width":0.108333334,"height":0.085},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"No easy way to combine calls with CRM + docs + other info - 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This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to securely control who and what data is accessible externally","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Private meetings shouldn’t be exposed via MCP","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ensure data is structured to be easily combined with:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"CRM (accounts, deals)","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"external docs","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Use consistent identifiers:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"account IDs","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"deal IDs","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"MCP is available only for Scale tier and above","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Customers who are on Capture tier should be able to access any of their data through MCP.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide visibility into how external AI tools access Jiminny data","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Audit logs:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"what data was accessed by which external system when","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"identity - who made the request","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"specific queries/filters used","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"failed access attempts (especially cross-visibility-boundary attempts)","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"example from Atlassian -","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide customers with example prompts to get started quickly","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"See examples in table below.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Expose Jiminny as a Connector in Claude","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide customers with example workflows to get started quickly","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"See examples in table below.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to use predefined Jiminny skills for common workflows","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"See examples in table below.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny UI widget for OpenAI","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The reason to have this is to make certain Jiminny use cases much easier than pure chat.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to run long-running analyses asynchronously and retrieve progress/results","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"start the job","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"let it run in the background","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"return a status like “in progress”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"let the client come back for the result later","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"For example - User asks in Claude:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"“Analyse all calls from this quarter and tell me the top 5 objections by segment.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"That might require:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"searching lots of calls","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"pulling many summaries/transcripts","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"aggregating results","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Instead of making Claude wait 45+ seconds, Jiminny could:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"create an analysis job","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"return: job_id = 123, status = running","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"We need this in order to support:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"large-scale analysis","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"multi-call aggregation across lots of data","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"playlist generation across many calls","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"heavy recap/report generation","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Creating a snippet through the MCP","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"example request:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"{","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"tool\": \"create_snippet\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"arguments\": {","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"call_id\": \"call_123\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"start_time\": \"00:18:24\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"end_time\": \"00:19:10\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"title\": \"Pricing objection\"","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"example response:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"{","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"snippet_id\": \"snip_987\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"title\": \"Pricing objection\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"share_url\": \"https://app.jiminny.com/snippets/snip_987\"","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ability to add recordings or snippets to a playlist","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow AI tools to read reusable Jiminny resources","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"fathom","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"current deal snapshot","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"account summary","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"weekly rep recap","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"approved talk tracks / objection handling guidance","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"curated playlists or snippet libraries","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ability to write coaching notes in Jiminny through MCP","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"What we want to do for prompts/worflows and skills","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Capability","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Type","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"User value / job to be done","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Example Jiminny use 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ID","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"keyword/topic","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"date range","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Search calls matching the topic or keyword.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Retrieve summaries, transcripts, and snippets where relevant.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Identify recurring objections and how they were handled.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Assess whether objections were resolved or remain open.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Summarise patterns and examples.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Objection analysis with themes, frequency, examples, and suggested follow-up actions","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Build onboarding playlist","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Workflow","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Help enablement teams curate useful learning content faster","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Build a playlist of strong discovery, demo, or pricing calls for new hires","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Topic","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"call type","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"rep/team filter","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"optional time period","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Search for calls matching the selected criteria.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Rank or filter calls based on score, topics, or key moments.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Identify useful snippets or full recordings.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Add selected calls/snippets into a playlist structure.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Generate short descriptions for each item.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Curated onboarding playlist with selected calls/snippets and descriptions","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Deal Inspection Skill","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Skill","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Give users a consistent, opinionated way to assess a deal","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude reviews a deal using a repeatable Jiminny methodology","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Deal ID or account name","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"optional review scope","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Determine whether the request is suitable for a deal inspection.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Retrieve relevant calls, summaries, CRM context, stakeholders, and risks.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Apply a standard Jiminny review framework to assess deal health.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Present findings in a consistent structure with recommended actions.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Structured deal inspection with health assessment, red flags, and recommendations","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Manager Coaching Skill","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Skill","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Help managers review calls consistently and identify coaching moments","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Analyse a rep’s call and surface strengths, gaps, and examples for coaching","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Call ID","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"optional rep ID","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"optional coaching focus","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Retrieve transcript, score, summary, and key moments from the call.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Evaluate the conversation against a coaching framework.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Identify what went well and where the rep could improve.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Suggest moments to clip as snippets.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Recommend coaching actions or playlist additions.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Coaching review with strengths, gaps, snippet suggestions, and recommended coaching actions","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"CSM / QBR Prep 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opportunities.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Apply a repeatable account review structure.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Summarise outcomes, open issues, and suggested next steps.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Structured QBR/account review summary with themes, risks, opportunities, and actions","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Snippet Builder Skill","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Skill","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"}]...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST [URL_WITH_CREDENTIALS] Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.
You said
Jiminny MCP Connector
Epic
Link to Epik in Jira
Document status
DRAFT
Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.
👤 Target user
Revenue teams using AI tools (Sales, CS, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
Build an AI Knowledge Connector (MCP-based) that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides structured insights (summaries, action items, scoring etc.)
Supports deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
Metric
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of MCP/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
User interaction and design
Simple setup flow:
Generate API key / connect MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus: usable by non-technical users
Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
Importance
Notes
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH
For each deal expose:
calls/meetings/emails
Identified risks
contacts related to the deal with their job title
linked account details
all CRM field information that we have in DI + fields used for CRM filling
example - get_deal_summary
request -
{
"tool": "get_deal_summary",
"arguments": {
"deal_name": "ACME"
}
}
responce:
{
"deal_name": "ACME",
"stage": "Negotiation",
"recent_activity": [
"Demo call on 2026-04-01",
"Pricing discussion on 2026-04-03"
],
"risks": [
"Pricing concerns",
"Security review pending"
], "next_steps": [
"Send follow-up to VP Sales",
"Share security documentation"
]
}
Ensure the AI can easily understand the schema of the data coming from Jiminny
HIGH
Models perform much better when tools have:
clear names
rich descriptions
predictable schemas
Provide clear, actionable error states for customers when MCP connections fail
HIGH
Error messages should guide agents toward solutions with specific suggestions and next steps.
Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”
Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”
Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”
Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”
Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”
Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”
No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”
Allow customers to securely control who and what data is accessible externally
HIGH
Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well.
Private meetings shouldn’t be exposed via MCP
HIGH
When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP.
Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows
HIGH
Ensure data is structured to be easily combined with:
CRM (accounts, deals)
external docs
Use consistent identifiers:
account IDs
deal IDs
MCP is available only for Scale tier and above
HIGH
Customers who are on Capture tier should be able to access any of their data through MCP.
This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working.
Provide visibility into how external AI tools access Jiminny data
HIGH
Audit logs:
what data was accessed by which external system when
identity - who made the request
specific queries/filters used
failed access attempts (especially cross-visibility-boundary attempts)
Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it.
example from Atlassian -
Provide customers with example prompts to get started quickly
HIGH
MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments.
See examples in table below.
Expose Jiminny as a Connector in Claude
medium
Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny
Provide customers with example workflows to get started quickly
medium
A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.
See examples in table below.
Allow customers to use predefined Jiminny skills for common workflows
medium
Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.
Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com
See examples in table below.
Jiminny UI widget for OpenAI
medium
The reason to have this is to make certain Jiminny use cases much easier than pure chat.
Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.
For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.
Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.
Allow customers to run long-running analyses asynchronously and retrieve progress/results
low
Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to:
start the job
let it run in the background
return a status like “in progress”
let the client come back for the result later
For example - User asks in Claude:
“Analyse all calls from this quarter and tell me the top 5 objections by segment.”
That might require:
searching lots of calls
pulling many summaries/transcripts
aggregating results
Instead of making Claude wait 45+ seconds, Jiminny could:
create an analysis job
return: job_id = 123, status = running
We need this in order to support:
large-scale analysis
multi-call aggregation across lots of data
playlist generation across many calls
heavy recap/report generation
Creating a snippet through the MCP
low
Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team.
example request:
{
"tool": "create_snippet",
"arguments": {
"call_id": "call_123",
"start_time": "00:18:24",
"end_time": "00:19:10",
"title": "Pricing objection"
}
}
example response:
{
"snippet_id": "snip_987",
"title": "Pricing objection",
"share_url": "https://app.jiminny.com/snippets/snip_987"
}
Ability to add recordings or snippets to a playlist
low
Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes
Allow AI tools to read reusable Jiminny resources
low
fathom
Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be:
current deal snapshot
account summary
weekly rep recap
approved talk tracks / objection handling guidance
curated playlists or snippet libraries
This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls.
Ability to write coaching notes in Jiminny through MCP
low
What we want to do for prompts/worflows and skills
Capability
Type
User value / job to be done
Example Jiminny use case
Inputs
Steps performed
Output
Draft follow-up email
Prompt
Help reps quickly create tailored follow-ups after calls
Use the latest customer call to draft an email with recap and next steps
Call ID
optional tone
optional recipient type
Retrieve call summary and action items from Jiminny.
Pull key points, commitments, and next steps.
Apply the chosen tone and structure.
Draft the email in a consistent format.
Customer-ready follow-up email draft
Summarise call
Prompt
Help users quickly extract the most important points from a conversation
Generate a concise summary of one call for internal or external use
Call ID
optional summary style
optional audience
Retrieve transcript, metadata, and structured insights.
Identify key topics, decisions, risks, and next steps.
Format output for the chosen audience.
Short or structured call summary with actions and risks
Generate deal recap
Prompt
Help reps and managers get a fast overview of a deal
Summarise the current state of a deal based on recent calls and linked CRM context
Deal ID or account name
optional date range
Retrieve calls and meetings linked to the deal.
Pull deal metadata and CRM-linked context.
Identify recent changes, blockers, risks, and next steps.
Summarise into a concise recap.
Deal recap with status, blockers, risks, and recommended next steps
Prepare weekly recap
Prompt
Help managers or reps review what changed over a time period
Weekly recap of calls, themes, and actions for one rep, team, or account
Rep ID
team ID or account ID
time period
Retrieve relevant calls and summaries for the selected time period.
Aggregate themes, action items, and changes.
Highlight notable risks, opportunities, and follow-ups.
Weekly recap with top themes, risks, opportunities, and actions
Prepare deal review
Workflow
Help reps and managers review deal status using multiple sources of context
Review a deal before pipeline review or forecast meeting
Deal ID
optional date range
Retrieve all recent calls, emails, and meetings tied to the deal.
Pull structured call summaries, score, action items, and key points.
Pull linked CRM context such as stage, contacts, and account details.
Aggregate open risks, unresolved objections, stakeholder gaps, and next steps.
Format into a review-ready structure.
Deal review document with current status, risks, stakeholders, blockers, and next steps
Weekly team recap
Workflow
Help managers monitor activity and risk across a group of reps or accounts
End-of-week team summary for sales leadership
Team ID
date range
Retrieve calls and structured insights across the team.
Group by rep, account, or deal.
Identify recurring themes, major risks, strong calls, and missed follow-ups.
Highlight calls or deals that need attention.
Team-level recap with patterns, key risks, notable wins, and action areas
Objection analysis across calls
Workflow
Help teams identify patterns across multiple conversations
Analyse pricing or competitor objections across recent calls
Account ID
deal ID
rep ID
keyword/topic
date range
Search calls matching the topic or keyword.
Retrieve summaries, transcripts, and snippets where relevant.
Identify recurring objections and how they were handled.
Assess whether objections were resolved or remain open.
Summarise patterns and examples.
Objection analysis with themes, frequency, examples, and suggested follow-up actions
Build onboarding playlist
Workflow
Help enablement teams curate useful learning content faster
Build a playlist of strong discovery, demo, or pricing calls for new hires
Topic
call type
rep/team filter
optional time period
Search for calls matching the selected criteria.
Rank or filter calls based on score, topics, or key moments.
Identify useful snippets or full recordings.
Add selected calls/snippets into a playlist structure.
Generate short descriptions for each item.
Curated onboarding playlist with selected calls/snippets and descriptions
Deal Inspection Skill
Skill
Give users a consistent, opinionated way to assess a deal
Claude reviews a deal using a repeatable Jiminny methodology
Deal ID or account name
optional review scope
Determine whether the request is suitable for a deal inspection.
Retrieve relevant calls, summaries, CRM context, stakeholders, and risks.
Apply a standard Jiminny review framework to assess deal health.
Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs.
Present findings in a consistent structure with recommended actions.
Structured deal inspection with health assessment, red flags, and recommendations
Manager Coaching Skill
Skill
Help managers review calls consistently and identify coaching moments
Analyse a rep’s call and surface strengths, gaps, and examples for coaching
Call ID
optional rep ID
optional coaching focus
Retrieve transcript, score, summary, and key moments from the call.
Evaluate the conversation against a coaching framework.
Identify what went well and where the rep could improve.
Suggest moments to clip as snippets.
Recommend coaching actions or playlist additions.
Coaching review with strengths, gaps, snippet suggestions, and recommended coaching actions
CSM / QBR Prep Skill
Skill
Help CS or AM teams prepare structured customer reviews from conversations
Generate an account review or QBR-style summary from recent customer interactions
Account ID, optional time period
Retrieve all recent calls, emails, and account-linked context.
Aggregate themes, requests, blockers, sentiment shifts, and opportunities.
Apply a repeatable account review structure.
Summarise outcomes, open issues, and suggested next steps.
Structured QBR/account review summary with themes, risks, opportunities, and actions
Snippet Builder Skill
Skill...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST [URL_WITH_CREDENTIALS] Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.
You said
Jiminny MCP Connector
Epic
Link to Epik in Jira
Document status
DRAFT
Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.
👤 Target user
Revenue teams using AI tools (Sales, CS, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
Build an AI Knowledge Connector (MCP-based) that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides structured insights (summaries, action items, scoring etc.)
Supports deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
Metric
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of MCP/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
User interaction and design
Simple setup flow:
Generate API key / connect MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus: usable by non-technical users
Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
Importance
Notes
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH
For each deal expose:
calls/meetings/emails
Identified risks
contacts related to the deal with their job title
linked account details
all CRM field information that we have in DI + fields used for CRM filling
example - get_deal_summary
request -
{
"tool": "get_deal_summary",
"arguments": {
"deal_name": "ACME"
}
}
responce:
{
"deal_name": "ACME",
"stage": "Negotiation",
"recent_activity": [
"Demo call on 2026-04-01",
"Pricing discussion on 2026-04-03"
],
"risks": [
"Pricing concerns",
"Security review pending"
], "next_steps": [
"Send follow-up to VP Sales",
"Share security documentation"
]
}
Ensure the AI can easily understand the schema of the data coming from Jiminny
HIGH
Models perform much better when tools have:
clear names
rich descriptions
predictable schemas
Provide clear, actionable error states for customers when MCP connections fail
HIGH
Error messages should guide agents toward solutions with specific suggestions and next steps.
Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”
Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”
Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”
Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”
Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”
Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”
No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”
Allow customers to securely control who and what data is accessible externally
HIGH
Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well.
Private meetings shouldn’t be exposed via MCP
HIGH
When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP.
Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows
HIGH
Ensure data is structured to be easily combined with:
CRM (accounts, deals)
external docs
Use consistent identifiers:
account IDs
deal IDs
MCP is available only for Scale tier and above
HIGH
Customers who are on Capture tier should be able to access any of their data through MCP.
This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working.
Provide visibility into how external AI tools access Jiminny data
HIGH
Audit logs:
what data was accessed by which external system when
identity - who made the request
specific queries/filters used
failed access attempts (especially cross-visibility-boundary attempts)
Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it.
example from Atlassian -
Provide customers with example prompts to get started quickly
HIGH
MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments.
See examples in table below.
Expose Jiminny as a Connector in Claude
medium
Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny
Provide customers with example workflows to get started quickly
medium
A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.
See examples in table below.
Allow customers to use predefined Jiminny skills for common workflows
medium
Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.
Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com
See examples in table below.
Jiminny UI widget for OpenAI
medium
The reason to have this is to make certain Jiminny use cases much easier than pure chat.
Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.
For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.
Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.
Allow customers to run long-running analyses asynchronously and retrieve progress/results
low
Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to:
start the job
let it run in the background
return a status like “in progress”
let the client come back for the result later
For example - User asks in Claude:
“Analyse all calls from this quarter and tell me the top 5 objections by segment.”
That might require:
searching lots of calls
pulling many summaries/transcripts
aggregating results
Instead of making Claude wait 45+ seconds, Jiminny could:
create an analysis job
return: job_id = 123, status = running
We need this in order to support:
large-scale analysis
multi-call aggregation across lots of data
playlist generation across many calls
heavy recap/report generation
Creating a snippet through the MCP
low
Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team.
example request:
{
"tool": "create_snippet",
"arguments": {
"call_id": "call_123",
"start_time": "00:18:24",
"end_time": "00:19:10",
"title": "Pricing objection"
}
}
example response:
{
"snippet_id": "snip_987",
"title": "Pricing objection",
"share_url": "https://app.jiminny.com/snippets/snip_987"
}
Ability to add recordings or snippets to a playlist
low
Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes
Allow AI tools to read reusable Jiminny resources
low
fathom
Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be:
current deal snapshot
account summary
weekly rep recap
approved talk tracks / objection handling guidance
curated playlists or snippet libraries
This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls.
Ability to write coaching notes in Jiminny through MCP
low
What we want to do for prompts/worflows and skills
Capability
Type
User value / job to be done
Example Jiminny use case
Inputs
Steps performed
Output
Draft follow-up email
Prompt
Help reps quickly create tailored follow-ups after calls
Use the latest customer call to draft an email with recap and next steps
Call ID
optional tone
optional recipient type
Retrieve call summary and action items from Jiminny.
Pull key points, commitments, and next steps.
Apply the chosen tone and structure.
Draft the email in a consistent format.
Customer-ready follow-up email draft
Summarise call
Prompt
Help users quickly extract the most important points from a conversation
Generate a concise summary of one call for internal or external use
Call ID
optional summary style
optional audience
Retrieve transcript, metadata, and structured insights.
Identify key topics, decisions, risks, and next steps.
Format output for the chosen audience.
Short or structured call summary with actions and risks
Generate deal recap
Prompt
Help reps and managers get a fast overview of a deal
Summarise the current state of a deal based on recent calls and linked CRM context
Deal ID or account name
optional date range
Retrieve calls and meetings linked to the deal.
Pull deal metadata and CRM-linked context.
Identify recent changes, blockers, risks, and next steps.
Summarise into a concise recap.
Deal recap with status, blockers, risks, and recommended next steps
Prepare weekly recap
Prompt
Help managers or reps review what changed over a time period
Weekly recap of calls, themes, and actions for one rep, team, or account
Rep ID
team ID or account ID
time period
Retrieve relevant calls and summaries for the selected time period.
Aggregate themes, action items, and changes.
Highlight notable risks, opportunities, and follow-ups.
Weekly recap with top themes, risks, opportunities, and actions
Prepare deal review
Workflow
Help reps and managers review deal status using multiple sources of context
Review a deal before pipeline review or forecast meeting
Deal ID
optional date range
Retrieve all recent calls, emails, and meetings tied to the deal.
Pull structured call summaries, score, action items, and key points.
Pull linked CRM context such as stage, contacts, and account details.
Aggregate open risks, unresolved objections, stakeholder gaps, and next steps.
Format into a review-ready structure.
Deal review document with current status, risks, stakeholders, blockers, and next steps
Weekly team recap
Workflow
Help managers monitor activity and risk across a group of reps or accounts
End-of-week team summary for sales leadership
Team ID
date range
Retrieve calls and structured insights across the team.
Group by rep, account, or deal.
Identify recurring themes, major risks, strong calls, and missed follow-ups.
Highlight calls or deals that need attention.
Team-level recap with patterns, key risks, notable wins, and action areas
Objection analysis across calls
Workflow
Help teams identify patterns across multiple conversations
Analyse pricing or competitor objections across recent calls
Account ID
deal ID
rep ID
keyword/topic
date range
Search calls matching the topic or keyword.
Retrieve summaries, transcripts, and snippets where relevant.
Identify recurring objections and how they were handled.
Assess whether objections were resolved or remain open.
Summarise patterns and examples.
Objection analysis with themes, frequency, examples, and suggested follow-up actions
Build onboarding playlist
Workflow
Help enablement teams curate useful learning content faster
Build a playlist of strong discovery, demo, or pricing calls for new hires
Topic
call type
rep/team filter
optional time period
Search for calls matching the selected criteria.
Rank or filter calls based on score, topics, or key moments.
Identify useful snippets or full recordings.
Add selected calls/snippets into a playlist structure.
Generate short descriptions for each item.
Curated onboarding playlist with selected calls/snippets and descriptions
Deal Inspection Skill
Skill
Give users a consistent, opinionated way to assess a deal
Claude reviews a deal using a repeatable Jiminny methodology
Deal ID or account name
optional review scope
Determine whether the request is suitable for a deal inspection.
Retrieve relevant calls, summaries, CRM context, stakeholders, and risks.
Apply a standard Jiminny review framework to assess deal health.
Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs.
Present findings in a consistent structure with recommended actions.
Structured deal inspection with health assessment, red flags, and recommendations
Manager Coaching Skill
Skill
Help managers review calls consistently and identify coaching moments
Analyse a rep’s call and surface strengths, gaps, and examples for coaching
Call ID
optional rep ID
optional coaching focus
Retrieve transcript, score, summary, and key moments from the call.
Evaluate the conversation against a coaching framework.
Identify what went well and where the rep could improve.
Suggest moments to clip as snippets.
Recommend coaching actions or playlist additions.
Coaching review with strengths, gaps, snippet suggestions, and recommended coaching actions
CSM / QBR Prep Skill
Skill
Help CS or AM teams prepare structured customer reviews from conversations
Generate an account review or QBR-style summary from recent customer interactions
Account ID, optional time period
Retrieve all recent calls, emails, and account-linked context.
Aggregate themes, requests, blockers, sentiment shifts, and opportunities.
Apply a repeatable account review structure.
Summarise outcomes, open issues, and suggested next steps.
Structured QBR/account review summary with themes, risks, opportunities, and actions
Snippet Builder Skill
Skill
Help users consistently capture and label useful moments from calls
Identify useful moments in a call and convert them into reusable snippets
Call ID, optional focus area such as pricing, objection, competitor, discovery
Retrieve transcript, recording timeline, and call insights.
Detect moments matching the requested focus area.
Propose snippet start/end times and titles.
Tag snippets by type or theme.
Optionally save them or add them to a playlist.
One or more proposed or saved snippets with title, timing, and tags
💡 Technical details
Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate.
Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users.
Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns.
Open Questions
Track unresolved issues or decisions that need to be made. Update as answers become available.
Question
Answer
Date Answered
Competitor research
Competitor
MCP Availability
Core Capabilities
Advanced Capabilities
Positioning
Key Strength
Key Weakness
Gong
✅ Mature MCP
List/search calls
Get transcripts
get Call metadata
Deal intelligence
Scorecards, analytics
CRM context
Enterprise data layer
Deep data + ecosystem integrations
Limited opinionated AI workflows
Avoma
✅ Mature MCP
list_meetings
get_meeting
get_transcript
key topics
Deal fields/Deal level analysis
Coaching/Scoring
CRM context
pushing insights into Notion / Confluence
AI-ready meeting intelligence
Strongest “outputs” layer
Mostly pull-based interactions
Fathom
✅ MCP
list/search meetings
get transcript
get summary/action items
Team/org context
Webhooks (event-based triggers)
get_weekly_recap
aggregated action items across meetings
creating webhooks directly from the MCP server
Lightweight meeting data access
Includes summaries + actions
Limited analytics depth
Grain
✅ Basic MCP
get meetings
download transcripts
Notes
Deals
Scorecards
Data access utility
Simple + accessible
Low sophistication, sometimes browser-based
Outreach
⚠️ MCP (workflow-focused)
Search sequences
Emails
Meetings (Kaia)
account / prospect / opportunity lookups
Workflow automation
Multi-system orchestration
Revenue workflow engine
End-to-end GTM workflows
Less depth in conversation data
Attention
⚠️ Emerging MCP
Meeting data
Transcripts
CRM-linked context
AI insights
Sales assistant workflows
create snippets
AI sales assistant + CRM
Strong CRM linkage
Less focus on external knowledge layer
Planhat -
Related content
Ask Jiminny Anything on Call level
Galya Dimitrova
Hubspot app
Galya Dimitrova
Product Vision & Strategy
Galya Dimitrova
Automated CRM Filling
Simona Georgieva (Deactivated)
Competitive analysis
Galya Dimitrova
AI agent ideas
Steliyan Georgiev
Comments
James Graham
9 Apr
A few areas around Security & Compliance that need more depth before this moves beyond draft:
Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate.
Team visibility enforcement should be its own HIGH requirement. The current note under access control ("ensure MCP only exposes data user has access to") is doing too much work as a single line. Every MCP tool response (especially search_calls and deal-level aggregations) must be filtered through the same visibility engine as the UI. Semantic search is the riskiest here; it could surface results across team boundaries if not properly scoped.
No data minimisation or scoping controls. We're exposing transcripts, CRM fields, contact details with job titles, deal context - significant PII and commercial data leaving the platform. Can customers configure which data types are exposed? Can transcripts be PII-stripped before they leave Jiminny? Given we position GDPR consent as a differentiator, we should be thinking about this.
GDPR downstream processing is unaddressed. Piping transcripts to OpenAI/Gemini introduces new data processors. Customers need to acknowledge responsibility for downstream processing during setup. We should also consider whether call participants' recording consent covers onward sharing to third-party AI - it almost certainly doesn't by default. This is one for me to pick up.
Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users.
Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns.
Audit logs should go deeper. Current spec (what data, which system, when) is a start. Add: identity behind the key, specific queries/filters used, and failed access attempts (especially cross-visibility-boundary attempts). Enterprise compliance teams will expect this.
Downgrade handling. Doc says Scale tier and above, but doesn't say what happens on downgrade. Keys should auto-deactivate.
Happy to help draft a dedicated Security & Compliance section i...
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Objective Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows. Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product. 👤 Target user Revenue teams using AI tools (Sales, CS, RevOps) Companies already experimenting with Claude / OpenAI Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc) 🤕 Pain point or problem Jiminny data is locked in the platform AI tools lack access to high-value conversation context Customers must manually export/copy transcripts No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people Peer pressure as almost all competitors have this 💥 Impact and benefits For customers Better AI outputs using real customer conversations Ability to build automated workflows across tools For Jiminny Increased product stickiness (embedded in workflows) Competitive parity with Gong / Avoma etc. 💡 Solution ideas Build an AI Knowledge Connector (MCP-based) that: Exposes Jiminny data (calls, transcripts, metadata) to AI tools Provides structured insights (summaries, action items, scoring etc.) Supports deal-level context Allows integration into customer-owned AI stacks (Claude + Zapier, etc.) Success metrics List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets). Goal Metric Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows % of customers connecting external AI tools Number of MCP/API calls per customer - and which ones are being used the most Decrease the manual copying of transcripts decrease by X % User interaction and design Simple setup flow: Generate API key / connect MCP Clear instructions for Claude / Zapier setup Provide: Example prompts for Claude Pre-built workflow templates for Zapier Clear documentation - with use cases 👉 Focus: usable by non-technical users Detailed Requirements List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification. Requirement Importance Notes Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI) HIGH Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack Allow customers to access call data through AI tools HIGH Expose Jiminny data: list calls (filter by date, user, account, deal) get call get transcript (speaker-separated) get call metadata search calls (keyword + semantic) Support filtering by: account / contact/ lead / deal jiminny user date range Allow customers to search and query calls using AI tools HIGH Support keyword and semantic search across conversations Provide structured insights so customers can generate better outputs HIGH For each call, expose: Summary Action items Key points Activity type Notes Call Score CRM information Allow customers to understand deal status across multiple calls/emails HIGH For each deal expose: calls/meetings/emails Identified risks contacts related to the deal with their job title linked account details all CRM field information that we have in DI + fields used for CRM filling example - get_deal_summary request - { \"tool\": \"get_deal_summary\", \"arguments\": { \"deal_name\": \"ACME\" } } responce: { \"deal_name\": \"ACME\", \"stage\": \"Negotiation\", \"recent_activity\": [ \"Demo call on 2026-04-01\", \"Pricing discussion on 2026-04-03\" ], \"risks\": [ \"Pricing concerns\", \"Security review pending\" ], \"next_steps\": [ \"Send follow-up to VP Sales\", \"Share security documentation\" ] } Ensure the AI can easily understand the schema of the data coming from Jiminny HIGH Models perform much better when tools have: clear names rich descriptions predictable schemas Provide clear, actionable error states for customers when MCP connections fail HIGH Error messages should guide agents toward solutions with specific suggestions and next steps. Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.” Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.” Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.” Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.” Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.” Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.” No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.” Allow customers to securely control who and what data is accessible externally HIGH Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well. Private meetings shouldn’t be exposed via MCP HIGH When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP. Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows HIGH Ensure data is structured to be easily combined with: CRM (accounts, deals) external docs Use consistent identifiers: account IDs deal IDs MCP is available only for Scale tier and above HIGH Customers who are on Capture tier should be able to access any of their data through MCP. This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working. Provide visibility into how external AI tools access Jiminny data HIGH Audit logs: what data was accessed by which external system when identity - who made the request specific queries/filters used failed access attempts (especially cross-visibility-boundary attempts) Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it. example from Atlassian - Provide customers with example prompts to get started quickly HIGH MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments. See examples in table below. Expose Jiminny as a Connector in Claude medium Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny Provide customers with example workflows to get started quickly medium A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome. See examples in table below. Allow customers to use predefined Jiminny skills for common workflows medium Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job. Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com See examples in table below. Jiminny UI widget for OpenAI medium The reason to have this is to make certain Jiminny use cases much easier than pure chat. Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it. For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface. Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section. Allow customers to run long-running analyses asynchronously and retrieve progress/results low Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to: start the job let it run in the background return a status like “in progress” let the client come back for the result later For example - User asks in Claude: “Analyse all calls from this quarter and tell me the top 5 objections by segment.” That might require: searching lots of calls pulling many summaries/transcripts aggregating results Instead of making Claude wait 45+ seconds, Jiminny could: create an analysis job return: job_id = 123, status = running We need this in order to support: large-scale analysis multi-call aggregation across lots of data playlist generation across many calls heavy recap/report generation Creating a snippet through the MCP low Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team. example request: { \"tool\": \"create_snippet\", \"arguments\": { \"call_id\": \"call_123\", \"start_time\": \"00:18:24\", \"end_time\": \"00:19:10\", \"title\": \"Pricing objection\" } } example response: { \"snippet_id\": \"snip_987\", \"title\": \"Pricing objection\", \"share_url\": \"https://app.jiminny.com/snippets/snip_987\" } Ability to add recordings or snippets to a playlist low Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes Allow AI tools to read reusable Jiminny resources low fathom Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be: current deal snapshot account summary weekly rep recap approved talk tracks / objection handling guidance curated playlists or snippet libraries This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls. Ability to write coaching notes in Jiminny through MCP low What we want to do for prompts/worflows and skills Capability Type User value / job to be done Example Jiminny use case Inputs Steps performed Output Draft follow-up email Prompt Help reps quickly create tailored follow-ups after calls Use the latest customer call to draft an email with recap and next steps Call ID optional tone optional recipient type Retrieve call summary and action items from Jiminny. Pull key points, commitments, and next steps. Apply the chosen tone and structure. Draft the email in a consistent format. Customer-ready follow-up email draft Summarise call Prompt Help users quickly extract the most important points from a conversation Generate a concise summary of one call for internal or external use Call ID optional summary style optional audience Retrieve transcript, metadata, and structured insights. Identify key topics, decisions, risks, and next steps. Format output for the chosen audience. Short or structured call summary with actions and risks Generate deal recap Prompt Help reps and managers get a fast overview of a deal Summarise the current state of a deal based on recent calls and linked CRM context Deal ID or account name optional date range Retrieve calls and meetings linked to the deal. Pull deal metadata and CRM-linked context. Identify recent changes, blockers, risks, and next steps. Summarise into a concise recap. Deal recap with status, blockers, risks, and recommended next steps Prepare weekly recap Prompt Help managers or reps review what changed over a time period Weekly recap of calls, themes, and actions for one rep, team, or account Rep ID team ID or account ID time period Retrieve relevant calls and summaries for the selected time period. Aggregate themes, action items, and changes. Highlight notable risks, opportunities, and follow-ups. Weekly recap with top themes, risks, opportunities, and actions Prepare deal review Workflow Help reps and managers review deal status using multiple sources of context Review a deal before pipeline review or forecast meeting Deal ID optional date range Retrieve all recent calls, emails, and meetings tied to the deal. Pull structured call summaries, score, action items, and key points. Pull linked CRM context such as stage, contacts, and account details. Aggregate open risks, unresolved objections, stakeholder gaps, and next steps. Format into a review-ready structure. Deal review document with current status, risks, stakeholders, blockers, and next steps Weekly team recap Workflow Help managers monitor activity and risk across a group of reps or accounts End-of-week team summary for sales leadership Team ID date range Retrieve calls and structured insights across the team. Group by rep, account, or deal. Identify recurring themes, major risks, strong calls, and missed follow-ups. Highlight calls or deals that need attention. Team-level recap with patterns, key risks, notable wins, and action areas Objection analysis across calls Workflow Help teams identify patterns across multiple conversations Analyse pricing or competitor objections across recent calls Account ID deal ID rep ID keyword/topic date range Search calls matching the topic or keyword. Retrieve summaries, transcripts, and snippets where relevant. Identify recurring objections and how they were handled. Assess whether objections were resolved or remain open. Summarise patterns and examples. Objection analysis with themes, frequency, examples, and suggested follow-up actions Build onboarding playlist Workflow Help enablement teams curate useful learning content faster Build a playlist of strong discovery, demo, or pricing calls for new hires Topic call type rep/team filter optional time period Search for calls matching the selected criteria. Rank or filter calls based on score, topics, or key moments. Identify useful snippets or full recordings. Add selected calls/snippets into a playlist structure. Generate short descriptions for each item. Curated onboarding playlist with selected calls/snippets and descriptions Deal Inspection Skill Skill Give users a consistent, opinionated way to assess a deal Claude reviews a deal using a repeatable Jiminny methodology Deal ID or account name optional review scope Determine whether the request is suitable for a deal inspection. Retrieve relevant calls, summaries, CRM context, stakeholders, and risks. Apply a standard Jiminny review framework to assess deal health. Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs. Present findings in a consistent structure with recommended actions. Structured deal inspection with health assessment, red flags, and recommendations Manager Coaching Skill Skill Help managers review calls consistently and identify coaching moments Analyse a rep’s call and surface strengths, gaps, and examples for coaching Call ID optional rep ID optional coaching focus Retrieve transcript, score, summary, and key moments from the call. Evaluate the conversation against a coaching framework. Identify what went well and where the rep could improve. Suggest moments to clip as snippets. Recommend coaching actions or playlist additions. Coaching review with strengths, gaps, snippet suggestions, and recommended coaching actions CSM / QBR Prep Skill Skill Help CS or AM teams prepare structured customer reviews from conversations Generate an account review or QBR-style summary from recent customer interactions Account ID, optional time period Retrieve all recent calls, emails, and account-linked context. Aggregate themes, requests, blockers, sentiment shifts, and opportunities. Apply a repeatable account review structure. Summarise outcomes, open issues, and suggested next steps. Structured QBR/account review summary with themes, risks, opportunities, and actions Snippet Builder Skill Skill Help users consistently capture and label useful moments from calls Identify useful moments in a call and convert them into reusable snippets Call ID, optional focus area such as pricing, objection, competitor, discovery Retrieve transcript, recording timeline, and call insights. Detect moments matching the requested focus area. Propose snippet start/end times and titles. Tag snippets by type or theme. Optionally save them or add them to a playlist. One or more proposed or saved snippets with title, timing, and tags 💡 Technical details Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate. Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users. Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns. Open Questions Track unresolved issues or decisions that need to be made. Update as answers become available. Question Answer Date Answered Competitor research Competitor MCP Availability Core Capabilities Advanced Capabilities Positioning Key Strength Key Weakness Gong ✅ Mature MCP List/search calls Get transcripts get Call metadata Deal intelligence Scorecards, analytics CRM context Enterprise data layer Deep data + ecosystem integrations Limited opinionated AI workflows Avoma ✅ Mature MCP list_meetings get_meeting get_transcript key topics Deal fields/Deal level analysis Coaching/Scoring CRM context pushing insights into Notion / Confluence AI-ready meeting intelligence Strongest “outputs” layer Mostly pull-based interactions Fathom ✅ MCP list/search meetings get transcript get summary/action items Team/org context Webhooks (event-based triggers) get_weekly_recap aggregated action items across meetings creating webhooks directly from the MCP server Lightweight meeting data access Includes summaries + actions Limited analytics depth Grain ✅ Basic MCP get meetings download transcripts Notes Deals Scorecards Data access utility Simple + accessible Low sophistication, sometimes browser-based Outreach ⚠️ MCP (workflow-focused) Search sequences Emails Meetings (Kaia) account / prospect / opportunity lookups Workflow automation Multi-system orchestration Revenue workflow engine End-to-end GTM workflows Less depth in conversation data Attention ⚠️ Emerging MCP Meeting data Transcripts CRM-linked context AI insights Sales assistant workflows create snippets AI sales assistant + CRM Strong CRM linkage Less focus on external knowledge layer Planhat - Related content Ask Jiminny Anything on Call level Galya Dimitrova Hubspot app Galya Dimitrova Product Vision & Strategy Galya Dimitrova Automated CRM Filling Simona Georgieva (Deactivated) Competitive analysis Galya Dimitrova AI agent ideas Steliyan Georgiev Comments James Graham 9 Apr A few areas around Security & Compliance that need more depth before this moves beyond draft: Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate. Team visibility enforcement should be its own HIGH requirement. The current note under access control (\"ensure MCP only exposes data user has access to\") is doing too much work as a single line. Every MCP tool response (especially search_calls and deal-level aggregations) must be filtered through the same visibility engine as the UI. Semantic search is the riskiest here; it could surface results across team boundaries if not properly scoped. No data minimisation or scoping controls. We're exposing transcripts, CRM fields, contact details with job titles, deal context - significant PII and commercial data leaving the platform. Can customers configure which data types are exposed? Can transcripts be PII-stripped before they leave Jiminny? Given we position GDPR consent as a differentiator, we should be thinking about this. GDPR downstream processing is unaddressed. Piping transcripts to OpenAI/Gemini introduces new data processors. Customers need to acknowledge responsibility for downstream processing during setup. We should also consider whether call participants' recording consent covers onward sharing to third-party AI - it almost certainly doesn't by default. This is one for me to pick up. Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users. Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns. Audit logs should go deeper. Current spec (what data, which system, when) is a start. Add: identity behind the key, specific queries/filters used, and failed access attempts (especially cross-visibility-boundary attempts). Enterprise compliance teams will expect this. Downgrade handling. Doc says Scale tier and above, but doesn't say what happens on downgrade. Keys should auto-deactivate. Happy to help draft a dedicated Security & Compliance section if useful. Galya Dimitrova 15 Apr Thanks for the input @James Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.","depth":21,"bounds":{"left":0.41805187,"top":0.19313647,"width":0.051861703,"height":0.11173184},"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"You said","depth":23,"bounds":{"left":0.36053857,"top":0.19553073,"width":0.019946808,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny MCP Connector","depth":23,"bounds":{"left":0.41805187,"top":0.1963288,"width":0.03125,"height":0.038707104},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Epic","depth":23,"bounds":{"left":0.41805187,"top":0.30806065,"width":0.010305851,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Link to Epik in Jira","depth":23,"bounds":{"left":0.41805187,"top":0.33040702,"width":0.043218084,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Document status","depth":23,"bounds":{"left":0.41805187,"top":0.3527534,"width":0.04105718,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"DRAFT","depth":23,"bounds":{"left":0.41805187,"top":0.37509975,"width":0.016289894,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Objective","depth":23,"bounds":{"left":0.41805187,"top":0.39744613,"width":0.024268618,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.","depth":23,"bounds":{"left":0.41805187,"top":0.4197925,"width":0.051529255,"height":0.17278531},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.","depth":23,"bounds":{"left":0.41805187,"top":0.59856343,"width":0.049700797,"height":0.10574621},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"👤 Target user","depth":23,"bounds":{"left":0.41805187,"top":0.75458896,"width":0.033909574,"height":0.016759777},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Revenue teams using AI tools (Sales, CS, RevOps)","depth":23,"bounds":{"left":0.41805187,"top":0.7773344,"width":0.051030584,"height":0.061053474},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Companies already experimenting with Claude / OpenAI","depth":23,"bounds":{"left":0.41805187,"top":0.8443735,"width":0.04637633,"height":0.061053474},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)","depth":23,"bounds":{"left":0.41805187,"top":0.9114126,"width":0.051363032,"height":0.0885874},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"🤕 Pain point or problem","depth":23,"bounds":{"left":0.41805187,"top":1.0,"width":0.03706782,"height":-0.0897845},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny data is locked in the platform","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"AI tools lack access to high-value conversation context","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Customers must manually export/copy transcripts","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"No easy way to combine calls with CRM + docs + other info - 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This is not a data or permissions issue — please retry in a few minutes.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to securely control who and what data is accessible externally","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Private meetings shouldn’t be exposed via MCP","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ensure data is structured to be easily combined with:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"CRM (accounts, deals)","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"external docs","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Use consistent identifiers:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"account IDs","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"deal IDs","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"MCP is available only for Scale tier and above","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Customers who are on Capture tier should be able to access any of their data through MCP.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide visibility into how external AI tools access Jiminny data","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Audit logs:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"what data was accessed by which external system when","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"identity - who made the request","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"specific queries/filters used","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"failed access attempts (especially cross-visibility-boundary attempts)","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"example from Atlassian -","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide customers with example prompts to get started quickly","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"See examples in table below.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Expose Jiminny as a Connector in Claude","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide customers with example workflows to get started quickly","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"See examples in table below.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to use predefined Jiminny skills for common workflows","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"See examples in table below.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny UI widget for OpenAI","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The reason to have this is to make certain Jiminny use cases much easier than pure chat.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to run long-running analyses asynchronously and retrieve progress/results","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"start the job","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"let it run in the background","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"return a status like “in progress”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"let the client come back for the result later","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"For example - User asks in Claude:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"“Analyse all calls from this quarter and tell me the top 5 objections by segment.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"That might require:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"searching lots of calls","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"pulling many summaries/transcripts","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"aggregating results","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Instead of making Claude wait 45+ seconds, Jiminny could:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"create an analysis job","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"return: job_id = 123, status = running","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"We need this in order to support:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"large-scale analysis","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"multi-call aggregation across lots of data","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"playlist generation across many calls","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"heavy recap/report generation","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Creating a snippet through the MCP","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"example request:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"{","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"tool\": \"create_snippet\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"arguments\": {","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"call_id\": \"call_123\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"start_time\": \"00:18:24\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"end_time\": \"00:19:10\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"title\": \"Pricing objection\"","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"example response:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"{","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"snippet_id\": \"snip_987\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"title\": \"Pricing objection\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"share_url\": \"https://app.jiminny.com/snippets/snip_987\"","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ability to add recordings or snippets to a playlist","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow AI tools to read reusable Jiminny resources","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"fathom","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"current deal snapshot","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"account summary","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"weekly rep recap","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"approved talk tracks / objection handling guidance","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"curated playlists or snippet libraries","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ability to write coaching notes in Jiminny through MCP","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"What we want to do for prompts/worflows and skills","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Capability","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Type","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"User value / job to be done","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Example Jiminny use 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Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Team visibility enforcement should be its own HIGH requirement. The current note under access control (\"ensure MCP only exposes data user has access to\") is doing too much work as a single line. Every MCP tool response (especially search_calls and deal-level aggregations) must be filtered through the same visibility engine as the UI. Semantic search is the riskiest here; it could surface results across team boundaries if not properly scoped.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"No data minimisation or scoping controls. We're exposing transcripts, CRM fields, contact details with job titles, deal context - significant PII and commercial data leaving the platform. Can customers configure which data types are exposed? Can transcripts be PII-stripped before they leave Jiminny? Given we position GDPR consent as a differentiator, we should be thinking about this.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"GDPR downstream processing is unaddressed. Piping transcripts to OpenAI/Gemini introduces new data processors. Customers need to acknowledge responsibility for downstream processing during setup. We should also consider whether call participants' recording consent covers onward sharing to third-party AI - it almost certainly doesn't by default. This is one for me to pick up.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Audit logs should go deeper. Current spec (what data, which system, when) is a start. Add: identity behind the key, specific queries/filters used, and failed access attempts (especially cross-visibility-boundary attempts). Enterprise compliance teams will expect this.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Downgrade handling. Doc says Scale tier and above, but doesn't say what happens on downgrade. Keys should auto-deactivate.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Happy to help draft a dedicated Security & Compliance section if useful.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Galya Dimitrova","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"15 Apr","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Thanks for the input @James Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Expand","depth":21,"bounds":{"left":0.46991357,"top":0.19313647,"width":0.013297873,"height":0.031923383},"role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXButton","text":"Listen","depth":21,"bounds":{"left":0.47523272,"top":0.3367917,"width":0.013297873,"height":0.031923383},"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXButton","text":"Show more options","depth":20,"bounds":{"left":0.47523272,"top":0.3312051,"width":0.013297873,"height":0.031923383},"role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Show thinking","depth":26,"bounds":{"left":0.3828125,"top":0.33918595,"width":0.030917553,"height":0.014764565},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"Gemini said","depth":20,"bounds":{"left":0.3804854,"top":0.38068634,"width":0.0003324468,"height":0.0007980846},"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Gemini said","depth":21,"bounds":{"left":0.3804854,"top":0.38268158,"width":0.04105718,"height":0.01915403},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Thank you for sharing the draft PRD for the","depth":24,"bounds":{"left":0.3695146,"top":0.39026338,"width":0.10521942,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny MCP Connector","depth":24,"bounds":{"left":0.3695146,"top":0.41101357,"width":0.0625,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":". It looks like a highly strategic initiative to transition Jiminny from a standalone platform into a foundational data layer for AI-driven revenue workflows.","depth":24,"bounds":{"left":0.3695146,"top":0.41101357,"width":0.11319814,"height":0.07861133},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Here is a quick breakdown of the document as it currently stands:","depth":24,"bounds":{"left":0.3695146,"top":0.5067837,"width":0.11635638,"height":0.037110932},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"Executive Summary","depth":23,"bounds":{"left":0.3695146,"top":0.56823623,"width":0.11635638,"height":0.01915403},"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Executive Summary","depth":24,"bounds":{"left":0.3695146,"top":0.5698324,"width":0.051363032,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Core Objective:","depth":26,"bounds":{"left":0.38115028,"top":0.5961692,"width":0.040392287,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Build an MCP-based AI Knowledge Connector to expose Jiminny data (transcripts, summaries, deal context) to external AI tools like Claude, OpenAI, and Gemini.","depth":26,"bounds":{"left":0.38115028,"top":0.5961692,"width":0.10472074,"height":0.09936153},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Target Value:","depth":26,"bounds":{"left":0.38115028,"top":0.70630485,"width":0.03374335,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Increases product stickiness, reduces manual data entry/copy-pasting, and achieves competitive parity with platforms like Gong and Avoma.","depth":26,"bounds":{"left":0.38115028,"top":0.70630485,"width":0.10305851,"height":0.07861133},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Key Capabilities:","depth":26,"bounds":{"left":0.38115028,"top":0.79569036,"width":0.043550532,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"* Call and deal-level data retrieval.","depth":26,"bounds":{"left":0.38115028,"top":0.79569036,"width":0.09458112,"height":0.037110932},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Semantic and keyword search.","depth":28,"bounds":{"left":0.39145613,"top":0.8435754,"width":0.073803194,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Pre-built prompts, workflows, and skills (e.g., deal inspection, objection analysis).","depth":28,"bounds":{"left":0.39145613,"top":0.8707103,"width":0.08976064,"height":0.057861134},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Current Bottlenecks & Discussions:","depth":26,"bounds":{"left":0.38115028,"top":0.9393456,"width":0.09142287,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"James Graham has flagged several critical security and compliance considerations (auth modeling, data minimization, key lifecycle, rate limiting, and GDPR downstream processing). Galya has integrated some of these into the technical requirements but pushed back on the GDPR point, noting that customers already export this data manually or via standard APIs.","depth":26,"bounds":{"left":0.38115028,"top":0.9393456,"width":0.10472074,"height":0.0606544},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Since you shared the document without a specific prompt, how would you like to proceed—would you like help drafting a response to Galya's GDPR question, refining the technical requirements based on James's feedback, or summarizing this into a pitch for stakeholders?","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXTextArea","text":"Enter a prompt for Gemini\nencrypted","depth":20,"bounds":{"left":0.37416887,"top":0.8104549,"width":0.10638298,"height":0.01915403},"value":"Enter a prompt for Gemini\nencrypted","help_text":"","role_description":"text entry area","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Enter a prompt for Gemini","depth":21,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"encrypted","depth":21,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Open upload file menu","depth":20,"bounds":{"left":0.37017953,"top":0.8447725,"width":0.013297873,"height":0.031923383},"role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Tools","depth":18,"bounds":{"left":0.38613698,"top":0.8447725,"width":0.013297873,"height":0.031923383},"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Open mode picker","depth":20,"bounds":{"left":0.4431516,"top":0.8439745,"width":0.026097074,"height":0.031923383},"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Pro","depth":23,"bounds":{"left":0.44847074,"top":0.8527534,"width":0.007480053,"height":0.014764565},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXCheckBox","text":"Microphone","depth":19,"bounds":{"left":0.47124335,"top":0.8439745,"width":0.013297873,"height":0.031923383},"role_description":"toggle button","subrole":"AXToggle","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Your Jiminny chats aren’t used to improve our models. Gemini is AI and can make mistakes, including about people.","depth":17,"bounds":{"left":0.3665226,"top":0.896249,"width":0.12167553,"height":0.025139665},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Your privacy & Gemini Opens in a new window","depth":17,"bounds":{"left":0.40724733,"top":0.92178774,"width":0.040226065,"height":0.012370312},"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Your privacy & Gemini","depth":18,"bounds":{"left":0.40724733,"top":0.92178774,"width":0.040226065,"height":0.012370312},"help_text":"","role_description":"text","subrole":"AXUnknown"}]...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST [URL_WITH_CREDENTIALS] Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.
You said
Jiminny MCP Connector
Epic
Link to Epik in Jira
Document status
DRAFT
Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.
👤 Target user
Revenue teams using AI tools (Sales, CS, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
Build an AI Knowledge Connector (MCP-based) that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides structured insights (summaries, action items, scoring etc.)
Supports deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
Metric
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of MCP/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
User interaction and design
Simple setup flow:
Generate API key / connect MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus: usable by non-technical users
Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
Importance
Notes
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH
For each deal expose:
calls/meetings/emails
Identified risks
contacts related to the deal with their job title
linked account details
all CRM field information that we have in DI + fields used for CRM filling
example - get_deal_summary
request -
{
"tool": "get_deal_summary",
"arguments": {
"deal_name": "ACME"
}
}
responce:
{
"deal_name": "ACME",
"stage": "Negotiation",
"recent_activity": [
"Demo call on 2026-04-01",
"Pricing discussion on 2026-04-03"
],
"risks": [
"Pricing concerns",
"Security review pending"
], "next_steps": [
"Send follow-up to VP Sales",
"Share security documentation"
]
}
Ensure the AI can easily understand the schema of the data coming from Jiminny
HIGH
Models perform much better when tools have:
clear names
rich descriptions
predictable schemas
Provide clear, actionable error states for customers when MCP connections fail
HIGH
Error messages should guide agents toward solutions with specific suggestions and next steps.
Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”
Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”
Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”
Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”
Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”
Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”
No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”
Allow customers to securely control who and what data is accessible externally
HIGH
Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well.
Private meetings shouldn’t be exposed via MCP
HIGH
When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP.
Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows
HIGH
Ensure data is structured to be easily combined with:
CRM (accounts, deals)
external docs
Use consistent identifiers:
account IDs
deal IDs
MCP is available only for Scale tier and above
HIGH
Customers who are on Capture tier should be able to access any of their data through MCP.
This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working.
Provide visibility into how external AI tools access Jiminny data
HIGH
Audit logs:
what data was accessed by which external system when
identity - who made the request
specific queries/filters used
failed access attempts (especially cross-visibility-boundary attempts)
Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it.
example from Atlassian -
Provide customers with example prompts to get started quickly
HIGH
MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments.
See examples in table below.
Expose Jiminny as a Connector in Claude
medium
Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny
Provide customers with example workflows to get started quickly
medium
A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.
See examples in table below.
Allow customers to use predefined Jiminny skills for common workflows
medium
Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.
Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com
See examples in table below.
Jiminny UI widget for OpenAI
medium
The reason to have this is to make certain Jiminny use cases much easier than pure chat.
Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.
For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.
Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.
Allow customers to run long-running analyses asynchronously and retrieve progress/results
low
Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to:
start the job
let it run in the background
return a status like “in progress”
let the client come back for the result later
For example - User asks in Claude:
“Analyse all calls from this quarter and tell me the top 5 objections by segment.”
That might require:
searching lots of calls
pulling many summaries/transcripts
aggregating results
Instead of making Claude wait 45+ seconds, Jiminny could:
create an analysis job
return: job_id = 123, status = running
We need this in order to support:
large-scale analysis
multi-call aggregation across lots of data
playlist generation across many calls
heavy recap/report generation
Creating a snippet through the MCP
low
Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team.
example request:
{
"tool": "create_snippet",
"arguments": {
"call_id": "call_123",
"start_time": "00:18:24",
"end_time": "00:19:10",
"title": "Pricing objection"
}
}
example response:
{
"snippet_id": "snip_987",
"title": "Pricing objection",
"share_url": "https://app.jiminny.com/snippets/snip_987"
}
Ability to add recordings or snippets to a playlist
low
Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes
Allow AI tools to read reusable Jiminny resources
low
fathom
Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be:
current deal snapshot
account summary
weekly rep recap
approved talk tracks / objection handling guidance
curated playlists or snippet libraries
This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls.
Ability to write coaching notes in Jiminny through MCP
low
What we want to do for prompts/worflows and skills
Capability
Type
User value / job to be done
Example Jiminny use case
Inputs
Steps performed
Output
Draft follow-up email
Prompt
Help reps quickly create tailored follow-ups after calls
Use the latest customer call to draft an email with recap and next steps
Call ID
optional tone
optional recipient type
Retrieve call summary and action items from Jiminny.
Pull key points, commitments, and next steps.
Apply the chosen tone and structure.
Draft the email in a consistent format.
Customer-ready follow-up email draft
Summarise call
Prompt
Help users quickly extract the most important points from a conversation
Generate a concise summary of one call for internal or external use
Call ID
optional summary style
optional audience
Retrieve transcript, metadata, and structured insights.
Identify key topics, decisions, risks, and next steps.
Format output for the chosen audience.
Short or structured call summary with actions and risks
Generate deal recap
Prompt
Help reps and managers get a fast overview of a deal
Summarise the current state of a deal based on recent calls and linked CRM context
Deal ID or account name
optional date range
Retrieve calls and meetings linked to the deal.
Pull deal metadata and CRM-linked context.
Identify recent changes, blockers, risks, and next steps.
Summarise into a concise recap.
Deal recap with status, blockers, risks, and recommended next steps
Prepare weekly recap
Prompt
Help managers or reps review what changed over a time period
Weekly recap of calls, themes, and actions for one rep, team, or account
Rep ID
team ID or account ID
time period
Retrieve relevant calls and summaries for the selected time period.
Aggregate themes, action items, and changes.
Highlight notable risks, opportunities, and follow-ups.
Weekly recap with top themes, risks, opportunities, and actions
Prepare deal review
Workflow
Help reps and managers review deal status using multiple sources of context
Review a deal before pipeline review or forecast meeting
Deal ID
optional date range
Retrieve all recent calls, emails, and meetings tied to the deal.
Pull structured call summaries, score, action items, and key points.
Pull linked CRM context such as stage, contacts, and account details.
Aggregate open risks, unresolved objections, stakeholder gaps, and next steps.
Format into a review-ready structure.
Deal review document with current status, risks, stakeholders, blockers, and next steps
Weekly team recap
Workflow
Help managers monitor activity and risk across a group of reps or accounts
End-of-week team summary for sales leadership
Team ID
date range
Retrieve calls and structured insights across the team.
Group by rep, account, or deal.
Identify recurring themes, major risks, strong calls, and missed follow-ups.
Highlight calls or deals that need attention.
Team-level recap with patterns, key risks, notable wins, and action areas
Objection analysis across calls
Workflow
Help teams identify patterns across multiple conversations
Analyse pricing or competitor objections across recent calls
Account ID
deal ID
rep ID
keyword/topic
date range
Search calls matching the topic or keyword.
Retrieve summaries, transcripts, and snippets where relevant.
Identify recurring objections and how they were handled.
Assess whether objections were resolved or remain open.
Summarise patterns and examples.
Objection analysis with themes, frequency, examples, and suggested follow-up actions
Build onboarding playlist
Workflow
Help enablement teams curate useful learning content faster
Build a playlist of strong discovery, demo, or pricing calls for new hires
Topic
call type
rep/team filter
optional time period
Search for calls matching the selected criteria.
Rank or filter calls based on score, topics, or key moments.
Identify useful snippets or full recordings.
Add selected calls/snippets into a playlist structure.
Generate short descriptions for each item.
Curated onboarding playlist with selected calls/snippets and descriptions
Deal Inspection Skill
Skill
Give users a consistent, opinionated way to assess a deal
Claude reviews a deal using a repeatable Jiminny methodology
Deal ID or account name
optional review scope
Determine whether the request is suitable for a deal inspection.
Retrieve relevant calls, summaries, CRM context, stakeholders, and risks.
Apply a standard Jiminny review framework to assess deal health.
Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs.
Present findings in a consistent structure with recommended actions.
Structured deal inspection with health assessment, red flags, and recommendations
Manager Coaching Skill
Skill
Help managers review calls consistently and identify coaching moments
Analyse a rep’s call and surface strengths, gaps, and examples for coaching
Call ID
optional rep ID
optional coaching focus
Retrieve transcript, score, summary, and key moments from the call.
Evaluate the conversation against a coaching framework.
Identify what went well and where the rep could improve.
Suggest moments to clip as snippets.
Recommend coaching actions or playlist additions.
Coaching review with strengths, gaps, snippet suggestions, and recommended coaching actions
CSM / QBR Prep Skill
Skill
Help CS or AM teams prepare structured customer reviews from conversations
Generate an account review or QBR-style summary from recent customer interactions
Account ID, optional time period
Retrieve all recent calls, emails, and account-linked context.
Aggregate themes, requests, blockers, sentiment shifts, and opportunities.
Apply a repeatable account review structure.
Summarise outcomes, open issues, and suggested next steps.
Structured QBR/account review summary with themes, risks, opportunities, and actions
Snippet Builder Skill
Skill
Help users consistently capture and label useful moments from calls
Identify useful moments in a call and convert them into reusable snippets
Call ID, optional focus area such as pricing, objection, competitor, discovery
Retrieve transcript, recording timeline, and call insights.
Detect moments matching the requested focus area.
Propose snippet start/end times and titles.
Tag snippets by type or theme.
Optionally save them or add them to a playlist.
One or more proposed or saved snippets with title, timing, and tags
💡 Technical details
Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate.
Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users.
Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns.
Open Questions
Track unresolved issues or decisions that need to be made. Update as answers become available.
Question
Answer
Date Answered
Competitor research
Competitor
MCP Availability
Core Capabilities
Advanced Capabilities
Positioning
Key Strength
Key Weakness
Gong
✅ Mature MCP
List/search calls
Get transcripts
get Call metadata
Deal intelligence
Scorecards, analytics
CRM context
Enterprise data layer
Deep data + ecosystem integrations
Limited opinionated AI workflows
Avoma
✅ Mature MCP
list_meetings
get_meeting
get_transcript
key topics
Deal fields/Deal level analysis
Coaching/Scoring
CRM context
pushing insights into Notion / Confluence
AI-ready meeting intelligence
Strongest “outputs” layer
Mostly pull-based interactions
Fathom
✅ MCP
list/search meetings
get transcript
get summary/action items
Team/org context
Webhooks (event-based triggers)
get_weekly_recap
aggregated action items across meetings
creating webhooks directly from the MCP server
Lightweight meeting data access
Includes summaries + actions
Limited analytics depth
Grain
✅ Basic MCP
get meetings
download transcripts
Notes
Deals
Scorecards
Data access utility
Simple + accessible
Low sophistication, sometimes browser-based
Outreach
⚠️ MCP (workflow-focused)
Search sequences
Emails
Meetings (Kaia)
account / prospect / opportunity lookups
Workflow automation
Multi-system orchestration
Revenue workflow engine
End-to-end GTM workflows
Less depth in conversation data
Attention
⚠️ Emerging MCP
Meeting data
Transcripts
CRM-linked context
AI insights
Sales assistant workflows
create snippets
AI sales assistant + CRM
Strong CRM linkage
Less focus on external knowledge layer
Planhat -
Related content
Ask Jiminny Anything on Call level
Galya Dimitrova
Hubspot app
Galya Dimitrova
Product Vision & Strategy
Galya Dimitrova
Automated CRM Filling
Simona Georgieva (Deactivated)
Competitive analysis
Galya Dimitrova
AI agent ideas
Steliyan Georgiev
Comments
James Graham
9 Apr
A few areas around Security & Compliance that need more depth before this moves beyond draft:
Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate.
Team visibility enforcement should be its own HIGH requirement. The current note under access control ("ensure MCP only exposes data user has access to") is doing too much work as a single line. Every MCP tool response (especially search_calls and deal-level aggregations) must be filtered through the same visibility engine as the UI. Semantic search is the riskiest here; it could surface results across team boundaries if not properly scoped.
No data minimisation or scoping controls. We're exposing transcripts, CRM fields, contact details with job titles, deal context - significant PII and commercial data leaving the platform. Can customers configure which data types are exposed? Can transcripts be PII-stripped before they leave Jiminny? Given we position GDPR consent as a differentiator, we should be thinking about this.
GDPR downstream processing is unaddressed. Piping transcripts to OpenAI/Gemini introduces new data processors. Customers need to acknowledge responsibility for downstream processing during setup. We should also consider whether call participants' recording consent covers onward sharing to third-party AI - it almost certainly doesn't by default. This is one for me to pick up.
Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users.
Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns.
Audit logs should go deeper. Current spec (what data, which system, when) is a start. Add: identity behind the key, specific queries/filters used, and failed access attempts (especially cross-visibility-boundary attempts). Enterprise compliance teams will expect this.
Downgrade handling. Doc says Scale tier and above, but doesn't say what happens on downgrade. Keys should auto-deactivate.
Happy to help draft a dedicated Security & Compliance section i...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST [URL_WITH_CREDENTIALS] Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.
You said
Jiminny MCP Connector
Epic
Link to Epik in Jira
Document status
DRAFT
Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.
👤 Target user
Revenue teams using AI tools (Sales, CS, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
Build an AI Knowledge Connector (MCP-based) that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides structured insights (summaries, action items, scoring etc.)
Supports deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
Metric
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of MCP/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
User interaction and design
Simple setup flow:
Generate API key / connect MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus: usable by non-technical users
Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
Importance
Notes
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH
For each deal expose:
calls/meetings/emails
Identified risks
contacts related to the deal with their job title
linked account details
all CRM field information that we have in DI + fields used for CRM filling
example - get_deal_summary
request -
{
"tool": "get_deal_summary",
"arguments": {
"deal_name": "ACME"
}
}
responce:
{
"deal_name": "ACME",
"stage": "Negotiation",
"recent_activity": [
"Demo call on 2026-04-01",
"Pricing discussion on 2026-04-03"
],
"risks": [
"Pricing concerns",
"Security review pending"
], "next_steps": [
"Send follow-up to VP Sales",
"Share security documentation"
]
}
Ensure the AI can easily understand the schema of the data coming from Jiminny
HIGH
Models perform much better when tools have:
clear names
rich descriptions
predictable schemas
Provide clear, actionable error states for customers when MCP connections fail
HIGH
Error messages should guide agents toward solutions with specific suggestions and next steps.
Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”
Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”
Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”
Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”
Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”
Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”
No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”
Allow customers to securely control who and what data is accessible externally
HIGH
Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well.
Private meetings shouldn’t be exposed via MCP
HIGH
When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP.
Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows
HIGH
Ensure data is structured to be easily combined with:
CRM (accounts, deals)
external docs
Use consistent identifiers:
account IDs
deal IDs
MCP is available only for Scale tier and above
HIGH
Customers who are on Capture tier should be able to access any of their data through MCP.
This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working.
Provide visibility into how external AI tools access Jiminny data
HIGH
Audit logs:
what data was accessed by which external system when
identity - who made the request
specific queries/filters used
failed access attempts (especially cross-visibility-boundary attempts)
Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it.
example from Atlassian -
Provide customers with example prompts to get started quickly
HIGH
MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments.
See examples in table below.
Expose Jiminny as a Connector in Claude
medium
Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny
Provide customers with example workflows to get started quickly
medium
A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.
See examples in table below.
Allow customers to use predefined Jiminny skills for common workflows
medium
Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.
Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com
See examples in table below.
Jiminny UI widget for OpenAI
medium
The reason to have this is to make certain Jiminny use cases much easier than pure chat.
Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.
For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.
Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.
Allow customers to run long-running analyses asynchronously and retrieve progress/results
low
Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to:
start the job
let it run in the background
return a status like “in progress”
let the client come back for the result later
For example - User asks in Claude:
“Analyse all calls from this quarter and tell me the top 5 objections by segment.”
That might require:
searching lots of calls
pulling many summaries/transcripts
aggregating results
Instead of making Claude wait 45+ seconds, Jiminny could:
create an analysis job
return: job_id = 123, status = running
We need this in order to support:
large-scale analysis
multi-call aggregation across lots of data
playlist generation across many calls
heavy recap/report generation
Creating a snippet through the MCP
low
Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team.
example request:
{
"tool": "create_snippet",
"arguments": {
"call_id": "call_123",
"start_time": "00:18:24",
"end_time": "00:19:10",
"title": "Pricing objection"
}
}
example response:
{
"snippet_id": "snip_987",
"title": "Pricing objection",
"share_url": "https://app.jiminny.com/snippets/snip_987"
}
Ability to add recordings or snippets to a playlist
low
Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes
Allow AI tools to read reusable Jiminny resources
low
fathom
Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be:
current deal snapshot
account summary
weekly rep recap
approved talk tracks / objection handling guidance
curated playlists or snippet libraries
This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls.
Ability to write coaching notes in Jiminny through MCP
low
What we want to do for prompts/worflows and skills
Capability
Type
User value / job to be done
Example Jiminny use case
Inputs
Steps performed
Output
Draft follow-up email
Prompt
Help reps quickly create tailored follow-ups after calls
Use the latest customer call to draft an email with recap and next steps
Call ID
optional tone
optional recipient type
Retrieve call summary and action items from Jiminny.
Pull key points, commitments, and next steps.
Apply the chosen tone and structure.
Draft the email in a consistent format.
Customer-ready follow-up email draft
Summarise call
Prompt
Help users quickly extract the most important points from a conversation
Generate a concise summary of one call for internal or external use
Call ID
optional summary style
optional audience
Retrieve transcript, metadata, and structured insights.
Identify key topics, decisions, risks, and next steps.
Format output for the chosen audience.
Short or structured call summary with actions and risks
Generate deal recap
Prompt
Help reps and managers get a fast overview of a deal
Summarise the current state of a deal based on recent calls and linked CRM context
Deal ID or account name
optional date range
Retrieve calls and meetings linked to the deal.
Pull deal metadata and CRM-linked context.
Identify recent changes, blockers, risks, and next steps.
Summarise into a concise recap.
Deal recap with status, blockers, risks, and recommended next steps
Prepare weekly recap
Prompt
Help managers or reps review what changed over a time period
Weekly recap of calls, themes, and actions for one rep, team, or account
Rep ID
team ID or account ID
time period
Retrieve relevant calls and summaries for the selected time period.
Aggregate themes, action items, and changes.
Highlight notable risks, opportunities, and follow-ups.
Weekly recap with top themes, risks, opportunities, and actions
Prepare deal review
Workflow
Help reps and managers review deal status using multiple sources of context
Review a deal before pipeline review or forecast meeting
Deal ID
optional date range
Retrieve all recent calls, emails, and meetings tied to the deal.
Pull structured call summaries, score, action items, and key points.
Pull linked CRM context such as stage, contacts, and account details.
Aggregate open risks, unresolved objections, stakeholder gaps, and next steps.
Format into a review-ready structure.
Deal review document with current status, risks, stakeholders, blockers, and next steps
Weekly team recap
Workflow
Help managers monitor activity and risk across a group of reps or accounts
End-of-week team summary for sales leadership
Team ID
date range
Retrieve calls and structured insights across the team.
Group by rep, account, or deal.
Identify recurring themes, major risks, strong calls, and missed follow-ups.
Highlight calls or deals that need attention.
Team-level recap with patterns, key risks, notable wins, and action areas
Objection analysis across calls
Workflow
Help teams identify patterns across multiple conversations
Analyse pricing or competitor objections across recent calls
Account ID
deal ID
rep ID
keyword/topic
date range
Search calls matching the topic or keyword.
Retrieve summaries, transcripts, and snippets where relevant.
Identify recurring objections and how they were handled.
Assess whether objections were resolved or remain open.
Summarise patterns and examples.
Objection analysis with themes, frequency, examples, and suggested follow-up actions
Build onboarding playlist
Workflow
Help enablement teams curate useful learning content faster
Build a playlist of strong discovery, demo, or pricing calls for new hires
Topic
call type
rep/team filter
optional time period
Search for calls matching the selected criteria.
Rank or filter calls based on score, topics, or key moments.
Identify useful snippets or full recordings.
Add selected calls/snippets into a playlist structure.
Generate short descriptions for each item.
Curated onboarding playlist with selected calls/snippets and descriptions
Deal Inspection Skill
Skill
Give users a consistent, opinionated way to assess a deal
Claude reviews a deal using a repeatable Jiminny methodology
Deal ID or account name
optional review scope
Determine whether the request is suitable for a deal inspection.
Retrieve relevant calls, summaries, CRM context, stakeholders, and risks.
Apply a standard Jiminny review framework to assess deal health.
Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs.
Present findings in a consistent structure with recommended actions.
Structured deal inspection with health assessment, red flags, and recommendations
Manager Coaching Skill
Skill
Help managers review calls consistently and identify coaching moments
Analyse a rep’s call and surface strengths, gaps, and examples for coaching
Call ID
optional rep ID
optional coaching focus
Retrieve transcript, score, summary, and key moments from the call.
Evaluate the conversation against a coaching framework.
Identify what went well and where the rep could improve.
Suggest moments to clip as snippets.
Recommend coaching actions or playlist additions.
Coaching review with strengths, gaps, snippet suggestions, and recommended coaching actions
CSM / QBR Prep Skill
Skill
Help CS or AM teams prepare structured customer reviews from conversations
Generate an account review or QBR-style summary from recent customer interactions
Account ID, optional time period
Retrieve all recent calls, emails, and account-linked context.
Aggregate themes, requests, blockers, sentiment shifts, and opportunities.
Apply a repeatable account review structure.
Summarise outcomes, open issues, and suggested next steps.
Structured QBR/account review summary with themes, risks, opportunities, and actions
Snippet Builder Skill
Skill
Help users consistently capture and label useful moments from calls
Identify useful moments in a call and convert them into reusable snippets
Call ID, optional focus area such as pricing, objection, competitor, discovery
Retrieve transcript, recording timeline, and call insights.
Detect moments matching the requested focus area.
Propose snippet start/end times and titles.
Tag snippets by type or theme.
Optionally save them or add them to a playlist.
One or more proposed or saved snippets with title, timing, and tags
💡 Technical details
Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate.
Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users.
Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns.
Open Questions
Track unresolved issues or decisions that need to be made. Update as answers become available.
Question
Answer
Date Answered
Competitor research
Competitor
MCP Availability
Core Capabilities
Advanced Capabilities
Positioning
Key Strength
Key Weakness
Gong
✅ Mature MCP
List/search calls
Get transcripts
get Call metadata
Deal intelligence
Scorecards, analytics
CRM context
Enterprise data layer
Deep data + ecosystem integrations
Limited opinionated AI workflows
Avoma
✅ Mature MCP
list_meetings
get_meeting
get_transcript
key topics
Deal fields/Deal level analysis
Coaching/Scoring
CRM context
pushing insights into Notion / Confluence
AI-ready meeting intelligence
Strongest “outputs” layer
Mostly pull-based interactions
Fathom
✅ MCP
list/search meetings
get transcript
get summary/action items
Team/org context
Webhooks (event-based triggers)
get_weekly_recap
aggregated action items across meetings
creating webhooks directly from the MCP server
Lightweight meeting data access
Includes summaries + actions
Limited analytics depth
Grain
✅ Basic MCP
get meetings
download transcripts
Notes
Deals
Scorecards
Data access utility
Simple + accessible
Low sophistication, sometimes browser-based
Outreach
⚠️ MCP (workflow-focused)
Search sequences
Emails
Meetings (Kaia)
account / prospect / opportunity lookups
Workflow automation
Multi-system orchestration
Revenue workflow engine
End-to-end GTM workflows
Less depth in conversation data
Attention
⚠️ Emerging MCP
Meeting data
Transcripts
CRM-linked context
AI insights
Sales assistant workflows
create snippets
AI sales assistant + CRM
Strong CRM linkage
Less focus on external knowledge layer
Planhat -
Related content
Ask Jiminny Anything on Call level
Galya Dimitrova
Hubspot app
Galya Dimitrova
Product Vision & Strategy
Galya Dimitrova
Automated CRM Filling
Simona Georgieva (Deactivated)
Competitive analysis
Galya Dimitrova
AI agent ideas
Steliyan Georgiev
Comments
James Graham
9 Apr
A few areas around Security & Compliance that need more depth before this moves beyond draft:
Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate.
Team visibility enforcement should be its own HIGH requirement. The current note under access control ("ensure MCP only exposes data user has access to") is doing too much work as a single line. Every MCP tool response (especially search_calls and deal-level aggregations) must be filtered through the same visibility engine as the UI. Semantic search is the riskiest here; it could surface results across team boundaries if not properly scoped.
No data minimisation or scoping controls. We're exposing transcripts, CRM fields, contact details with job titles, deal context - significant PII and commercial data leaving the platform. Can customers configure which data types are exposed? Can transcripts be PII-stripped before they leave Jiminny? Given we position GDPR consent as a differentiator, we should be thinking about this.
GDPR downstream processing is unaddressed. Piping transcripts to OpenAI/Gemini introduces new data processors. Customers need to acknowledge responsibility for downstream processing during setup. We should also consider whether call participants' recording consent covers onward sharing to third-party AI - it almost certainly doesn't by default. This is one for me to pick up.
Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users.
Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns.
Audit logs should go deeper. Current spec (what data, which system, when) is a start. Add: identity behind the key, specific queries/filters used, and failed access attempts (especially cross-visibility-boundary attempts). Enterprise compliance teams will expect this.
Downgrade handling. Doc says Scale tier and above, but doesn't say what happens on downgrade. Keys should auto-deactivate.
Happy to help draft a dedicated Security & Compliance section i...
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Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product. 👤 Target user Revenue teams using AI tools (Sales, CS, RevOps) Companies already experimenting with Claude / OpenAI Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc) 🤕 Pain point or problem Jiminny data is locked in the platform AI tools lack access to high-value conversation context Customers must manually export/copy transcripts No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people Peer pressure as almost all competitors have this 💥 Impact and benefits For customers Better AI outputs using real customer conversations Ability to build automated workflows across tools For Jiminny Increased product stickiness (embedded in workflows) Competitive parity with Gong / Avoma etc. 💡 Solution ideas Build an AI Knowledge Connector (MCP-based) that: Exposes Jiminny data (calls, transcripts, metadata) to AI tools Provides structured insights (summaries, action items, scoring etc.) Supports deal-level context Allows integration into customer-owned AI stacks (Claude + Zapier, etc.) Success metrics List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets). Goal Metric Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows % of customers connecting external AI tools Number of MCP/API calls per customer - and which ones are being used the most Decrease the manual copying of transcripts decrease by X % User interaction and design Simple setup flow: Generate API key / connect MCP Clear instructions for Claude / Zapier setup Provide: Example prompts for Claude Pre-built workflow templates for Zapier Clear documentation - with use cases 👉 Focus: usable by non-technical users Detailed Requirements List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification. Requirement Importance Notes Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI) HIGH Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack Allow customers to access call data through AI tools HIGH Expose Jiminny data: list calls (filter by date, user, account, deal) get call get transcript (speaker-separated) get call metadata search calls (keyword + semantic) Support filtering by: account / contact/ lead / deal jiminny user date range Allow customers to search and query calls using AI tools HIGH Support keyword and semantic search across conversations Provide structured insights so customers can generate better outputs HIGH For each call, expose: Summary Action items Key points Activity type Notes Call Score CRM information Allow customers to understand deal status across multiple calls/emails HIGH For each deal expose: calls/meetings/emails Identified risks contacts related to the deal with their job title linked account details all CRM field information that we have in DI + fields used for CRM filling example - get_deal_summary request - { \"tool\": \"get_deal_summary\", \"arguments\": { \"deal_name\": \"ACME\" } } responce: { \"deal_name\": \"ACME\", \"stage\": \"Negotiation\", \"recent_activity\": [ \"Demo call on 2026-04-01\", \"Pricing discussion on 2026-04-03\" ], \"risks\": [ \"Pricing concerns\", \"Security review pending\" ], \"next_steps\": [ \"Send follow-up to VP Sales\", \"Share security documentation\" ] } Ensure the AI can easily understand the schema of the data coming from Jiminny HIGH Models perform much better when tools have: clear names rich descriptions predictable schemas Provide clear, actionable error states for customers when MCP connections fail HIGH Error messages should guide agents toward solutions with specific suggestions and next steps. Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.” Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.” Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.” Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.” Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.” Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.” No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.” Allow customers to securely control who and what data is accessible externally HIGH Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well. Private meetings shouldn’t be exposed via MCP HIGH When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP. Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows HIGH Ensure data is structured to be easily combined with: CRM (accounts, deals) external docs Use consistent identifiers: account IDs deal IDs MCP is available only for Scale tier and above HIGH Customers who are on Capture tier should be able to access any of their data through MCP. This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working. Provide visibility into how external AI tools access Jiminny data HIGH Audit logs: what data was accessed by which external system when identity - who made the request specific queries/filters used failed access attempts (especially cross-visibility-boundary attempts) Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it. example from Atlassian - Provide customers with example prompts to get started quickly HIGH MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments. See examples in table below. Expose Jiminny as a Connector in Claude medium Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny Provide customers with example workflows to get started quickly medium A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome. See examples in table below. Allow customers to use predefined Jiminny skills for common workflows medium Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job. Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com See examples in table below. Jiminny UI widget for OpenAI medium The reason to have this is to make certain Jiminny use cases much easier than pure chat. Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it. For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface. Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section. Allow customers to run long-running analyses asynchronously and retrieve progress/results low Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to: start the job let it run in the background return a status like “in progress” let the client come back for the result later For example - User asks in Claude: “Analyse all calls from this quarter and tell me the top 5 objections by segment.” That might require: searching lots of calls pulling many summaries/transcripts aggregating results Instead of making Claude wait 45+ seconds, Jiminny could: create an analysis job return: job_id = 123, status = running We need this in order to support: large-scale analysis multi-call aggregation across lots of data playlist generation across many calls heavy recap/report generation Creating a snippet through the MCP low Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team. example request: { \"tool\": \"create_snippet\", \"arguments\": { \"call_id\": \"call_123\", \"start_time\": \"00:18:24\", \"end_time\": \"00:19:10\", \"title\": \"Pricing objection\" } } example response: { \"snippet_id\": \"snip_987\", \"title\": \"Pricing objection\", \"share_url\": \"https://app.jiminny.com/snippets/snip_987\" } Ability to add recordings or snippets to a playlist low Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes Allow AI tools to read reusable Jiminny resources low fathom Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be: current deal snapshot account summary weekly rep recap approved talk tracks / objection handling guidance curated playlists or snippet libraries This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls. Ability to write coaching notes in Jiminny through MCP low What we want to do for prompts/worflows and skills Capability Type User value / job to be done Example Jiminny use case Inputs Steps performed Output Draft follow-up email Prompt Help reps quickly create tailored follow-ups after calls Use the latest customer call to draft an email with recap and next steps Call ID optional tone optional recipient type Retrieve call summary and action items from Jiminny. Pull key points, commitments, and next steps. Apply the chosen tone and structure. Draft the email in a consistent format. Customer-ready follow-up email draft Summarise call Prompt Help users quickly extract the most important points from a conversation Generate a concise summary of one call for internal or external use Call ID optional summary style optional audience Retrieve transcript, metadata, and structured insights. Identify key topics, decisions, risks, and next steps. Format output for the chosen audience. Short or structured call summary with actions and risks Generate deal recap Prompt Help reps and managers get a fast overview of a deal Summarise the current state of a deal based on recent calls and linked CRM context Deal ID or account name optional date range Retrieve calls and meetings linked to the deal. Pull deal metadata and CRM-linked context. Identify recent changes, blockers, risks, and next steps. Summarise into a concise recap. Deal recap with status, blockers, risks, and recommended next steps Prepare weekly recap Prompt Help managers or reps review what changed over a time period Weekly recap of calls, themes, and actions for one rep, team, or account Rep ID team ID or account ID time period Retrieve relevant calls and summaries for the selected time period. Aggregate themes, action items, and changes. Highlight notable risks, opportunities, and follow-ups. Weekly recap with top themes, risks, opportunities, and actions Prepare deal review Workflow Help reps and managers review deal status using multiple sources of context Review a deal before pipeline review or forecast meeting Deal ID optional date range Retrieve all recent calls, emails, and meetings tied to the deal. Pull structured call summaries, score, action items, and key points. Pull linked CRM context such as stage, contacts, and account details. Aggregate open risks, unresolved objections, stakeholder gaps, and next steps. Format into a review-ready structure. Deal review document with current status, risks, stakeholders, blockers, and next steps Weekly team recap Workflow Help managers monitor activity and risk across a group of reps or accounts End-of-week team summary for sales leadership Team ID date range Retrieve calls and structured insights across the team. Group by rep, account, or deal. Identify recurring themes, major risks, strong calls, and missed follow-ups. Highlight calls or deals that need attention. Team-level recap with patterns, key risks, notable wins, and action areas Objection analysis across calls Workflow Help teams identify patterns across multiple conversations Analyse pricing or competitor objections across recent calls Account ID deal ID rep ID keyword/topic date range Search calls matching the topic or keyword. Retrieve summaries, transcripts, and snippets where relevant. Identify recurring objections and how they were handled. Assess whether objections were resolved or remain open. Summarise patterns and examples. Objection analysis with themes, frequency, examples, and suggested follow-up actions Build onboarding playlist Workflow Help enablement teams curate useful learning content faster Build a playlist of strong discovery, demo, or pricing calls for new hires Topic call type rep/team filter optional time period Search for calls matching the selected criteria. Rank or filter calls based on score, topics, or key moments. Identify useful snippets or full recordings. Add selected calls/snippets into a playlist structure. Generate short descriptions for each item. Curated onboarding playlist with selected calls/snippets and descriptions Deal Inspection Skill Skill Give users a consistent, opinionated way to assess a deal Claude reviews a deal using a repeatable Jiminny methodology Deal ID or account name optional review scope Determine whether the request is suitable for a deal inspection. Retrieve relevant calls, summaries, CRM context, stakeholders, and risks. Apply a standard Jiminny review framework to assess deal health. Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs. Present findings in a consistent structure with recommended actions. Structured deal inspection with health assessment, red flags, and recommendations Manager Coaching Skill Skill Help managers review calls consistently and identify coaching moments Analyse a rep’s call and surface strengths, gaps, and examples for coaching Call ID optional rep ID optional coaching focus Retrieve transcript, score, summary, and key moments from the call. Evaluate the conversation against a coaching framework. Identify what went well and where the rep could improve. Suggest moments to clip as snippets. Recommend coaching actions or playlist additions. Coaching review with strengths, gaps, snippet suggestions, and recommended coaching actions CSM / QBR Prep Skill Skill Help CS or AM teams prepare structured customer reviews from conversations Generate an account review or QBR-style summary from recent customer interactions Account ID, optional time period Retrieve all recent calls, emails, and account-linked context. Aggregate themes, requests, blockers, sentiment shifts, and opportunities. Apply a repeatable account review structure. Summarise outcomes, open issues, and suggested next steps. Structured QBR/account review summary with themes, risks, opportunities, and actions Snippet Builder Skill Skill Help users consistently capture and label useful moments from calls Identify useful moments in a call and convert them into reusable snippets Call ID, optional focus area such as pricing, objection, competitor, discovery Retrieve transcript, recording timeline, and call insights. Detect moments matching the requested focus area. Propose snippet start/end times and titles. Tag snippets by type or theme. Optionally save them or add them to a playlist. One or more proposed or saved snippets with title, timing, and tags 💡 Technical details Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate. Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users. Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns. Open Questions Track unresolved issues or decisions that need to be made. Update as answers become available. Question Answer Date Answered Competitor research Competitor MCP Availability Core Capabilities Advanced Capabilities Positioning Key Strength Key Weakness Gong ✅ Mature MCP List/search calls Get transcripts get Call metadata Deal intelligence Scorecards, analytics CRM context Enterprise data layer Deep data + ecosystem integrations Limited opinionated AI workflows Avoma ✅ Mature MCP list_meetings get_meeting get_transcript key topics Deal fields/Deal level analysis Coaching/Scoring CRM context pushing insights into Notion / Confluence AI-ready meeting intelligence Strongest “outputs” layer Mostly pull-based interactions Fathom ✅ MCP list/search meetings get transcript get summary/action items Team/org context Webhooks (event-based triggers) get_weekly_recap aggregated action items across meetings creating webhooks directly from the MCP server Lightweight meeting data access Includes summaries + actions Limited analytics depth Grain ✅ Basic MCP get meetings download transcripts Notes Deals Scorecards Data access utility Simple + accessible Low sophistication, sometimes browser-based Outreach ⚠️ MCP (workflow-focused) Search sequences Emails Meetings (Kaia) account / prospect / opportunity lookups Workflow automation Multi-system orchestration Revenue workflow engine End-to-end GTM workflows Less depth in conversation data Attention ⚠️ Emerging MCP Meeting data Transcripts CRM-linked context AI insights Sales assistant workflows create snippets AI sales assistant + CRM Strong CRM linkage Less focus on external knowledge layer Planhat - Related content Ask Jiminny Anything on Call level Galya Dimitrova Hubspot app Galya Dimitrova Product Vision & Strategy Galya Dimitrova Automated CRM Filling Simona Georgieva (Deactivated) Competitive analysis Galya Dimitrova AI agent ideas Steliyan Georgiev Comments James Graham 9 Apr A few areas around Security & Compliance that need more depth before this moves beyond draft: Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate. Team visibility enforcement should be its own HIGH requirement. The current note under access control (\"ensure MCP only exposes data user has access to\") is doing too much work as a single line. Every MCP tool response (especially search_calls and deal-level aggregations) must be filtered through the same visibility engine as the UI. Semantic search is the riskiest here; it could surface results across team boundaries if not properly scoped. No data minimisation or scoping controls. We're exposing transcripts, CRM fields, contact details with job titles, deal context - significant PII and commercial data leaving the platform. Can customers configure which data types are exposed? Can transcripts be PII-stripped before they leave Jiminny? Given we position GDPR consent as a differentiator, we should be thinking about this. GDPR downstream processing is unaddressed. Piping transcripts to OpenAI/Gemini introduces new data processors. Customers need to acknowledge responsibility for downstream processing during setup. We should also consider whether call participants' recording consent covers onward sharing to third-party AI - it almost certainly doesn't by default. This is one for me to pick up. Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users. Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns. Audit logs should go deeper. Current spec (what data, which system, when) is a start. Add: identity behind the key, specific queries/filters used, and failed access attempts (especially cross-visibility-boundary attempts). Enterprise compliance teams will expect this. Downgrade handling. Doc says Scale tier and above, but doesn't say what happens on downgrade. Keys should auto-deactivate. Happy to help draft a dedicated Security & Compliance section if useful. Galya Dimitrova 15 Apr Thanks for the input @James Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.","depth":21,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"You said","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny MCP Connector","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Epic","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Link to Epik in Jira","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Document status","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"DRAFT","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Objective","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"👤 Target user","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Revenue teams using AI tools (Sales, CS, RevOps)","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Companies already experimenting with Claude / OpenAI","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"🤕 Pain point or problem","depth":23,"bounds":{"left":0.30868056,"top":0.125,"width":0.077430554,"height":0.054444443},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny data is locked in the platform","depth":23,"bounds":{"left":0.30868056,"top":0.18777777,"width":0.074652776,"height":0.085},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"AI tools lack access to high-value conversation context","depth":23,"bounds":{"left":0.30868056,"top":0.28111112,"width":0.10555556,"height":0.085},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Customers must manually export/copy transcripts","depth":23,"bounds":{"left":0.30868056,"top":0.37444445,"width":0.108333334,"height":0.085},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"No easy way to combine calls with CRM + docs + other info - 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The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Trying to access private meeting - “Error: Access denied. 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This is not a data or permissions issue — please retry in a few minutes.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to securely control who and what data is accessible externally","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ensure MCP only exposes data user has access to - based on team visibility settings. 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This will make it easier for customers to find us and to connect Jiminny","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide customers with example workflows to get started quickly","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"See examples in table below.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to use predefined Jiminny skills for common workflows","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"See examples in table below.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny UI widget for OpenAI","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The reason to have this is to make certain Jiminny use cases much easier than pure chat.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to run long-running analyses asynchronously and retrieve progress/results","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Some requests/questions require analysis of Hugh volumes of data. 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Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"example request:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"{","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"tool\": \"create_snippet\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"arguments\": {","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"call_id\": \"call_123\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"start_time\": \"00:18:24\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"end_time\": \"00:19:10\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"title\": \"Pricing objection\"","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"example response:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"{","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"snippet_id\": \"snip_987\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"title\": \"Pricing objection\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"share_url\": \"https://app.jiminny.com/snippets/snip_987\"","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ability to add recordings or snippets to a playlist","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow AI tools to read reusable Jiminny resources","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"fathom","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"current deal snapshot","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"account summary","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"weekly rep recap","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"approved talk tracks / objection handling guidance","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"curated playlists or snippet libraries","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ability to write coaching notes in Jiminny through MCP","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"What we want to do for prompts/worflows and skills","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Capability","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Type","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"User value / job to be done","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Example Jiminny use 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Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Team visibility enforcement should be its own HIGH requirement. The current note under access control (\"ensure MCP only exposes data user has access to\") is doing too much work as a single line. Every MCP tool response (especially search_calls and deal-level aggregations) must be filtered through the same visibility engine as the UI. Semantic search is the riskiest here; it could surface results across team boundaries if not properly scoped.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"No data minimisation or scoping controls. We're exposing transcripts, CRM fields, contact details with job titles, deal context - significant PII and commercial data leaving the platform. Can customers configure which data types are exposed? Can transcripts be PII-stripped before they leave Jiminny? Given we position GDPR consent as a differentiator, we should be thinking about this.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"GDPR downstream processing is unaddressed. Piping transcripts to OpenAI/Gemini introduces new data processors. Customers need to acknowledge responsibility for downstream processing during setup. We should also consider whether call participants' recording consent covers onward sharing to third-party AI - it almost certainly doesn't by default. This is one for me to pick up.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. 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Keys should auto-deactivate.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Happy to help draft a dedicated Security & Compliance section if useful.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Galya Dimitrova","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"15 Apr","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Thanks for the input @James Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Expand","depth":21,"role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXButton","text":"Listen","depth":21,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXButton","text":"Show more options","depth":20,"role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false}]...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST [URL_WITH_CREDENTIALS] Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.
You said
Jiminny MCP Connector
Epic
Link to Epik in Jira
Document status
DRAFT
Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.
👤 Target user
Revenue teams using AI tools (Sales, CS, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
Build an AI Knowledge Connector (MCP-based) that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides structured insights (summaries, action items, scoring etc.)
Supports deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
Metric
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of MCP/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
User interaction and design
Simple setup flow:
Generate API key / connect MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus: usable by non-technical users
Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
Importance
Notes
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH
For each deal expose:
calls/meetings/emails
Identified risks
contacts related to the deal with their job title
linked account details
all CRM field information that we have in DI + fields used for CRM filling
example - get_deal_summary
request -
{
"tool": "get_deal_summary",
"arguments": {
"deal_name": "ACME"
}
}
responce:
{
"deal_name": "ACME",
"stage": "Negotiation",
"recent_activity": [
"Demo call on 2026-04-01",
"Pricing discussion on 2026-04-03"
],
"risks": [
"Pricing concerns",
"Security review pending"
], "next_steps": [
"Send follow-up to VP Sales",
"Share security documentation"
]
}
Ensure the AI can easily understand the schema of the data coming from Jiminny
HIGH
Models perform much better when tools have:
clear names
rich descriptions
predictable schemas
Provide clear, actionable error states for customers when MCP connections fail
HIGH
Error messages should guide agents toward solutions with specific suggestions and next steps.
Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”
Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”
Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”
Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”
Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”
Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”
No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”
Allow customers to securely control who and what data is accessible externally
HIGH
Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well.
Private meetings shouldn’t be exposed via MCP
HIGH
When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP.
Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows
HIGH
Ensure data is structured to be easily combined with:
CRM (accounts, deals)
external docs
Use consistent identifiers:
account IDs
deal IDs
MCP is available only for Scale tier and above
HIGH
Customers who are on Capture tier should be able to access any of their data through MCP.
This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working.
Provide visibility into how external AI tools access Jiminny data
HIGH
Audit logs:
what data was accessed by which external system when
identity - who made the request
specific queries/filters used
failed access attempts (especially cross-visibility-boundary attempts)
Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it.
example from Atlassian -
Provide customers with example prompts to get started quickly
HIGH
MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments.
See examples in table below.
Expose Jiminny as a Connector in Claude
medium
Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny
Provide customers with example workflows to get started quickly
medium
A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.
See examples in table below.
Allow customers to use predefined Jiminny skills for common workflows
medium
Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.
Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com
See examples in table below.
Jiminny UI widget for OpenAI
medium
The reason to have this is to make certain Jiminny use cases much easier than pure chat.
Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.
For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.
Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.
Allow customers to run long-running analyses asynchronously and retrieve progress/results
low
Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to:
start the job
let it run in the background
return a status like “in progress”
let the client come back for the result later
For example - User asks in Claude:
“Analyse all calls from this quarter and tell me the top 5 objections by segment.”
That might require:
searching lots of calls
pulling many summaries/transcripts
aggregating results
Instead of making Claude wait 45+ seconds, Jiminny could:
create an analysis job
return: job_id = 123, status = running
We need this in order to support:
large-scale analysis
multi-call aggregation across lots of data
playlist generation across many calls
heavy recap/report generation
Creating a snippet through the MCP
low
Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team.
example request:
{
"tool": "create_snippet",
"arguments": {
"call_id": "call_123",
"start_time": "00:18:24",
"end_time": "00:19:10",
"title": "Pricing objection"
}
}
example response:
{
"snippet_id": "snip_987",
"title": "Pricing objection",
"share_url": "https://app.jiminny.com/snippets/snip_987"
}
Ability to add recordings or snippets to a playlist
low
Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes
Allow AI tools to read reusable Jiminny resources
low
fathom
Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be:
current deal snapshot
account summary
weekly rep recap
approved talk tracks / objection handling guidance
curated playlists or snippet libraries
This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls.
Ability to write coaching notes in Jiminny through MCP
low
What we want to do for prompts/worflows and skills
Capability
Type
User value / job to be done
Example Jiminny use case
Inputs
Steps performed
Output
Draft follow-up email
Prompt
Help reps quickly create tailored follow-ups after calls
Use the latest customer call to draft an email with recap and next steps
Call ID
optional tone
optional recipient type
Retrieve call summary and action items from Jiminny.
Pull key points, commitments, and next steps.
Apply the chosen tone and structure.
Draft the email in a consistent format.
Customer-ready follow-up email draft
Summarise call
Prompt
Help users quickly extract the most important points from a conversation
Generate a concise summary of one call for internal or external use
Call ID
optional summary style
optional audience
Retrieve transcript, metadata, and structured insights.
Identify key topics, decisions, risks, and next steps.
Format output for the chosen audience.
Short or structured call summary with actions and risks
Generate deal recap
Prompt
Help reps and managers get a fast overview of a deal
Summarise the current state of a deal based on recent calls and linked CRM context
Deal ID or account name
optional date range
Retrieve calls and meetings linked to the deal.
Pull deal metadata and CRM-linked context.
Identify recent changes, blockers, risks, and next steps.
Summarise into a concise recap.
Deal recap with status, blockers, risks, and recommended next steps
Prepare weekly recap
Prompt
Help managers or reps review what changed over a time period
Weekly recap of calls, themes, and actions for one rep, team, or account
Rep ID
team ID or account ID
time period
Retrieve relevant calls and summaries for the selected time period.
Aggregate themes, action items, and changes.
Highlight notable risks, opportunities, and follow-ups.
Weekly recap with top themes, risks, opportunities, and actions
Prepare deal review
Workflow
Help reps and managers review deal status using multiple sources of context
Review a deal before pipeline review or forecast meeting
Deal ID
optional date range
Retrieve all recent calls, emails, and meetings tied to the deal.
Pull structured call summaries, score, action items, and key points.
Pull linked CRM context such as stage, contacts, and account details.
Aggregate open risks, unresolved objections, stakeholder gaps, and next steps.
Format into a review-ready structure.
Deal review document with current status, risks, stakeholders, blockers, and next steps
Weekly team recap
Workflow
Help managers monitor activity and risk across a group of reps or accounts
End-of-week team summary for sales leadership
Team ID
date range
Retrieve calls and structured insights across the team.
Group by rep, account, or deal.
Identify recurring themes, major risks, strong calls, and missed follow-ups.
Highlight calls or deals that need attention.
Team-level recap with patterns, key risks, notable wins, and action areas
Objection analysis across calls
Workflow
Help teams identify patterns across multiple conversations
Analyse pricing or competitor objections across recent calls
Account ID
deal ID
rep ID
keyword/topic
date range
Search calls matching the topic or keyword.
Retrieve summaries, transcripts, and snippets where relevant.
Identify recurring objections and how they were handled.
Assess whether objections were resolved or remain open.
Summarise patterns and examples.
Objection analysis with themes, frequency, examples, and suggested follow-up actions
Build onboarding playlist
Workflow
Help enablement teams curate useful learning content faster
Build a playlist of strong discovery, demo, or pricing calls for new hires
Topic
call type
rep/team filter
optional time period
Search for calls matching the selected criteria.
Rank or filter calls based on score, topics, or key moments.
Identify useful snippets or full recordings.
Add selected calls/snippets into a playlist structure.
Generate short descriptions for each item.
Curated onboarding playlist with selected calls/snippets and descriptions
Deal Inspection Skill
Skill
Give users a consistent, opinionated way to assess a deal
Claude reviews a deal using a repeatable Jiminny methodology
Deal ID or account name
optional review scope
Determine whether the request is suitable for a deal inspection.
Retrieve relevant calls, summaries, CRM context, stakeholders, and risks.
Apply a standard Jiminny review framework to assess deal health.
Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs.
Present findings in a consistent structure with recommended actions.
Structured deal inspection with health assessment, red flags, and recommendations
Manager Coaching Skill
Skill
Help managers review calls consistently and identify coaching moments
Analyse a rep’s call and surface strengths, gaps, and examples for coaching
Call ID
optional rep ID
optional coaching focus
Retrieve transcript, score, summary, and key moments from the call.
Evaluate the conversation against a coaching framework.
Identify what went well and where the rep could improve.
Suggest moments to clip as snippets.
Recommend coaching actions or playlist additions.
Coaching review with strengths, gaps, snippet suggestions, and recommended coaching actions
CSM / QBR Prep Skill
Skill
Help CS or AM teams prepare structured customer reviews from conversations
Generate an account review or QBR-style summary from recent customer interactions
Account ID, optional time period
Retrieve all recent calls, emails, and account-linked context.
Aggregate themes, requests, blockers, sentiment shifts, and opportunities.
Apply a repeatable account review structure.
Summarise outcomes, open issues, and suggested next steps.
Structured QBR/account review summary with themes, risks, opportunities, and actions
Snippet Builder Skill
Skill
Help users consistently capture and label useful moments from calls
Identify useful moments in a call and convert them into reusable snippets
Call ID, optional focus area such as pricing, objection, competitor, discovery
Retrieve transcript, recording timeline, and call insights.
Detect moments matching the requested focus area.
Propose snippet start/end times and titles.
Tag snippets by type or theme.
Optionally save them or add them to a playlist.
One or more proposed or saved snippets with title, timing, and tags
💡 Technical details
Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate.
Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users.
Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns.
Open Questions
Track unresolved issues or decisions that need to be made. Update as answers become available.
Question
Answer
Date Answered
Competitor research
Competitor
MCP Availability
Core Capabilities
Advanced Capabilities
Positioning
Key Strength
Key Weakness
Gong
✅ Mature MCP
List/search calls
Get transcripts
get Call metadata
Deal intelligence
Scorecards, analytics
CRM context
Enterprise data layer
Deep data + ecosystem integrations
Limited opinionated AI workflows
Avoma
✅ Mature MCP
list_meetings
get_meeting
get_transcript
key topics
Deal fields/Deal level analysis
Coaching/Scoring
CRM context
pushing insights into Notion / Confluence
AI-ready meeting intelligence
Strongest “outputs” layer
Mostly pull-based interactions
Fathom
✅ MCP
list/search meetings
get transcript
get summary/action items
Team/org context
Webhooks (event-based triggers)
get_weekly_recap
aggregated action items across meetings
creating webhooks directly from the MCP server
Lightweight meeting data access
Includes summaries + actions
Limited analytics depth
Grain
✅ Basic MCP
get meetings
download transcripts
Notes
Deals
Scorecards
Data access utility
Simple + accessible
Low sophistication, sometimes browser-based
Outreach
⚠️ MCP (workflow-focused)
Search sequences
Emails
Meetings (Kaia)
account / prospect / opportunity lookups
Workflow automation
Multi-system orchestration
Revenue workflow engine
End-to-end GTM workflows
Less depth in conversation data
Attention
⚠️ Emerging MCP
Meeting data
Transcripts
CRM-linked context
AI insights
Sales assistant workflows
create snippets
AI sales assistant + CRM
Strong CRM linkage
Less focus on external knowledge layer
Planhat -
Related content
Ask Jiminny Anything on Call level
Galya Dimitrova
Hubspot app
Galya Dimitrova
Product Vision & Strategy
Galya Dimitrova
Automated CRM Filling
Simona Georgieva (Deactivated)
Competitive analysis
Galya Dimitrova
AI agent ideas
Steliyan Georgiev
Comments
James Graham
9 Apr
A few areas around Security & Compliance that need more depth before this moves beyond draft:
Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate.
Team visibility enforcement should be its own HIGH requirement. The current note under access control ("ensure MCP only exposes data user has access to") is doing too much work as a single line. Every MCP tool response (especially search_calls and deal-level aggregations) must be filtered through the same visibility engine as the UI. Semantic search is the riskiest here; it could surface results across team boundaries if not properly scoped.
No data minimisation or scoping controls. We're exposing transcripts, CRM fields, contact details with job titles, deal context - significant PII and commercial data leaving the platform. Can customers configure which data types are exposed? Can transcripts be PII-stripped before they leave Jiminny? Given we position GDPR consent as a differentiator, we should be thinking about this.
GDPR downstream processing is unaddressed. Piping transcripts to OpenAI/Gemini introduces new data processors. Customers need to acknowledge responsibility for downstream processing during setup. We should also consider whether call participants' recording consent covers onward sharing to third-party AI - it almost certainly doesn't by default. This is one for me to pick up.
Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users.
Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns.
Audit logs should go deeper. Current spec (what data, which system, when) is a start. Add: identity behind the key, specific queries/filters used, and failed access attempts (especially cross-visibility-boundary attempts). Enterprise compliance teams will expect this.
Downgrade handling. Doc says Scale tier and above, but doesn't say what happens on downgrade. Keys should auto-deactivate.
Happy to help draft a dedicated Security & Compliance section i...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST [URL_WITH_CREDENTIALS] Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.
You said
Jiminny MCP Connector
Epic
Link to Epik in Jira
Document status
DRAFT
Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.
👤 Target user
Revenue teams using AI tools (Sales, CS, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
Build an AI Knowledge Connector (MCP-based) that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides structured insights (summaries, action items, scoring etc.)
Supports deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
Metric
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of MCP/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
User interaction and design
Simple setup flow:
Generate API key / connect MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus: usable by non-technical users
Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
Importance
Notes
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH
For each deal expose:
calls/meetings/emails
Identified risks
contacts related to the deal with their job title
linked account details
all CRM field information that we have in DI + fields used for CRM filling
example - get_deal_summary
request -
{
"tool": "get_deal_summary",
"arguments": {
"deal_name": "ACME"
}
}
responce:
{
"deal_name": "ACME",
"stage": "Negotiation",
"recent_activity": [
"Demo call on 2026-04-01",
"Pricing discussion on 2026-04-03"
],
"risks": [
"Pricing concerns",
"Security review pending"
], "next_steps": [
"Send follow-up to VP Sales",
"Share security documentation"
]
}
Ensure the AI can easily understand the schema of the data coming from Jiminny
HIGH
Models perform much better when tools have:
clear names
rich descriptions
predictable schemas
Provide clear, actionable error states for customers when MCP connections fail
HIGH
Error messages should guide agents toward solutions with specific suggestions and next steps.
Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”
Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”
Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”
Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”
Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”
Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”
No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”
Allow customers to securely control who and what data is accessible externally
HIGH
Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well.
Private meetings shouldn’t be exposed via MCP
HIGH
When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP.
Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows
HIGH
Ensure data is structured to be easily combined with:
CRM (accounts, deals)
external docs
Use consistent identifiers:
account IDs
deal IDs
MCP is available only for Scale tier and above
HIGH
Customers who are on Capture tier should be able to access any of their data through MCP.
This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working.
Provide visibility into how external AI tools access Jiminny data
HIGH
Audit logs:
what data was accessed by which external system when
identity - who made the request
specific queries/filters used
failed access attempts (especially cross-visibility-boundary attempts)
Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it.
example from Atlassian -
Provide customers with example prompts to get started quickly
HIGH
MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments.
See examples in table below.
Expose Jiminny as a Connector in Claude
medium
Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny
Provide customers with example workflows to get started quickly
medium
A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.
See examples in table below.
Allow customers to use predefined Jiminny skills for common workflows
medium
Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.
Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com
See examples in table below.
Jiminny UI widget for OpenAI
medium
The reason to have this is to make certain Jiminny use cases much easier than pure chat.
Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.
For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.
Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.
Allow customers to run long-running analyses asynchronously and retrieve progress/results
low
Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to:
start the job
let it run in the background
return a status like “in progress”
let the client come back for the result later
For example - User asks in Claude:
“Analyse all calls from this quarter and tell me the top 5 objections by segment.”
That might require:
searching lots of calls
pulling many summaries/transcripts
aggregating results
Instead of making Claude wait 45+ seconds, Jiminny could:
create an analysis job
return: job_id = 123, status = running
We need this in order to support:
large-scale analysis
multi-call aggregation across lots of data
playlist generation across many calls
heavy recap/report generation
Creating a snippet through the MCP
low
Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team.
example request:
{
"tool": "create_snippet",
"arguments": {
"call_id": "call_123",
"start_time": "00:18:24",
"end_time": "00:19:10",
"title": "Pricing objection"
}
}
example response:
{
"snippet_id": "snip_987",
"title": "Pricing objection",
"share_url": "https://app.jiminny.com/snippets/snip_987"
}
Ability to add recordings or snippets to a playlist
low
Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes
Allow AI tools to read reusable Jiminny resources
low
fathom
Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be:
current deal snapshot
account summary
weekly rep recap
approved talk tracks / objection handling guidance
curated playlists or snippet libraries
This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls.
Ability to write coaching notes in Jiminny through MCP
low
What we want to do for prompts/worflows and skills
Capability
Type
User value / job to be done
Example Jiminny use case
Inputs
Steps performed
Output
Draft follow-up email
Prompt
Help reps quickly create tailored follow-ups after calls
Use the latest customer call to draft an email with recap and next steps
Call ID
optional tone
optional recipient type
Retrieve call summary and action items from Jiminny.
Pull key points, commitments, and next steps.
Apply the chosen tone and structure.
Draft the email in a consistent format.
Customer-ready follow-up email draft
Summarise call
Prompt
Help users quickly extract the most important points from a conversation
Generate a concise summary of one call for internal or external use
Call ID
optional summary style
optional audience
Retrieve transcript, metadata, and structured insights.
Identify key topics, decisions, risks, and next steps.
Format output for the chosen audience.
Short or structured call summary with actions and risks
Generate deal recap
Prompt
Help reps and managers get a fast overview of a deal
Summarise the current state of a deal based on recent calls and linked CRM context
Deal ID or account name
optional date range
Retrieve calls and meetings linked to the deal.
Pull deal metadata and CRM-linked context.
Identify recent changes, blockers, risks, and next steps.
Summarise into a concise recap.
Deal recap with status, blockers, risks, and recommended next steps
Prepare weekly recap
Prompt
Help managers or reps review what changed over a time period
Weekly recap of calls, themes, and actions for one rep, team, or account
Rep ID
team ID or account ID
time period
Retrieve relevant calls and summaries for the selected time period.
Aggregate themes, action items, and changes.
Highlight notable risks, opportunities, and follow-ups.
Weekly recap with top themes, risks, opportunities, and actions
Prepare deal review
Workflow
Help reps and managers review deal status using multiple sources of context
Review a deal before pipeline review or forecast meeting
Deal ID
optional date range
Retrieve all recent calls, emails, and meetings tied to the deal.
Pull structured call summaries, score, action items, and key points.
Pull linked CRM context such as stage, contacts, and account details.
Aggregate open risks, unresolved objections, stakeholder gaps, and next steps.
Format into a review-ready structure.
Deal review document with current status, risks, stakeholders, blockers, and next steps
Weekly team recap
Workflow
Help managers monitor activity and risk across a group of reps or accounts
End-of-week team summary for sales leadership
Team ID
date range
Retrieve calls and structured insights across the team.
Group by rep, account, or deal.
Identify recurring themes, major risks, strong calls, and missed follow-ups.
Highlight calls or deals that need attention.
Team-level recap with patterns, key risks, notable wins, and action areas
Objection analysis across calls
Workflow
Help teams identify patterns across multiple conversations
Analyse pricing or competitor objections across recent calls
Account ID
deal ID
rep ID
keyword/topic
date range
Search calls matching the topic or keyword.
Retrieve summaries, transcripts, and snippets where relevant.
Identify recurring objections and how they were handled.
Assess whether objections were resolved or remain open.
Summarise patterns and examples.
Objection analysis with themes, frequency, examples, and suggested follow-up actions
Build onboarding playlist
Workflow
Help enablement teams curate useful learning content faster
Build a playlist of strong discovery, demo, or pricing calls for new hires
Topic
call type
rep/team filter
optional time period
Search for calls matching the selected criteria.
Rank or filter calls based on score, topics, or key moments.
Identify useful snippets or full recordings.
Add selected calls/snippets into a playlist structure.
Generate short descriptions for each item.
Curated onboarding playlist with selected calls/snippets and descriptions
Deal Inspection Skill
Skill
Give users a consistent, opinionated way to assess a deal
Claude reviews a deal using a repeatable Jiminny methodology
Deal ID or account name
optional review scope
Determine whether the request is suitable for a deal inspection.
Retrieve relevant calls, summaries, CRM context, stakeholders, and risks.
Apply a standard Jiminny review framework to assess deal health.
Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs....
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Objective Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows. Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product. 👤 Target user Revenue teams using AI tools (Sales, CS, RevOps) Companies already experimenting with Claude / OpenAI Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc) 🤕 Pain point or problem Jiminny data is locked in the platform AI tools lack access to high-value conversation context Customers must manually export/copy transcripts No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people Peer pressure as almost all competitors have this 💥 Impact and benefits For customers Better AI outputs using real customer conversations Ability to build automated workflows across tools For Jiminny Increased product stickiness (embedded in workflows) Competitive parity with Gong / Avoma etc. 💡 Solution ideas Build an AI Knowledge Connector (MCP-based) that: Exposes Jiminny data (calls, transcripts, metadata) to AI tools Provides structured insights (summaries, action items, scoring etc.) Supports deal-level context Allows integration into customer-owned AI stacks (Claude + Zapier, etc.) Success metrics List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets). Goal Metric Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows % of customers connecting external AI tools Number of MCP/API calls per customer - and which ones are being used the most Decrease the manual copying of transcripts decrease by X % User interaction and design Simple setup flow: Generate API key / connect MCP Clear instructions for Claude / Zapier setup Provide: Example prompts for Claude Pre-built workflow templates for Zapier Clear documentation - with use cases 👉 Focus: usable by non-technical users Detailed Requirements List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification. Requirement Importance Notes Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI) HIGH Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack Allow customers to access call data through AI tools HIGH Expose Jiminny data: list calls (filter by date, user, account, deal) get call get transcript (speaker-separated) get call metadata search calls (keyword + semantic) Support filtering by: account / contact/ lead / deal jiminny user date range Allow customers to search and query calls using AI tools HIGH Support keyword and semantic search across conversations Provide structured insights so customers can generate better outputs HIGH For each call, expose: Summary Action items Key points Activity type Notes Call Score CRM information Allow customers to understand deal status across multiple calls/emails HIGH For each deal expose: calls/meetings/emails Identified risks contacts related to the deal with their job title linked account details all CRM field information that we have in DI + fields used for CRM filling example - get_deal_summary request - { \"tool\": \"get_deal_summary\", \"arguments\": { \"deal_name\": \"ACME\" } } responce: { \"deal_name\": \"ACME\", \"stage\": \"Negotiation\", \"recent_activity\": [ \"Demo call on 2026-04-01\", \"Pricing discussion on 2026-04-03\" ], \"risks\": [ \"Pricing concerns\", \"Security review pending\" ], \"next_steps\": [ \"Send follow-up to VP Sales\", \"Share security documentation\" ] } Ensure the AI can easily understand the schema of the data coming from Jiminny HIGH Models perform much better when tools have: clear names rich descriptions predictable schemas Provide clear, actionable error states for customers when MCP connections fail HIGH Error messages should guide agents toward solutions with specific suggestions and next steps. Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.” Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.” Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.” Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.” Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.” Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.” No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.” Allow customers to securely control who and what data is accessible externally HIGH Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well. Private meetings shouldn’t be exposed via MCP HIGH When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP. Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows HIGH Ensure data is structured to be easily combined with: CRM (accounts, deals) external docs Use consistent identifiers: account IDs deal IDs MCP is available only for Scale tier and above HIGH Customers who are on Capture tier should be able to access any of their data through MCP. This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working. Provide visibility into how external AI tools access Jiminny data HIGH Audit logs: what data was accessed by which external system when identity - who made the request specific queries/filters used failed access attempts (especially cross-visibility-boundary attempts) Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it. example from Atlassian - Provide customers with example prompts to get started quickly HIGH MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments. See examples in table below. Expose Jiminny as a Connector in Claude medium Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny Provide customers with example workflows to get started quickly medium A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome. See examples in table below. Allow customers to use predefined Jiminny skills for common workflows medium Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job. Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com See examples in table below. Jiminny UI widget for OpenAI medium The reason to have this is to make certain Jiminny use cases much easier than pure chat. Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it. For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface. Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section. Allow customers to run long-running analyses asynchronously and retrieve progress/results low Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to: start the job let it run in the background return a status like “in progress” let the client come back for the result later For example - User asks in Claude: “Analyse all calls from this quarter and tell me the top 5 objections by segment.” That might require: searching lots of calls pulling many summaries/transcripts aggregating results Instead of making Claude wait 45+ seconds, Jiminny could: create an analysis job return: job_id = 123, status = running We need this in order to support: large-scale analysis multi-call aggregation across lots of data playlist generation across many calls heavy recap/report generation Creating a snippet through the MCP low Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team. example request: { \"tool\": \"create_snippet\", \"arguments\": { \"call_id\": \"call_123\", \"start_time\": \"00:18:24\", \"end_time\": \"00:19:10\", \"title\": \"Pricing objection\" } } example response: { \"snippet_id\": \"snip_987\", \"title\": \"Pricing objection\", \"share_url\": \"https://app.jiminny.com/snippets/snip_987\" } Ability to add recordings or snippets to a playlist low Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes Allow AI tools to read reusable Jiminny resources low fathom Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be: current deal snapshot account summary weekly rep recap approved talk tracks / objection handling guidance curated playlists or snippet libraries This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls. Ability to write coaching notes in Jiminny through MCP low What we want to do for prompts/worflows and skills Capability Type User value / job to be done Example Jiminny use case Inputs Steps performed Output Draft follow-up email Prompt Help reps quickly create tailored follow-ups after calls Use the latest customer call to draft an email with recap and next steps Call ID optional tone optional recipient type Retrieve call summary and action items from Jiminny. Pull key points, commitments, and next steps. Apply the chosen tone and structure. Draft the email in a consistent format. Customer-ready follow-up email draft Summarise call Prompt Help users quickly extract the most important points from a conversation Generate a concise summary of one call for internal or external use Call ID optional summary style optional audience Retrieve transcript, metadata, and structured insights. Identify key topics, decisions, risks, and next steps. Format output for the chosen audience. Short or structured call summary with actions and risks Generate deal recap Prompt Help reps and managers get a fast overview of a deal Summarise the current state of a deal based on recent calls and linked CRM context Deal ID or account name optional date range Retrieve calls and meetings linked to the deal. Pull deal metadata and CRM-linked context. Identify recent changes, blockers, risks, and next steps. Summarise into a concise recap. Deal recap with status, blockers, risks, and recommended next steps Prepare weekly recap Prompt Help managers or reps review what changed over a time period Weekly recap of calls, themes, and actions for one rep, team, or account Rep ID team ID or account ID time period Retrieve relevant calls and summaries for the selected time period. Aggregate themes, action items, and changes. Highlight notable risks, opportunities, and follow-ups. Weekly recap with top themes, risks, opportunities, and actions Prepare deal review Workflow Help reps and managers review deal status using multiple sources of context Review a deal before pipeline review or forecast meeting Deal ID optional date range Retrieve all recent calls, emails, and meetings tied to the deal. Pull structured call summaries, score, action items, and key points. Pull linked CRM context such as stage, contacts, and account details. Aggregate open risks, unresolved objections, stakeholder gaps, and next steps. Format into a review-ready structure. Deal review document with current status, risks, stakeholders, blockers, and next steps Weekly team recap Workflow Help managers monitor activity and risk across a group of reps or accounts End-of-week team summary for sales leadership Team ID date range Retrieve calls and structured insights across the team. Group by rep, account, or deal. Identify recurring themes, major risks, strong calls, and missed follow-ups. Highlight calls or deals that need attention. Team-level recap with patterns, key risks, notable wins, and action areas Objection analysis across calls Workflow Help teams identify patterns across multiple conversations Analyse pricing or competitor objections across recent calls Account ID deal ID rep ID keyword/topic date range Search calls matching the topic or keyword. Retrieve summaries, transcripts, and snippets where relevant. Identify recurring objections and how they were handled. Assess whether objections were resolved or remain open. Summarise patterns and examples. Objection analysis with themes, frequency, examples, and suggested follow-up actions Build onboarding playlist Workflow Help enablement teams curate useful learning content faster Build a playlist of strong discovery, demo, or pricing calls for new hires Topic call type rep/team filter optional time period Search for calls matching the selected criteria. Rank or filter calls based on score, topics, or key moments. Identify useful snippets or full recordings. Add selected calls/snippets into a playlist structure. Generate short descriptions for each item. Curated onboarding playlist with selected calls/snippets and descriptions Deal Inspection Skill Skill Give users a consistent, opinionated way to assess a deal Claude reviews a deal using a repeatable Jiminny methodology Deal ID or account name optional review scope Determine whether the request is suitable for a deal inspection. Retrieve relevant calls, summaries, CRM context, stakeholders, and risks. Apply a standard Jiminny review framework to assess deal health. Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs. Present findings in a consistent structure with recommended actions. Structured deal inspection with health assessment, red flags, and recommendations Manager Coaching Skill Skill Help managers review calls consistently and identify coaching moments Analyse a rep’s call and surface strengths, gaps, and examples for coaching Call ID optional rep ID optional coaching focus Retrieve transcript, score, summary, and key moments from the call. Evaluate the conversation against a coaching framework. Identify what went well and where the rep could improve. Suggest moments to clip as snippets. Recommend coaching actions or playlist additions. Coaching review with strengths, gaps, snippet suggestions, and recommended coaching actions CSM / QBR Prep Skill Skill Help CS or AM teams prepare structured customer reviews from conversations Generate an account review or QBR-style summary from recent customer interactions Account ID, optional time period Retrieve all recent calls, emails, and account-linked context. Aggregate themes, requests, blockers, sentiment shifts, and opportunities. Apply a repeatable account review structure. Summarise outcomes, open issues, and suggested next steps. Structured QBR/account review summary with themes, risks, opportunities, and actions Snippet Builder Skill Skill Help users consistently capture and label useful moments from calls Identify useful moments in a call and convert them into reusable snippets Call ID, optional focus area such as pricing, objection, competitor, discovery Retrieve transcript, recording timeline, and call insights. Detect moments matching the requested focus area. Propose snippet start/end times and titles. Tag snippets by type or theme. Optionally save them or add them to a playlist. One or more proposed or saved snippets with title, timing, and tags 💡 Technical details Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate. Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users. Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns. Open Questions Track unresolved issues or decisions that need to be made. Update as answers become available. Question Answer Date Answered Competitor research Competitor MCP Availability Core Capabilities Advanced Capabilities Positioning Key Strength Key Weakness Gong ✅ Mature MCP List/search calls Get transcripts get Call metadata Deal intelligence Scorecards, analytics CRM context Enterprise data layer Deep data + ecosystem integrations Limited opinionated AI workflows Avoma ✅ Mature MCP list_meetings get_meeting get_transcript key topics Deal fields/Deal level analysis Coaching/Scoring CRM context pushing insights into Notion / Confluence AI-ready meeting intelligence Strongest “outputs” layer Mostly pull-based interactions Fathom ✅ MCP list/search meetings get transcript get summary/action items Team/org context Webhooks (event-based triggers) get_weekly_recap aggregated action items across meetings creating webhooks directly from the MCP server Lightweight meeting data access Includes summaries + actions Limited analytics depth Grain ✅ Basic MCP get meetings download transcripts Notes Deals Scorecards Data access utility Simple + accessible Low sophistication, sometimes browser-based Outreach ⚠️ MCP (workflow-focused) Search sequences Emails Meetings (Kaia) account / prospect / opportunity lookups Workflow automation Multi-system orchestration Revenue workflow engine End-to-end GTM workflows Less depth in conversation data Attention ⚠️ Emerging MCP Meeting data Transcripts CRM-linked context AI insights Sales assistant workflows create snippets AI sales assistant + CRM Strong CRM linkage Less focus on external knowledge layer Planhat - Related content Ask Jiminny Anything on Call level Galya Dimitrova Hubspot app Galya Dimitrova Product Vision & Strategy Galya Dimitrova Automated CRM Filling Simona Georgieva (Deactivated) Competitive analysis Galya Dimitrova AI agent ideas Steliyan Georgiev Comments James Graham 9 Apr A few areas around Security & Compliance that need more depth before this moves beyond draft: Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate. Team visibility enforcement should be its own HIGH requirement. The current note under access control (\"ensure MCP only exposes data user has access to\") is doing too much work as a single line. Every MCP tool response (especially search_calls and deal-level aggregations) must be filtered through the same visibility engine as the UI. Semantic search is the riskiest here; it could surface results across team boundaries if not properly scoped. No data minimisation or scoping controls. We're exposing transcripts, CRM fields, contact details with job titles, deal context - significant PII and commercial data leaving the platform. Can customers configure which data types are exposed? Can transcripts be PII-stripped before they leave Jiminny? Given we position GDPR consent as a differentiator, we should be thinking about this. GDPR downstream processing is unaddressed. Piping transcripts to OpenAI/Gemini introduces new data processors. Customers need to acknowledge responsibility for downstream processing during setup. We should also consider whether call participants' recording consent covers onward sharing to third-party AI - it almost certainly doesn't by default. This is one for me to pick up. Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users. Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns. Audit logs should go deeper. Current spec (what data, which system, when) is a start. Add: identity behind the key, specific queries/filters used, and failed access attempts (especially cross-visibility-boundary attempts). Enterprise compliance teams will expect this. Downgrade handling. Doc says Scale tier and above, but doesn't say what happens on downgrade. Keys should auto-deactivate. Happy to help draft a dedicated Security & Compliance section if useful. Galya Dimitrova 15 Apr Thanks for the input @James Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.","depth":21,"bounds":{"left":0.41805187,"top":0.19313647,"width":0.051861703,"height":0.11173184},"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"You said","depth":23,"bounds":{"left":0.36053857,"top":0.19553073,"width":0.019946808,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny MCP Connector","depth":23,"bounds":{"left":0.41805187,"top":0.1963288,"width":0.03125,"height":0.038707104},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Epic","depth":23,"bounds":{"left":0.41805187,"top":0.30806065,"width":0.010305851,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Link to Epik in Jira","depth":23,"bounds":{"left":0.41805187,"top":0.33040702,"width":0.043218084,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Document status","depth":23,"bounds":{"left":0.41805187,"top":0.3527534,"width":0.04105718,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"DRAFT","depth":23,"bounds":{"left":0.41805187,"top":0.37509975,"width":0.016289894,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Objective","depth":23,"bounds":{"left":0.41805187,"top":0.39744613,"width":0.024268618,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.","depth":23,"bounds":{"left":0.41805187,"top":0.4197925,"width":0.051529255,"height":0.17278531},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.","depth":23,"bounds":{"left":0.41805187,"top":0.59856343,"width":0.049700797,"height":0.10574621},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"👤 Target user","depth":23,"bounds":{"left":0.41805187,"top":0.75458896,"width":0.033909574,"height":0.016759777},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Revenue teams using AI tools (Sales, CS, RevOps)","depth":23,"bounds":{"left":0.41805187,"top":0.7773344,"width":0.051030584,"height":0.061053474},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Companies already experimenting with Claude / OpenAI","depth":23,"bounds":{"left":0.41805187,"top":0.8443735,"width":0.04637633,"height":0.061053474},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)","depth":23,"bounds":{"left":0.41805187,"top":0.9114126,"width":0.051363032,"height":0.0885874},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"🤕 Pain point or problem","depth":23,"bounds":{"left":0.41805187,"top":1.0,"width":0.03706782,"height":-0.0897845},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny data is locked in the platform","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"AI tools lack access to high-value conversation context","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Customers must manually export/copy transcripts","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"No easy way to combine calls with CRM + docs + other info - 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The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to securely control who and what data is accessible externally","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Private meetings shouldn’t be exposed via MCP","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ensure data is structured to be easily combined with:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"CRM (accounts, deals)","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"external docs","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Use consistent identifiers:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"account IDs","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"deal IDs","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"MCP is available only for Scale tier and above","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Customers who are on Capture tier should be able to access any of their data through MCP.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide visibility into how external AI tools access Jiminny data","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Audit logs:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"what data was accessed by which external system when","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"identity - who made the request","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"specific queries/filters used","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"failed access attempts (especially cross-visibility-boundary attempts)","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"example from Atlassian -","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide customers with example prompts to get started quickly","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"See examples in table below.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Expose Jiminny as a Connector in Claude","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide customers with example workflows to get started quickly","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"See examples in table below.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to use predefined Jiminny skills for common workflows","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"See examples in table below.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny UI widget for OpenAI","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The reason to have this is to make certain Jiminny use cases much easier than pure chat.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to run long-running analyses asynchronously and retrieve progress/results","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"start the job","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"let it run in the background","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"return a status like “in progress”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"let the client come back for the result later","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"For example - User asks in Claude:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"“Analyse all calls from this quarter and tell me the top 5 objections by segment.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"That might require:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"searching lots of calls","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"pulling many summaries/transcripts","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"aggregating results","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Instead of making Claude wait 45+ seconds, Jiminny could:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"create an analysis job","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"return: job_id = 123, status = running","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"We need this in order to support:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"large-scale analysis","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"multi-call aggregation across lots of data","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"playlist generation across many calls","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"heavy recap/report generation","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Creating a snippet through the MCP","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"example request:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"{","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"tool\": \"create_snippet\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"arguments\": {","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"call_id\": \"call_123\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"start_time\": \"00:18:24\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"end_time\": \"00:19:10\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"title\": \"Pricing objection\"","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"example response:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"{","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"snippet_id\": \"snip_987\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"title\": \"Pricing objection\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"share_url\": \"https://app.jiminny.com/snippets/snip_987\"","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ability to add recordings or snippets to a playlist","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to create libraries of calls automatically. 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calls.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ability to write coaching notes in Jiminny through MCP","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"What we want to do for prompts/worflows and skills","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Capability","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Type","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"User value / job to be done","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Example Jiminny use 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ID","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"optional date range","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Retrieve all recent calls, emails, and meetings tied to the deal.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Pull structured call summaries, score, action items, and key points.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Pull linked CRM context such as stage, contacts, and account details.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Aggregate open risks, unresolved objections, stakeholder gaps, and next steps.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Format into a review-ready structure.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Deal review document with current status, risks, stakeholders, blockers, and next steps","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Weekly team recap","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Workflow","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Help managers monitor activity and risk across a group of reps or accounts","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"End-of-week team summary for sales leadership","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Team ID","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"date range","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Retrieve calls and structured insights across the team.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Group by rep, account, or deal.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Identify recurring themes, major risks, strong calls, and missed follow-ups.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Highlight calls or deals that need attention.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Team-level recap with patterns, key risks, notable wins, and action areas","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Objection analysis across calls","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Workflow","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Help teams identify patterns across multiple conversations","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Analyse pricing or competitor objections across recent calls","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Account ID","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"deal ID","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"rep ID","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"keyword/topic","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"date range","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Search calls matching the topic or keyword.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Retrieve summaries, transcripts, and snippets where relevant.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Identify recurring objections and how they were handled.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Assess whether objections were resolved or remain open.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Summarise patterns and examples.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Objection analysis with themes, frequency, examples, and suggested follow-up actions","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Build onboarding playlist","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Workflow","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Help enablement teams curate useful learning content faster","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Build a playlist of strong discovery, demo, or pricing calls for new hires","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Topic","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"call type","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"rep/team filter","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"optional time period","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Search for calls matching the selected criteria.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Rank or filter calls based on score, topics, or key moments.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Identify useful snippets or full recordings.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Add selected calls/snippets into a playlist structure.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Generate short descriptions for each item.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Curated onboarding playlist with selected calls/snippets and descriptions","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Deal Inspection Skill","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Skill","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Give users a consistent, opinionated way to assess a deal","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude reviews a deal using a repeatable Jiminny methodology","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Deal ID or account name","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"optional review scope","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Determine whether the request is suitable for a deal inspection.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Retrieve relevant calls, summaries, CRM context, stakeholders, and risks.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Apply a standard Jiminny review framework to assess deal health.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"}]...
|
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idle
|
accessibility
|
NULL
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST [URL_WITH_CREDENTIALS] Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.
You said
Jiminny MCP Connector
Epic
Link to Epik in Jira
Document status
DRAFT
Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.
👤 Target user
Revenue teams using AI tools (Sales, CS, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
Build an AI Knowledge Connector (MCP-based) that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides structured insights (summaries, action items, scoring etc.)
Supports deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
Metric
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of MCP/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
User interaction and design
Simple setup flow:
Generate API key / connect MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus: usable by non-technical users
Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
Importance
Notes
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH
For each deal expose:
calls/meetings/emails
Identified risks
contacts related to the deal with their job title
linked account details
all CRM field information that we have in DI + fields used for CRM filling
example - get_deal_summary
request -
{
"tool": "get_deal_summary",
"arguments": {
"deal_name": "ACME"
}
}
responce:
{
"deal_name": "ACME",
"stage": "Negotiation",
"recent_activity": [
"Demo call on 2026-04-01",
"Pricing discussion on 2026-04-03"
],
"risks": [
"Pricing concerns",
"Security review pending"
], "next_steps": [
"Send follow-up to VP Sales",
"Share security documentation"
]
}
Ensure the AI can easily understand the schema of the data coming from Jiminny
HIGH
Models perform much better when tools have:
clear names
rich descriptions
predictable schemas
Provide clear, actionable error states for customers when MCP connections fail
HIGH
Error messages should guide agents toward solutions with specific suggestions and next steps.
Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”
Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”
Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”
Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”
Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”
Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”
No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”
Allow customers to securely control who and what data is accessible externally
HIGH
Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well.
Private meetings shouldn’t be exposed via MCP
HIGH
When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP.
Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows
HIGH
Ensure data is structured to be easily combined with:
CRM (accounts, deals)
external docs
Use consistent identifiers:
account IDs
deal IDs
MCP is available only for Scale tier and above
HIGH
Customers who are on Capture tier should be able to access any of their data through MCP.
This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working.
Provide visibility into how external AI tools access Jiminny data
HIGH
Audit logs:
what data was accessed by which external system when
identity - who made the request
specific queries/filters used
failed access attempts (especially cross-visibility-boundary attempts)
Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it.
example from Atlassian -
Provide customers with example prompts to get started quickly
HIGH
MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments.
See examples in table below.
Expose Jiminny as a Connector in Claude
medium
Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny
Provide customers with example workflows to get started quickly
medium
A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.
See examples in table below.
Allow customers to use predefined Jiminny skills for common workflows
medium
Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.
Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com
See examples in table below.
Jiminny UI widget for OpenAI
medium
The reason to have this is to make certain Jiminny use cases much easier than pure chat.
Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.
For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.
Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.
Allow customers to run long-running analyses asynchronously and retrieve progress/results
low
Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to:
start the job
let it run in the background
return a status like “in progress”
let the client come back for the result later
For example - User asks in Claude:
“Analyse all calls from this quarter and tell me the top 5 objections by segment.”
That might require:
searching lots of calls
pulling many summaries/transcripts
aggregating results
Instead of making Claude wait 45+ seconds, Jiminny could:
create an analysis job
return: job_id = 123, status = running
We need this in order to support:
large-scale analysis
multi-call aggregation across lots of data
playlist generation across many calls
heavy recap/report generation
Creating a snippet through the MCP
low
Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team.
example request:
{
"tool": "create_snippet",
"arguments": {
"call_id": "call_123",
"start_time": "00:18:24",
"end_time": "00:19:10",
"title": "Pricing objection"
}
}
example response:
{
"snippet_id": "snip_987",
"title": "Pricing objection",
"share_url": "https://app.jiminny.com/snippets/snip_987"
}
Ability to add recordings or snippets to a playlist
low
Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes
Allow AI tools to read reusable Jiminny resources
low
fathom
Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be:
current deal snapshot
account summary
weekly rep recap
approved talk tracks / objection handling guidance
curated playlists or snippet libraries
This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls.
Ability to write coaching notes in Jiminny through MCP
low
What we want to do for prompts/worflows and skills
Capability
Type
User value / job to be done
Example Jiminny use case
Inputs
Steps performed
Output
Draft follow-up email
Prompt
Help reps quickly create tailored follow-ups after calls
Use the latest customer call to draft an email with recap and next steps
Call ID
optional tone
optional recipient type
Retrieve call summary and action items from Jiminny.
Pull key points, commitments, and next steps.
Apply the chosen tone and structure.
Draft the email in a consistent format.
Customer-ready follow-up email draft
Summarise call
Prompt
Help users quickly extract the most important points from a conversation
Generate a concise summary of one call for internal or external use
Call ID
optional summary style
optional audience
Retrieve transcript, metadata, and structured insights.
Identify key topics, decisions, risks, and next steps.
Format output for the chosen audience.
Short or structured call summary with actions and risks
Generate deal recap
Prompt
Help reps and managers get a fast overview of a deal
Summarise the current state of a deal based on recent calls and linked CRM context
Deal ID or account name
optional date range
Retrieve calls and meetings linked to the deal.
Pull deal metadata and CRM-linked context.
Identify recent changes, blockers, risks, and next steps.
Summarise into a concise recap.
Deal recap with status, blockers, risks, and recommended next steps
Prepare weekly recap
Prompt
Help managers or reps review what changed over a time period
Weekly recap of calls, themes, and actions for one rep, team, or account
Rep ID
team ID or account ID
time period
Retrieve relevant calls and summaries for the selected time period.
Aggregate themes, action items, and changes.
Highlight notable risks, opportunities, and follow-ups.
Weekly recap with top themes, risks, opportunities, and actions
Prepare deal review
Workflow
Help reps and managers review deal status using multiple sources of context
Review a deal before pipeline review or forecast meeting
Deal ID
optional date range
Retrieve all recent calls, emails, and meetings tied to the deal.
Pull structured call summaries, score, action items, and key points.
Pull linked CRM context such as stage, contacts, and account details.
Aggregate open risks, unresolved objections, stakeholder gaps, and next steps.
Format into a review-ready structure.
Deal review document with current status, risks, stakeholders, blockers, and next steps
Weekly team recap
Workflow
Help managers monitor activity and risk across a group of reps or accounts
End-of-week team summary for sales leadership
Team ID
date range
Retrieve calls and structured insights across the team.
Group by rep, account, or deal.
Identify recurring themes, major risks, strong calls, and missed follow-ups.
Highlight calls or deals that need attention.
Team-level recap with patterns, key risks, notable wins, and action areas
Objection analysis across calls
Workflow
Help teams identify patterns across multiple conversations
Analyse pricing or competitor objections across recent calls
Account ID
deal ID
rep ID
keyword/topic
date range
Search calls matching the topic or keyword.
Retrieve summaries, transcripts, and snippets where relevant.
Identify recurring objections and how they were handled.
Assess whether objections were resolved or remain open.
Summarise patterns and examples.
Objection analysis with themes, frequency, examples, and suggested follow-up actions
Build onboarding playlist
Workflow
Help enablement teams curate useful learning content faster
Build a playlist of strong discovery, demo, or pricing calls for new hires
Topic
call type
rep/team filter
optional time period
Search for calls matching the selected criteria.
Rank or filter calls based on score, topics, or key moments.
Identify useful snippets or full recordings.
Add selected calls/snippets into a playlist structure.
Generate short descriptions for each item.
Curated onboarding playlist with selected calls/snippets and descriptions
Deal Inspection Skill
Skill
Give users a consistent, opinionated way to assess a deal
Claude reviews a deal using a repeatable Jiminny methodology
Deal ID or account name
optional review scope
Determine whether the request is suitable for a deal inspection.
Retrieve relevant calls, summaries, CRM context, stakeholders, and risks.
Apply a standard Jiminny review framework to assess deal health.
Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs....
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2026-04-22T14:38:22.811943+00:00
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/Users/lukas/.screenpipe/data/data/2026-04-22/1776 /Users/lukas/.screenpipe/data/data/2026-04-22/1776868702811_m1.jpg...
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Jiminny MCP Connector - Product - Confluence — Wor Jiminny MCP Connector - Product - Confluence — Work...
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jiminny.atlassian.net/wiki/spaces/PROD/pages/37282 jiminny.atlassian.net/wiki/spaces/PROD/pages/3728244737/Jiminny+MCP+Connector...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST [URL_WITH_CREDENTIALS] Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.
You said
Jiminny MCP Connector
Epic
Link to Epik in Jira
Document status
DRAFT
Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.
👤 Target user
Revenue teams using AI tools (Sales, CS, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
Build an AI Knowledge Connector (MCP-based) that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides structured insights (summaries, action items, scoring etc.)
Supports deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
Metric
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of MCP/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
User interaction and design
Simple setup flow:
Generate API key / connect MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus: usable by non-technical users
Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
Importance
Notes
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH
For each deal expose:
calls/meetings/emails
Identified risks
contacts related to the deal with their job title
linked account details
all CRM field information that we have in DI + fields used for CRM filling
example - get_deal_summary
request -
{
"tool": "get_deal_summary",
"arguments": {
"deal_name": "ACME"
}
}
responce:
{
"deal_name": "ACME",
"stage": "Negotiation",
"recent_activity": [
"Demo call on 2026-04-01",
"Pricing discussion on 2026-04-03"
],
"risks": [
"Pricing concerns",
"Security review pending"
], "next_steps": [
"Send follow-up to VP Sales",
"Share security documentation"
]
}
Ensure the AI can easily understand the schema of the data coming from Jiminny
HIGH
Models perform much better when tools have:
clear names
rich descriptions
predictable schemas
Provide clear, actionable error states for customers when MCP connections fail
HIGH
Error messages should guide agents toward solutions with specific suggestions and next steps.
Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”
Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”
Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”
Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”
Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”
Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”
No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”
Allow customers to securely control who and what data is accessible externally
HIGH
Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well.
Private meetings shouldn’t be exposed via MCP
HIGH
When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP.
Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows
HIGH
Ensure data is structured to be easily combined with:
CRM (accounts, deals)
external docs
Use consistent identifiers:
account IDs
deal IDs
MCP is available only for Scale tier and above
HIGH
Customers who are on Capture tier should be able to access any of their data through MCP.
This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working.
Provide visibility into how external AI tools access Jiminny data
HIGH
Audit logs:
what data was accessed by which external system when
identity - who made the request
specific queries/filters used
failed access attempts (especially cross-visibility-boundary attempts)
Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it.
example from Atlassian -
Provide customers with example prompts to get started quickly
HIGH
MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments.
See examples in table below.
Expose Jiminny as a Connector in Claude
medium
Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny
Provide customers with example workflows to get started quickly
medium
A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.
See examples in table below.
Allow customers to use predefined Jiminny skills for common workflows
medium
Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.
Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com
See examples in table below.
Jiminny UI widget for OpenAI
medium
The reason to have this is to make certain Jiminny use cases much easier than pure chat.
Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.
For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.
Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.
Allow customers to run long-running analyses asynchronously and retrieve progress/results
low
Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to:
start the job
let it run in the background
return a status like “in progress”
let the client come back for the result later
For example - User asks in Claude:
“Analyse all calls from this quarter and tell me the top 5 objections by segment.”
That might require:
searching lots of calls
pulling many summaries/transcripts
aggregating results
Instead of making Claude wait 45+ seconds, Jiminny could:
create an analysis job
return: job_id = 123, status = running
We need this in order to support:
large-scale analysis
multi-call aggregation across lots of data
playlist generation across many calls
heavy recap/report generation
Creating a snippet through the MCP
low
Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team.
example request:
{
"tool": "create_snippet",
"arguments": {
"call_id": "call_123",
"start_time": "00:18:24",
"end_time": "00:19:10",
"title": "Pricing objection"
}
}
example response:
{
"snippet_id": "snip_987",
"title": "Pricing objection",
"share_url": "https://app.jiminny.com/snippets/snip_987"
}
Ability to add recordings or snippets to a playlist
low
Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes
Allow AI tools to read reusable Jiminny resources
low
fathom
Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be:
current deal snapshot
account summary
weekly rep recap
approved talk tracks / objection handling guidance
curated playlists or snippet libraries
This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls.
Ability to write coaching notes in Jiminny through MCP
low
What we want to do for prompts/worflows and skills
Capability
Type
User value / job to be done
Example Jiminny use case
Inputs
Steps performed
Output
Draft follow-up email
Prompt
Help reps quickly create tailored follow-ups after calls
Use the latest customer call to draft an email with recap and next steps
Call ID
optional tone
optional recipient type
Retrieve call summary and action items from Jiminny.
Pull key points, commitments, and next steps.
Apply the chosen tone and structure.
Draft the email in a consistent format.
Customer-ready follow-up email draft
Summarise call
Prompt
Help users quickly extract the most important points from a conversation
Generate a concise summary of one call for internal or external use
Call ID
optional summary style
optional audience
Retrieve transcript, metadata, and structured insights.
Identify key topics, decisions, risks, and next steps.
Format output for the chosen audience.
Short or structured call summary with actions and risks
Generate deal recap
Prompt
Help reps and managers get a fast overview of a deal
Summarise the current state of a deal based on recent calls and linked CRM context
Deal ID or account name
optional date range
Retrieve calls and meetings linked to the deal.
Pull deal metadata and CRM-linked context.
Identify recent changes, blockers, risks, and next steps.
Summarise into a concise recap.
Deal recap with status, blockers, risks, and recommended next steps
Prepare weekly recap
Prompt
Help managers or reps review what changed over a time period
Weekly recap of calls, themes, and actions for one rep, team, or account
Rep ID
team ID or account ID
time period
Retrieve relevant calls and summaries for the selected time period.
Aggregate themes, action items, and changes.
Highlight notable risks, opportunities, and follow-ups.
Weekly recap with top themes, risks, opportunities, and actions
Prepare deal review
Workflow
Help reps and managers review deal status using multiple sources of context
Review a deal before pipeline review or forecast meeting
Deal ID
optional date range
Retrieve all recent calls, emails, and meetings tied to the deal.
Pull structured call summaries, score, action items, and key points.
Pull linked CRM context such as stage, contacts, and account details.
Aggregate open risks, unresolved objections, stakeholder gaps, and next steps.
Format into a review-ready structure.
Deal review document with current status, risks, stakeholders, blockers, and next steps
Weekly team recap...
|
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Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product. 👤 Target user Revenue teams using AI tools (Sales, CS, RevOps) Companies already experimenting with Claude / OpenAI Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc) 🤕 Pain point or problem Jiminny data is locked in the platform AI tools lack access to high-value conversation context Customers must manually export/copy transcripts No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people Peer pressure as almost all competitors have this 💥 Impact and benefits For customers Better AI outputs using real customer conversations Ability to build automated workflows across tools For Jiminny Increased product stickiness (embedded in workflows) Competitive parity with Gong / Avoma etc. 💡 Solution ideas Build an AI Knowledge Connector (MCP-based) that: Exposes Jiminny data (calls, transcripts, metadata) to AI tools Provides structured insights (summaries, action items, scoring etc.) Supports deal-level context Allows integration into customer-owned AI stacks (Claude + Zapier, etc.) Success metrics List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets). Goal Metric Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows % of customers connecting external AI tools Number of MCP/API calls per customer - and which ones are being used the most Decrease the manual copying of transcripts decrease by X % User interaction and design Simple setup flow: Generate API key / connect MCP Clear instructions for Claude / Zapier setup Provide: Example prompts for Claude Pre-built workflow templates for Zapier Clear documentation - with use cases 👉 Focus: usable by non-technical users Detailed Requirements List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification. Requirement Importance Notes Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI) HIGH Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack Allow customers to access call data through AI tools HIGH Expose Jiminny data: list calls (filter by date, user, account, deal) get call get transcript (speaker-separated) get call metadata search calls (keyword + semantic) Support filtering by: account / contact/ lead / deal jiminny user date range Allow customers to search and query calls using AI tools HIGH Support keyword and semantic search across conversations Provide structured insights so customers can generate better outputs HIGH For each call, expose: Summary Action items Key points Activity type Notes Call Score CRM information Allow customers to understand deal status across multiple calls/emails HIGH For each deal expose: calls/meetings/emails Identified risks contacts related to the deal with their job title linked account details all CRM field information that we have in DI + fields used for CRM filling example - get_deal_summary request - { \"tool\": \"get_deal_summary\", \"arguments\": { \"deal_name\": \"ACME\" } } responce: { \"deal_name\": \"ACME\", \"stage\": \"Negotiation\", \"recent_activity\": [ \"Demo call on 2026-04-01\", \"Pricing discussion on 2026-04-03\" ], \"risks\": [ \"Pricing concerns\", \"Security review pending\" ], \"next_steps\": [ \"Send follow-up to VP Sales\", \"Share security documentation\" ] } Ensure the AI can easily understand the schema of the data coming from Jiminny HIGH Models perform much better when tools have: clear names rich descriptions predictable schemas Provide clear, actionable error states for customers when MCP connections fail HIGH Error messages should guide agents toward solutions with specific suggestions and next steps. Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.” Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.” Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.” Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.” Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.” Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.” No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.” Allow customers to securely control who and what data is accessible externally HIGH Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well. Private meetings shouldn’t be exposed via MCP HIGH When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP. Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows HIGH Ensure data is structured to be easily combined with: CRM (accounts, deals) external docs Use consistent identifiers: account IDs deal IDs MCP is available only for Scale tier and above HIGH Customers who are on Capture tier should be able to access any of their data through MCP. This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working. Provide visibility into how external AI tools access Jiminny data HIGH Audit logs: what data was accessed by which external system when identity - who made the request specific queries/filters used failed access attempts (especially cross-visibility-boundary attempts) Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it. example from Atlassian - Provide customers with example prompts to get started quickly HIGH MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments. See examples in table below. Expose Jiminny as a Connector in Claude medium Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny Provide customers with example workflows to get started quickly medium A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome. See examples in table below. Allow customers to use predefined Jiminny skills for common workflows medium Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job. Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com See examples in table below. Jiminny UI widget for OpenAI medium The reason to have this is to make certain Jiminny use cases much easier than pure chat. Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it. For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface. Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section. Allow customers to run long-running analyses asynchronously and retrieve progress/results low Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to: start the job let it run in the background return a status like “in progress” let the client come back for the result later For example - User asks in Claude: “Analyse all calls from this quarter and tell me the top 5 objections by segment.” That might require: searching lots of calls pulling many summaries/transcripts aggregating results Instead of making Claude wait 45+ seconds, Jiminny could: create an analysis job return: job_id = 123, status = running We need this in order to support: large-scale analysis multi-call aggregation across lots of data playlist generation across many calls heavy recap/report generation Creating a snippet through the MCP low Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team. example request: { \"tool\": \"create_snippet\", \"arguments\": { \"call_id\": \"call_123\", \"start_time\": \"00:18:24\", \"end_time\": \"00:19:10\", \"title\": \"Pricing objection\" } } example response: { \"snippet_id\": \"snip_987\", \"title\": \"Pricing objection\", \"share_url\": \"https://app.jiminny.com/snippets/snip_987\" } Ability to add recordings or snippets to a playlist low Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes Allow AI tools to read reusable Jiminny resources low fathom Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be: current deal snapshot account summary weekly rep recap approved talk tracks / objection handling guidance curated playlists or snippet libraries This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls. Ability to write coaching notes in Jiminny through MCP low What we want to do for prompts/worflows and skills Capability Type User value / job to be done Example Jiminny use case Inputs Steps performed Output Draft follow-up email Prompt Help reps quickly create tailored follow-ups after calls Use the latest customer call to draft an email with recap and next steps Call ID optional tone optional recipient type Retrieve call summary and action items from Jiminny. Pull key points, commitments, and next steps. Apply the chosen tone and structure. Draft the email in a consistent format. Customer-ready follow-up email draft Summarise call Prompt Help users quickly extract the most important points from a conversation Generate a concise summary of one call for internal or external use Call ID optional summary style optional audience Retrieve transcript, metadata, and structured insights. Identify key topics, decisions, risks, and next steps. Format output for the chosen audience. Short or structured call summary with actions and risks Generate deal recap Prompt Help reps and managers get a fast overview of a deal Summarise the current state of a deal based on recent calls and linked CRM context Deal ID or account name optional date range Retrieve calls and meetings linked to the deal. Pull deal metadata and CRM-linked context. Identify recent changes, blockers, risks, and next steps. Summarise into a concise recap. Deal recap with status, blockers, risks, and recommended next steps Prepare weekly recap Prompt Help managers or reps review what changed over a time period Weekly recap of calls, themes, and actions for one rep, team, or account Rep ID team ID or account ID time period Retrieve relevant calls and summaries for the selected time period. Aggregate themes, action items, and changes. Highlight notable risks, opportunities, and follow-ups. Weekly recap with top themes, risks, opportunities, and actions Prepare deal review Workflow Help reps and managers review deal status using multiple sources of context Review a deal before pipeline review or forecast meeting Deal ID optional date range Retrieve all recent calls, emails, and meetings tied to the deal. Pull structured call summaries, score, action items, and key points. Pull linked CRM context such as stage, contacts, and account details. Aggregate open risks, unresolved objections, stakeholder gaps, and next steps. Format into a review-ready structure. Deal review document with current status, risks, stakeholders, blockers, and next steps Weekly team recap Workflow Help managers monitor activity and risk across a group of reps or accounts End-of-week team summary for sales leadership Team ID date range Retrieve calls and structured insights across the team. Group by rep, account, or deal. Identify recurring themes, major risks, strong calls, and missed follow-ups. Highlight calls or deals that need attention. Team-level recap with patterns, key risks, notable wins, and action areas Objection analysis across calls Workflow Help teams identify patterns across multiple conversations Analyse pricing or competitor objections across recent calls Account ID deal ID rep ID keyword/topic date range Search calls matching the topic or keyword. Retrieve summaries, transcripts, and snippets where relevant. Identify recurring objections and how they were handled. Assess whether objections were resolved or remain open. Summarise patterns and examples. Objection analysis with themes, frequency, examples, and suggested follow-up actions Build onboarding playlist Workflow Help enablement teams curate useful learning content faster Build a playlist of strong discovery, demo, or pricing calls for new hires Topic call type rep/team filter optional time period Search for calls matching the selected criteria. Rank or filter calls based on score, topics, or key moments. Identify useful snippets or full recordings. Add selected calls/snippets into a playlist structure. Generate short descriptions for each item. Curated onboarding playlist with selected calls/snippets and descriptions Deal Inspection Skill Skill Give users a consistent, opinionated way to assess a deal Claude reviews a deal using a repeatable Jiminny methodology Deal ID or account name optional review scope Determine whether the request is suitable for a deal inspection. Retrieve relevant calls, summaries, CRM context, stakeholders, and risks. Apply a standard Jiminny review framework to assess deal health. Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs. Present findings in a consistent structure with recommended actions. Structured deal inspection with health assessment, red flags, and recommendations Manager Coaching Skill Skill Help managers review calls consistently and identify coaching moments Analyse a rep’s call and surface strengths, gaps, and examples for coaching Call ID optional rep ID optional coaching focus Retrieve transcript, score, summary, and key moments from the call. Evaluate the conversation against a coaching framework. Identify what went well and where the rep could improve. Suggest moments to clip as snippets. Recommend coaching actions or playlist additions. Coaching review with strengths, gaps, snippet suggestions, and recommended coaching actions CSM / QBR Prep Skill Skill Help CS or AM teams prepare structured customer reviews from conversations Generate an account review or QBR-style summary from recent customer interactions Account ID, optional time period Retrieve all recent calls, emails, and account-linked context. Aggregate themes, requests, blockers, sentiment shifts, and opportunities. Apply a repeatable account review structure. Summarise outcomes, open issues, and suggested next steps. Structured QBR/account review summary with themes, risks, opportunities, and actions Snippet Builder Skill Skill Help users consistently capture and label useful moments from calls Identify useful moments in a call and convert them into reusable snippets Call ID, optional focus area such as pricing, objection, competitor, discovery Retrieve transcript, recording timeline, and call insights. Detect moments matching the requested focus area. Propose snippet start/end times and titles. Tag snippets by type or theme. Optionally save them or add them to a playlist. One or more proposed or saved snippets with title, timing, and tags 💡 Technical details Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate. Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users. Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns. Open Questions Track unresolved issues or decisions that need to be made. Update as answers become available. Question Answer Date Answered Competitor research Competitor MCP Availability Core Capabilities Advanced Capabilities Positioning Key Strength Key Weakness Gong ✅ Mature MCP List/search calls Get transcripts get Call metadata Deal intelligence Scorecards, analytics CRM context Enterprise data layer Deep data + ecosystem integrations Limited opinionated AI workflows Avoma ✅ Mature MCP list_meetings get_meeting get_transcript key topics Deal fields/Deal level analysis Coaching/Scoring CRM context pushing insights into Notion / Confluence AI-ready meeting intelligence Strongest “outputs” layer Mostly pull-based interactions Fathom ✅ MCP list/search meetings get transcript get summary/action items Team/org context Webhooks (event-based triggers) get_weekly_recap aggregated action items across meetings creating webhooks directly from the MCP server Lightweight meeting data access Includes summaries + actions Limited analytics depth Grain ✅ Basic MCP get meetings download transcripts Notes Deals Scorecards Data access utility Simple + accessible Low sophistication, sometimes browser-based Outreach ⚠️ MCP (workflow-focused) Search sequences Emails Meetings (Kaia) account / prospect / opportunity lookups Workflow automation Multi-system orchestration Revenue workflow engine End-to-end GTM workflows Less depth in conversation data Attention ⚠️ Emerging MCP Meeting data Transcripts CRM-linked context AI insights Sales assistant workflows create snippets AI sales assistant + CRM Strong CRM linkage Less focus on external knowledge layer Planhat - Related content Ask Jiminny Anything on Call level Galya Dimitrova Hubspot app Galya Dimitrova Product Vision & Strategy Galya Dimitrova Automated CRM Filling Simona Georgieva (Deactivated) Competitive analysis Galya Dimitrova AI agent ideas Steliyan Georgiev Comments James Graham 9 Apr A few areas around Security & Compliance that need more depth before this moves beyond draft: Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate. Team visibility enforcement should be its own HIGH requirement. The current note under access control (\"ensure MCP only exposes data user has access to\") is doing too much work as a single line. Every MCP tool response (especially search_calls and deal-level aggregations) must be filtered through the same visibility engine as the UI. Semantic search is the riskiest here; it could surface results across team boundaries if not properly scoped. No data minimisation or scoping controls. We're exposing transcripts, CRM fields, contact details with job titles, deal context - significant PII and commercial data leaving the platform. Can customers configure which data types are exposed? Can transcripts be PII-stripped before they leave Jiminny? Given we position GDPR consent as a differentiator, we should be thinking about this. GDPR downstream processing is unaddressed. Piping transcripts to OpenAI/Gemini introduces new data processors. Customers need to acknowledge responsibility for downstream processing during setup. We should also consider whether call participants' recording consent covers onward sharing to third-party AI - it almost certainly doesn't by default. This is one for me to pick up. Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users. Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns. Audit logs should go deeper. Current spec (what data, which system, when) is a start. Add: identity behind the key, specific queries/filters used, and failed access attempts (especially cross-visibility-boundary attempts). Enterprise compliance teams will expect this. Downgrade handling. Doc says Scale tier and above, but doesn't say what happens on downgrade. Keys should auto-deactivate. Happy to help draft a dedicated Security & Compliance section if useful. Galya Dimitrova 15 Apr Thanks for the input @James Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.","depth":21,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"You said","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny MCP Connector","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Epic","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Link to Epik in Jira","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Document status","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"DRAFT","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Objective","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"👤 Target user","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Revenue teams using AI tools (Sales, CS, RevOps)","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Companies already experimenting with Claude / OpenAI","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"🤕 Pain point or problem","depth":23,"bounds":{"left":0.30868056,"top":0.125,"width":0.077430554,"height":0.054444443},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny data is locked in the platform","depth":23,"bounds":{"left":0.30868056,"top":0.18777777,"width":0.074652776,"height":0.085},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"AI tools lack access to high-value conversation context","depth":23,"bounds":{"left":0.30868056,"top":0.28111112,"width":0.10555556,"height":0.085},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Customers must manually export/copy transcripts","depth":23,"bounds":{"left":0.30868056,"top":0.37444445,"width":0.108333334,"height":0.085},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"No easy way to combine calls with CRM + docs + other info - 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The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Trying to access private meeting - “Error: Access denied. 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This is not a data or permissions issue — please retry in a few minutes.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to securely control who and what data is accessible externally","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Private meetings shouldn’t be exposed via MCP","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ensure data is structured to be easily combined with:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"CRM (accounts, deals)","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"external docs","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Use consistent identifiers:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"account IDs","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"deal IDs","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"MCP is available only for Scale tier and above","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Customers who are on Capture tier should be able to access any of their data through MCP.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide visibility into how external AI tools access Jiminny data","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Audit logs:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"what data was accessed by which external system when","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"identity - who made the request","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"specific queries/filters used","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"failed access attempts (especially cross-visibility-boundary attempts)","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"example from Atlassian -","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide customers with example prompts to get started quickly","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"See examples in table below.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Expose Jiminny as a Connector in Claude","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide customers with example workflows to get started quickly","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"See examples in table below.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to use predefined Jiminny skills for common workflows","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"See examples in table below.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny UI widget for OpenAI","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The reason to have this is to make certain Jiminny use cases much easier than pure chat.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to run long-running analyses asynchronously and retrieve progress/results","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"start the job","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"let it run in the background","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"return a status like “in progress”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"let the client come back for the result later","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"For example - User asks in Claude:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"“Analyse all calls from this quarter and tell me the top 5 objections by segment.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"That might require:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"searching lots of calls","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"pulling many summaries/transcripts","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"aggregating results","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Instead of making Claude wait 45+ seconds, Jiminny could:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"create an analysis job","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"return: job_id = 123, status = running","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"We need this in order to support:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"large-scale analysis","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"multi-call aggregation across lots of data","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"playlist generation across many calls","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"heavy recap/report generation","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Creating a snippet through the MCP","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"example request:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"{","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"tool\": \"create_snippet\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"arguments\": {","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"call_id\": \"call_123\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"start_time\": \"00:18:24\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"end_time\": \"00:19:10\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"title\": \"Pricing objection\"","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"example response:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"{","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"snippet_id\": \"snip_987\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"title\": \"Pricing objection\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"share_url\": \"https://app.jiminny.com/snippets/snip_987\"","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ability to add recordings or snippets to a playlist","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow AI tools to read reusable Jiminny resources","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"fathom","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"current deal snapshot","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"account summary","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"weekly rep recap","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"approved talk tracks / objection handling guidance","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"curated playlists or snippet libraries","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ability to write coaching notes in Jiminny through MCP","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"What we want to do for prompts/worflows and skills","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Capability","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Type","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"User value / job to be done","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Example Jiminny use 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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST [URL_WITH_CREDENTIALS] Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.
You said
Jiminny MCP Connector
Epic
Link to Epik in Jira
Document status
DRAFT
Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.
👤 Target user
Revenue teams using AI tools (Sales, CS, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
Build an AI Knowledge Connector (MCP-based) that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides structured insights (summaries, action items, scoring etc.)
Supports deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
Metric
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of MCP/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
User interaction and design
Simple setup flow:
Generate API key / connect MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus: usable by non-technical users
Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
Importance
Notes
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH
For each deal expose:
calls/meetings/emails
Identified risks
contacts related to the deal with their job title
linked account details
all CRM field information that we have in DI + fields used for CRM filling
example - get_deal_summary
request -
{
"tool": "get_deal_summary",
"arguments": {
"deal_name": "ACME"
}
}
responce:
{
"deal_name": "ACME",
"stage": "Negotiation",
"recent_activity": [
"Demo call on 2026-04-01",
"Pricing discussion on 2026-04-03"
],
"risks": [
"Pricing concerns",
"Security review pending"
], "next_steps": [
"Send follow-up to VP Sales",
"Share security documentation"
]
}
Ensure the AI can easily understand the schema of the data coming from Jiminny
HIGH
Models perform much better when tools have:
clear names
rich descriptions
predictable schemas
Provide clear, actionable error states for customers when MCP connections fail
HIGH
Error messages should guide agents toward solutions with specific suggestions and next steps.
Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”
Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”
Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”
Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”
Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”
Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”
No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”
Allow customers to securely control who and what data is accessible externally
HIGH
Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well.
Private meetings shouldn’t be exposed via MCP
HIGH
When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP.
Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows
HIGH
Ensure data is structured to be easily combined with:
CRM (accounts, deals)
external docs
Use consistent identifiers:
account IDs
deal IDs
MCP is available only for Scale tier and above
HIGH
Customers who are on Capture tier should be able to access any of their data through MCP.
This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working.
Provide visibility into how external AI tools access Jiminny data
HIGH
Audit logs:
what data was accessed by which external system when
identity - who made the request
specific queries/filters used
failed access attempts (especially cross-visibility-boundary attempts)
Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it.
example from Atlassian -
Provide customers with example prompts to get started quickly
HIGH
MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments.
See examples in table below.
Expose Jiminny as a Connector in Claude
medium
Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny
Provide customers with example workflows to get started quickly
medium
A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.
See examples in table below.
Allow customers to use predefined Jiminny skills for common workflows
medium
Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.
Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com
See examples in table below.
Jiminny UI widget for OpenAI
medium
The reason to have this is to make certain Jiminny use cases much easier than pure chat.
Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.
For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.
Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.
Allow customers to run long-running analyses asynchronously and retrieve progress/results
low
Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to:
start the job
let it run in the background
return a status like “in progress”
let the client come back for the result later
For example - User asks in Claude:
“Analyse all calls from this quarter and tell me the top 5 objections by segment.”
That might require:
searching lots of calls
pulling many summaries/transcripts
aggregating results
Instead of making Claude wait 45+ seconds, Jiminny could:
create an analysis job
return: job_id = 123, status = running
We need this in order to support:
large-scale analysis
multi-call aggregation across lots of data
playlist generation across many calls
heavy recap/report generation
Creating a snippet through the MCP
low
Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team.
example request:
{
"tool": "create_snippet",
"arguments": {
"call_id": "call_123",
"start_time": "00:18:24",
"end_time": "00:19:10",
"title": "Pricing objection"
}
}
example response:
{
"snippet_id": "snip_987",
"title": "Pricing objection",
"share_url": "https://app.jiminny.com/snippets/snip_987"
}
Ability to add recordings or snippets to a playlist
low
Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes
Allow AI tools to read reusable Jiminny resources
low
fathom
Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be:
current deal snapshot
account summary
weekly rep recap
approved talk tracks / objection handling guidance
curated playlists or snippet libraries
This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls.
Ability to write coaching notes in Jiminny through MCP
low
What we want to do for prompts/worflows and skills
Capability
Type
User value / job to be done
Example Jiminny use case
Inputs
Steps performed
Output
Draft follow-up email
Prompt
Help reps quickly create tailored follow-ups after calls
Use the latest customer call to draft an email with recap and next steps
Call ID
optional tone
optional recipient type
Retrieve call summary and action items from Jiminny.
Pull key points, commitments, and next steps.
Apply the chosen tone and structure.
Draft the email in a consistent format.
Customer-ready follow-up email draft
Summarise call
Prompt
Help users quickly extract the most important points from a conversation
Generate a concise summary of one call for internal or external use
Call ID
optional summary style
optional audience
Retrieve transcript, metadata, and structured insights.
Identify key topics, decisions, risks, and next steps.
Format output for the chosen audience.
Short or structured call summary with actions and risks
Generate deal recap
Prompt
Help reps and managers get a fast overview of a deal
Summarise the current state of a deal based on recent calls and linked CRM context
Deal ID or account name
optional date range
Retrieve calls and meetings linked to the deal.
Pull deal metadata and CRM-linked context.
Identify recent changes, blockers, risks, and next steps.
Summarise into a concise recap.
Deal recap with status, blockers, risks, and recommended next steps
Prepare weekly recap
Prompt
Help managers or reps review what changed over a time period
Weekly recap of calls, themes, and actions for one rep, team, or account
Rep ID
team ID or account ID
time period
Retrieve relevant calls and summaries for the selected time period.
Aggregate themes, action items, and changes.
Highlight notable risks, opportunities, and follow-ups.
Weekly recap with top themes, risks, opportunities, and actions
Prepare deal review
Workflow
Help reps and managers review deal status using multiple sources of context
Review a deal before pipeline review or forecast meeting
Deal ID
optional date range
Retrieve all recent calls, emails, and meetings tied to the deal.
Pull structured call summaries, score, action items, and key points.
Pull linked CRM context such as stage, contacts, and account details.
Aggregate open risks, unresolved objections, stakeholder gaps, and next steps.
Format into a review-ready structure.
Deal review document with current status, risks, stakeholders, blockers, and next steps
Weekly team recap...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST [URL_WITH_CREDENTIALS] Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.
You said
Jiminny MCP Connector
Epic
Link to Epik in Jira
Document status
DRAFT
Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.
👤 Target user
Revenue teams using AI tools (Sales, CS, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
Build an AI Knowledge Connector (MCP-based) that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides structured insights (summaries, action items, scoring etc.)
Supports deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
Metric
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of MCP/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
User interaction and design
Simple setup flow:
Generate API key / connect MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus: usable by non-technical users
Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
Importance
Notes
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH
For each deal expose:
calls/meetings/emails
Identified risks
contacts related to the deal with their job title
linked account details
all CRM field information that we have in DI + fields used for CRM filling
example - get_deal_summary
request -
{
"tool": "get_deal_summary",
"arguments": {
"deal_name": "ACME"
}
}
responce:
{
"deal_name": "ACME",
"stage": "Negotiation",
"recent_activity": [
"Demo call on 2026-04-01",
"Pricing discussion on 2026-04-03"
],
"risks": [
"Pricing concerns",
"Security review pending"
], "next_steps": [
"Send follow-up to VP Sales",
"Share security documentation"
]
}
Ensure the AI can easily understand the schema of the data coming from Jiminny
HIGH
Models perform much better when tools have:
clear names
rich descriptions
predictable schemas
Provide clear, actionable error states for customers when MCP connections fail
HIGH
Error messages should guide agents toward solutions with specific suggestions and next steps.
Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”
Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”
Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”
Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”
Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”
Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”
No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”
Allow customers to securely control who and what data is accessible externally
HIGH
Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well.
Private meetings shouldn’t be exposed via MCP
HIGH
When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP.
Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows
HIGH
Ensure data is structured to be easily combined with:
CRM (accounts, deals)
external docs
Use consistent identifiers:
account IDs
deal IDs
MCP is available only for Scale tier and above
HIGH
Customers who are on Capture tier should be able to access any of their data through MCP.
This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working.
Provide visibility into how external AI tools access Jiminny data
HIGH
Audit logs:
what data was accessed by which external system when
identity - who made the request
specific queries/filters used
failed access attempts (especially cross-visibility-boundary attempts)
Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it.
example from Atlassian -
Provide customers with example prompts to get started quickly
HIGH
MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments.
See examples in table below.
Expose Jiminny as a Connector in Claude
medium
Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny
Provide customers with example workflows to get started quickly
medium
A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.
See examples in table below.
Allow customers to use predefined Jiminny skills for common workflows
medium
Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.
Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com
See examples in table below.
Jiminny UI widget for OpenAI
medium
The reason to have this is to make certain Jiminny use cases much easier than pure chat.
Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.
For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.
Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.
Allow customers to run long-running analyses asynchronously and retrieve progress/results
low
Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to:
start the job
let it run in the background
return a status like “in progress”
let the client come back for the result later
For example - User asks in Claude:
“Analyse all calls from this quarter and tell me the top 5 objections by segment.”
That might require:
searching lots of calls
pulling many summaries/transcripts
aggregating results
Instead of making Claude wait 45+ seconds, Jiminny could:
create an analysis job
return: job_id = 123, status = running
We need this in order to support:
large-scale analysis
multi-call aggregation across lots of data
playlist generation across many calls
heavy recap/report generation
Creating a snippet through the MCP
low
Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team.
example request:
{
"tool": "create_snippet",
"arguments": {
"call_id": "call_123",
"start_time": "00:18:24",
"end_time": "00:19:10",
"title": "Pricing objection"
}
}
example response:
{
"snippet_id": "snip_987",
"title": "Pricing objection",
"share_url": "https://app.jiminny.com/snippets/snip_987"
}
Ability to add recordings or snippets to a playlist
low
Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes
Allow AI tools to read reusable Jiminny resources
low
fathom
Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be:
current deal snapshot
account summary
weekly rep recap
approved talk tracks / objection handling guidance
curated playlists or snippet libraries
This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls.
Ability to write coaching notes in Jiminny through MCP
low
What we want to do for prompts/worflows and skills
Capability
Type
User value / job to be done
Example Jiminny use case
Inputs
Steps performed
Output
Draft follow-up email
Prompt
Help reps quickly create tailored follow-ups after calls
Use the latest customer call to draft an email with recap and next steps
Call ID
optional tone
optional recipient type
Retrieve call summary and action items from Jiminny.
Pull key points, commitments, and next steps.
Apply the chosen tone and structure.
Draft the email in a consistent format.
Customer-ready follow-up email draft
Summarise call
Prompt
Help users quickly extract the most important points from a conversation
Generate a concise summary of one call for internal or external use
Call ID
optional summary style
optional audience
Retrieve transcript, metadata, and structured insights.
Identify key topics, decisions, risks, and next steps.
Format output for the chosen audience.
Short or structured call summary with actions and risks
Generate deal recap
Prompt
Help reps and managers get a fast overview of a deal
Summarise the current state of a deal based on recent calls and linked CRM context
Deal ID or account name
optional date range
Retrieve calls and meetings linked to the deal.
Pull deal metadata and CRM-linked context.
Identify recent changes, blockers, risks, and next steps.
Summarise into a concise recap.
Deal recap with status, blockers, risks, and recommended next steps
Prepare weekly recap
Prompt
Help managers or reps review what changed over a time period
Weekly recap of calls, themes, and actions for one rep, team, or account
Rep ID
team ID or account ID
time period
Retrieve relevant calls and summaries for the selected time period.
Aggregate themes, action items, and changes.
Highlight notable risks, opportunities, and follow-ups.
Weekly recap with top themes, risks, opportunities, and actions
Prepare deal review
Workflow
Help reps and managers review deal status using multiple sources of context
Review a deal before pipeline review or forecast meeting
Deal ID
optional date range
Retrieve all recent calls, emails, and meetings tied to the deal.
Pull structured call summaries, score, action items, and key points.
Pull linked CRM context such as stage, contacts, and account details.
Aggregate open risks, unresolved objections, stakeholder gaps, and next steps.
Format into a review-ready structure.
Deal review document with current status, risks, stakeholders, blockers, and next steps
Weekly team recap
Workflow
Help managers monitor activity and risk across a group of reps or accounts
End-of-week team summary for sales leadership
Team ID
date range
Retrieve calls and structured insights across the team.
Group by rep, account, or deal.
Identify recurring themes, major risks, strong calls, and missed follow-ups.
Highlight calls or deals that need attention.
Team-level recap with patterns, key risks, notable wins, and action areas
Objection analysis across calls
Workflow
Help teams identify patterns across multiple conversations
Analyse pricing or competitor objections across recent calls
Account ID
deal ID
rep ID
keyword/topic
date range
Search calls matching the topic or keyword.
Retrieve summaries, transcripts, and snippets where relevant.
Identify recurring objections and how they were handled.
Assess whether objections were resolved or remain open.
Summarise patterns and examples.
Objection analysis with themes, frequency, examples, and suggested follow-up actions
Build onboarding playlist
Workflow
Help enablement teams curate useful learning content faster
Build a playlist of strong discovery, demo, or pricing calls for new hires
Topic
call type
rep/team filter
optional time period
Search for calls matching the selected criteria.
Rank or filter calls based on score, topics, or key moments.
Identify useful snippets or full recordings.
Add selected calls/snippets into a playlist structure.
Generate short descriptions for each item.
Curated onboarding playlist with selected calls/snippets and descriptions
Deal Inspection Skill
Skill
Give users a consistent, opinionated way to assess a deal
Claude reviews a deal using a repeatable Jiminny methodology
Deal ID or account name
optional review scope
Determine whether the request is suitable for a deal inspection.
Retrieve relevant calls, summaries, CRM context, stakeholders, and risks.
Apply a standard Jiminny review framework to assess deal health.
Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs.
Present findings in a consistent structure with recommended actions.
Structured deal inspection with health assessment, red flags, and recommendations
Manager Coaching Skill
Skill
Help managers review calls consistently and identify coaching moments
Analyse a rep’s call and surface strengths, gaps, and examples for coaching
Call ID
optional rep ID
optional coaching focus
Retrieve transcript, score, summary, and key moments from the call.
Evaluate the conversation against a coaching framework.
Identify what went well and where the rep could improve.
Suggest moments to clip as snippets.
Recommend coaching actions or playlist additions.
Coaching review with strengths, gaps, snippet suggestions, and recommended coaching actions
CSM / QBR Prep Skill
Skill
Help CS or AM teams prepare structured customer reviews from conversations
Generate an account review or QBR-style summary from recent customer interactions
Account ID, optional time period
Retrieve all recent calls, emails, and account-linked context.
Aggregate themes, requests, blockers, sentiment shifts, and opportunities.
Apply a repeatable account review structure.
Summarise outcomes, open issues, and suggested next steps.
Structured QBR/account review summary with themes, risks, opportunities, and actions
Snippet Builder Skill
Skill
Help users consistently capture and label useful moments from calls
Identify useful moments in a call and convert them into reusable snippets
Call ID, optional focus area such as pricing, objection, competitor, discovery
Retrieve transcript, recording timeline, and call insights.
Detect moments matching the requested focus area.
Propose snippet start/end times and titles.
Tag snippets by type or theme.
Optionally save them or add them to a playlist.
One or more proposed or saved snippets with title, timing, and tags
💡 Technical details
Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate.
Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users.
Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns.
Open Questions
Track unresolved issues or decisions that need to be made. Update as answers become available.
Question
Answer
Date Answered
Competitor research
Competitor
MCP Availability
Core Capabilities
Advanced Capabilities
Positioning
Key Strength
Key Weakness
Gong
✅ Mature MCP
List/search calls
Get transcripts
get Call metadata
Deal intelligence
Scorecards, analytics
CRM context
Enterprise data layer
Deep data + ecosystem integrations
Limited opinionated AI workflows
Avoma
✅ Mature MCP
list_meetings
get_meeting
get_transcript
key topics
Deal fields/Deal level analysis
Coaching/Scoring
CRM context
pushing insights into Notion / Confluence
AI-ready meeting intelligence
Strongest “outputs” layer
Mostly pull-based interactions
Fathom
✅ MCP
list/search meetings
get transcript
get summary/action items
Team/org context
Webhooks (event-based triggers)
get_weekly_recap
aggregated action items across meetings
creating webhooks directly from the MCP server
Lightweight meeting data access
Includes summaries + actions
Limited analytics depth
Grain
✅ Basic MCP
get meetings
download transcripts
Notes
Deals
Scorecards
Data access utility
Simple + accessible
Low sophistication, sometimes browser-based
Outreach
⚠️ MCP (workflow-focused)
Search sequences
Emails
Meetings (Kaia)
account / prospect / opportunity lookups
Workflow automation
Multi-system orchestration
Revenue workflow engine
End-to-end GTM workflows
Less depth in conversation data
Attention
⚠️ Emerging MCP
Meeting data
Transcripts
CRM-linked context
AI insights
Sales assistant workflows
create snippets
AI sales assistant + CRM
Strong CRM linkage
Less focus on external knowledge layer
Planhat -
Related content
Ask Jiminny Anything on Call level
Galya Dimitrova
Hubspot app
Galya Dimitrova
Product Vision & Strategy
Galya Dimitrova
Automated CRM Filling
Simona Georgieva (Deactivated)
Competitive analysis
Galya Dimitrova
AI agent ideas
Steliyan Georgiev
Comments
James Graham
9 Apr
A few areas around Security & Compliance that need more depth before this moves beyond draft:
Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate.
Team visibility enforcement should be its own HIGH requirement. The current note under access control ("ensure MCP only exposes data user has access to") is doing too much work as a single line. Every MCP tool response (especially search_calls and deal-level aggregations) must be filtered through the same visibility engine as the UI. Semantic search is the riskiest here; it could surface results across team boundaries if not properly scoped.
No data minimisation or scoping controls. We're exposing transcripts, CRM fields, contact details with job titles, deal context - significant PII and commercial data leaving the platform. Can customers configure which data types are exposed? Can transcripts be PII-stripped before they leave Jiminny? Given we position GDPR consent as a differentiator, we should be thinking about this.
GDPR downstream processing is unaddressed. Piping transcripts to OpenAI/Gemini introduces new data processors. Customers need to acknowledge responsibility for downstream processing during setup. We should also consider whether call participants' recording consent covers onward sharing to third-party AI - it almost certainly doesn't by default. This is one for me to pick up.
Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users.
Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns.
Audit logs should go deeper. Current spec (what data, which system, when) is a start. Add: identity behind the key, specific queries/filters used, and failed access attempts (especially cross-visibility-boundary attempts). Enterprise compliance teams will expect this.
Downgrade handling. Doc says Scale tier and above, but doesn't say what happens on downgrade. Keys should auto-deactivate.
Happy to help draft a dedicated Security & Compliance section i...
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Objective Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows. Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product. 👤 Target user Revenue teams using AI tools (Sales, CS, RevOps) Companies already experimenting with Claude / OpenAI Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc) 🤕 Pain point or problem Jiminny data is locked in the platform AI tools lack access to high-value conversation context Customers must manually export/copy transcripts No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people Peer pressure as almost all competitors have this 💥 Impact and benefits For customers Better AI outputs using real customer conversations Ability to build automated workflows across tools For Jiminny Increased product stickiness (embedded in workflows) Competitive parity with Gong / Avoma etc. 💡 Solution ideas Build an AI Knowledge Connector (MCP-based) that: Exposes Jiminny data (calls, transcripts, metadata) to AI tools Provides structured insights (summaries, action items, scoring etc.) Supports deal-level context Allows integration into customer-owned AI stacks (Claude + Zapier, etc.) Success metrics List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets). Goal Metric Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows % of customers connecting external AI tools Number of MCP/API calls per customer - and which ones are being used the most Decrease the manual copying of transcripts decrease by X % User interaction and design Simple setup flow: Generate API key / connect MCP Clear instructions for Claude / Zapier setup Provide: Example prompts for Claude Pre-built workflow templates for Zapier Clear documentation - with use cases 👉 Focus: usable by non-technical users Detailed Requirements List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification. Requirement Importance Notes Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI) HIGH Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack Allow customers to access call data through AI tools HIGH Expose Jiminny data: list calls (filter by date, user, account, deal) get call get transcript (speaker-separated) get call metadata search calls (keyword + semantic) Support filtering by: account / contact/ lead / deal jiminny user date range Allow customers to search and query calls using AI tools HIGH Support keyword and semantic search across conversations Provide structured insights so customers can generate better outputs HIGH For each call, expose: Summary Action items Key points Activity type Notes Call Score CRM information Allow customers to understand deal status across multiple calls/emails HIGH For each deal expose: calls/meetings/emails Identified risks contacts related to the deal with their job title linked account details all CRM field information that we have in DI + fields used for CRM filling example - get_deal_summary request - { \"tool\": \"get_deal_summary\", \"arguments\": { \"deal_name\": \"ACME\" } } responce: { \"deal_name\": \"ACME\", \"stage\": \"Negotiation\", \"recent_activity\": [ \"Demo call on 2026-04-01\", \"Pricing discussion on 2026-04-03\" ], \"risks\": [ \"Pricing concerns\", \"Security review pending\" ], \"next_steps\": [ \"Send follow-up to VP Sales\", \"Share security documentation\" ] } Ensure the AI can easily understand the schema of the data coming from Jiminny HIGH Models perform much better when tools have: clear names rich descriptions predictable schemas Provide clear, actionable error states for customers when MCP connections fail HIGH Error messages should guide agents toward solutions with specific suggestions and next steps. Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.” Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.” Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.” Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.” Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.” Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.” No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.” Allow customers to securely control who and what data is accessible externally HIGH Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well. Private meetings shouldn’t be exposed via MCP HIGH When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP. Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows HIGH Ensure data is structured to be easily combined with: CRM (accounts, deals) external docs Use consistent identifiers: account IDs deal IDs MCP is available only for Scale tier and above HIGH Customers who are on Capture tier should be able to access any of their data through MCP. This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working. Provide visibility into how external AI tools access Jiminny data HIGH Audit logs: what data was accessed by which external system when identity - who made the request specific queries/filters used failed access attempts (especially cross-visibility-boundary attempts) Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it. example from Atlassian - Provide customers with example prompts to get started quickly HIGH MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments. See examples in table below. Expose Jiminny as a Connector in Claude medium Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny Provide customers with example workflows to get started quickly medium A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome. See examples in table below. Allow customers to use predefined Jiminny skills for common workflows medium Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job. Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com See examples in table below. Jiminny UI widget for OpenAI medium The reason to have this is to make certain Jiminny use cases much easier than pure chat. Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it. For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface. Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section. Allow customers to run long-running analyses asynchronously and retrieve progress/results low Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to: start the job let it run in the background return a status like “in progress” let the client come back for the result later For example - User asks in Claude: “Analyse all calls from this quarter and tell me the top 5 objections by segment.” That might require: searching lots of calls pulling many summaries/transcripts aggregating results Instead of making Claude wait 45+ seconds, Jiminny could: create an analysis job return: job_id = 123, status = running We need this in order to support: large-scale analysis multi-call aggregation across lots of data playlist generation across many calls heavy recap/report generation Creating a snippet through the MCP low Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team. example request: { \"tool\": \"create_snippet\", \"arguments\": { \"call_id\": \"call_123\", \"start_time\": \"00:18:24\", \"end_time\": \"00:19:10\", \"title\": \"Pricing objection\" } } example response: { \"snippet_id\": \"snip_987\", \"title\": \"Pricing objection\", \"share_url\": \"https://app.jiminny.com/snippets/snip_987\" } Ability to add recordings or snippets to a playlist low Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes Allow AI tools to read reusable Jiminny resources low fathom Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be: current deal snapshot account summary weekly rep recap approved talk tracks / objection handling guidance curated playlists or snippet libraries This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls. Ability to write coaching notes in Jiminny through MCP low What we want to do for prompts/worflows and skills Capability Type User value / job to be done Example Jiminny use case Inputs Steps performed Output Draft follow-up email Prompt Help reps quickly create tailored follow-ups after calls Use the latest customer call to draft an email with recap and next steps Call ID optional tone optional recipient type Retrieve call summary and action items from Jiminny. Pull key points, commitments, and next steps. Apply the chosen tone and structure. Draft the email in a consistent format. Customer-ready follow-up email draft Summarise call Prompt Help users quickly extract the most important points from a conversation Generate a concise summary of one call for internal or external use Call ID optional summary style optional audience Retrieve transcript, metadata, and structured insights. Identify key topics, decisions, risks, and next steps. Format output for the chosen audience. Short or structured call summary with actions and risks Generate deal recap Prompt Help reps and managers get a fast overview of a deal Summarise the current state of a deal based on recent calls and linked CRM context Deal ID or account name optional date range Retrieve calls and meetings linked to the deal. Pull deal metadata and CRM-linked context. Identify recent changes, blockers, risks, and next steps. Summarise into a concise recap. Deal recap with status, blockers, risks, and recommended next steps Prepare weekly recap Prompt Help managers or reps review what changed over a time period Weekly recap of calls, themes, and actions for one rep, team, or account Rep ID team ID or account ID time period Retrieve relevant calls and summaries for the selected time period. Aggregate themes, action items, and changes. Highlight notable risks, opportunities, and follow-ups. Weekly recap with top themes, risks, opportunities, and actions Prepare deal review Workflow Help reps and managers review deal status using multiple sources of context Review a deal before pipeline review or forecast meeting Deal ID optional date range Retrieve all recent calls, emails, and meetings tied to the deal. Pull structured call summaries, score, action items, and key points. Pull linked CRM context such as stage, contacts, and account details. Aggregate open risks, unresolved objections, stakeholder gaps, and next steps. Format into a review-ready structure. Deal review document with current status, risks, stakeholders, blockers, and next steps Weekly team recap Workflow Help managers monitor activity and risk across a group of reps or accounts End-of-week team summary for sales leadership Team ID date range Retrieve calls and structured insights across the team. Group by rep, account, or deal. Identify recurring themes, major risks, strong calls, and missed follow-ups. Highlight calls or deals that need attention. Team-level recap with patterns, key risks, notable wins, and action areas Objection analysis across calls Workflow Help teams identify patterns across multiple conversations Analyse pricing or competitor objections across recent calls Account ID deal ID rep ID keyword/topic date range Search calls matching the topic or keyword. Retrieve summaries, transcripts, and snippets where relevant. Identify recurring objections and how they were handled. Assess whether objections were resolved or remain open. Summarise patterns and examples. Objection analysis with themes, frequency, examples, and suggested follow-up actions Build onboarding playlist Workflow Help enablement teams curate useful learning content faster Build a playlist of strong discovery, demo, or pricing calls for new hires Topic call type rep/team filter optional time period Search for calls matching the selected criteria. Rank or filter calls based on score, topics, or key moments. Identify useful snippets or full recordings. Add selected calls/snippets into a playlist structure. Generate short descriptions for each item. Curated onboarding playlist with selected calls/snippets and descriptions Deal Inspection Skill Skill Give users a consistent, opinionated way to assess a deal Claude reviews a deal using a repeatable Jiminny methodology Deal ID or account name optional review scope Determine whether the request is suitable for a deal inspection. Retrieve relevant calls, summaries, CRM context, stakeholders, and risks. Apply a standard Jiminny review framework to assess deal health. Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs. Present findings in a consistent structure with recommended actions. Structured deal inspection with health assessment, red flags, and recommendations Manager Coaching Skill Skill Help managers review calls consistently and identify coaching moments Analyse a rep’s call and surface strengths, gaps, and examples for coaching Call ID optional rep ID optional coaching focus Retrieve transcript, score, summary, and key moments from the call. Evaluate the conversation against a coaching framework. Identify what went well and where the rep could improve. Suggest moments to clip as snippets. Recommend coaching actions or playlist additions. Coaching review with strengths, gaps, snippet suggestions, and recommended coaching actions CSM / QBR Prep Skill Skill Help CS or AM teams prepare structured customer reviews from conversations Generate an account review or QBR-style summary from recent customer interactions Account ID, optional time period Retrieve all recent calls, emails, and account-linked context. Aggregate themes, requests, blockers, sentiment shifts, and opportunities. Apply a repeatable account review structure. Summarise outcomes, open issues, and suggested next steps. Structured QBR/account review summary with themes, risks, opportunities, and actions Snippet Builder Skill Skill Help users consistently capture and label useful moments from calls Identify useful moments in a call and convert them into reusable snippets Call ID, optional focus area such as pricing, objection, competitor, discovery Retrieve transcript, recording timeline, and call insights. Detect moments matching the requested focus area. Propose snippet start/end times and titles. Tag snippets by type or theme. Optionally save them or add them to a playlist. One or more proposed or saved snippets with title, timing, and tags 💡 Technical details Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate. Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users. Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns. Open Questions Track unresolved issues or decisions that need to be made. Update as answers become available. Question Answer Date Answered Competitor research Competitor MCP Availability Core Capabilities Advanced Capabilities Positioning Key Strength Key Weakness Gong ✅ Mature MCP List/search calls Get transcripts get Call metadata Deal intelligence Scorecards, analytics CRM context Enterprise data layer Deep data + ecosystem integrations Limited opinionated AI workflows Avoma ✅ Mature MCP list_meetings get_meeting get_transcript key topics Deal fields/Deal level analysis Coaching/Scoring CRM context pushing insights into Notion / Confluence AI-ready meeting intelligence Strongest “outputs” layer Mostly pull-based interactions Fathom ✅ MCP list/search meetings get transcript get summary/action items Team/org context Webhooks (event-based triggers) get_weekly_recap aggregated action items across meetings creating webhooks directly from the MCP server Lightweight meeting data access Includes summaries + actions Limited analytics depth Grain ✅ Basic MCP get meetings download transcripts Notes Deals Scorecards Data access utility Simple + accessible Low sophistication, sometimes browser-based Outreach ⚠️ MCP (workflow-focused) Search sequences Emails Meetings (Kaia) account / prospect / opportunity lookups Workflow automation Multi-system orchestration Revenue workflow engine End-to-end GTM workflows Less depth in conversation data Attention ⚠️ Emerging MCP Meeting data Transcripts CRM-linked context AI insights Sales assistant workflows create snippets AI sales assistant + CRM Strong CRM linkage Less focus on external knowledge layer Planhat - Related content Ask Jiminny Anything on Call level Galya Dimitrova Hubspot app Galya Dimitrova Product Vision & Strategy Galya Dimitrova Automated CRM Filling Simona Georgieva (Deactivated) Competitive analysis Galya Dimitrova AI agent ideas Steliyan Georgiev Comments James Graham 9 Apr A few areas around Security & Compliance that need more depth before this moves beyond draft: Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate. Team visibility enforcement should be its own HIGH requirement. The current note under access control (\"ensure MCP only exposes data user has access to\") is doing too much work as a single line. Every MCP tool response (especially search_calls and deal-level aggregations) must be filtered through the same visibility engine as the UI. Semantic search is the riskiest here; it could surface results across team boundaries if not properly scoped. No data minimisation or scoping controls. We're exposing transcripts, CRM fields, contact details with job titles, deal context - significant PII and commercial data leaving the platform. Can customers configure which data types are exposed? Can transcripts be PII-stripped before they leave Jiminny? Given we position GDPR consent as a differentiator, we should be thinking about this. GDPR downstream processing is unaddressed. Piping transcripts to OpenAI/Gemini introduces new data processors. Customers need to acknowledge responsibility for downstream processing during setup. We should also consider whether call participants' recording consent covers onward sharing to third-party AI - it almost certainly doesn't by default. This is one for me to pick up. Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users. Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns. Audit logs should go deeper. Current spec (what data, which system, when) is a start. Add: identity behind the key, specific queries/filters used, and failed access attempts (especially cross-visibility-boundary attempts). Enterprise compliance teams will expect this. Downgrade handling. Doc says Scale tier and above, but doesn't say what happens on downgrade. Keys should auto-deactivate. Happy to help draft a dedicated Security & Compliance section if useful. Galya Dimitrova 15 Apr Thanks for the input @James Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.","depth":21,"bounds":{"left":0.41805187,"top":0.19313647,"width":0.051861703,"height":0.11173184},"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"You said","depth":23,"bounds":{"left":0.36053857,"top":0.19553073,"width":0.019946808,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny MCP Connector","depth":23,"bounds":{"left":0.41805187,"top":0.1963288,"width":0.03125,"height":0.038707104},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Epic","depth":23,"bounds":{"left":0.41805187,"top":0.30806065,"width":0.010305851,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Link to Epik in Jira","depth":23,"bounds":{"left":0.41805187,"top":0.33040702,"width":0.043218084,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Document status","depth":23,"bounds":{"left":0.41805187,"top":0.3527534,"width":0.04105718,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"DRAFT","depth":23,"bounds":{"left":0.41805187,"top":0.37509975,"width":0.016289894,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Objective","depth":23,"bounds":{"left":0.41805187,"top":0.39744613,"width":0.024268618,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.","depth":23,"bounds":{"left":0.41805187,"top":0.4197925,"width":0.051529255,"height":0.17278531},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.","depth":23,"bounds":{"left":0.41805187,"top":0.59856343,"width":0.049700797,"height":0.10574621},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"👤 Target user","depth":23,"bounds":{"left":0.41805187,"top":0.75458896,"width":0.033909574,"height":0.016759777},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Revenue teams using AI tools (Sales, CS, RevOps)","depth":23,"bounds":{"left":0.41805187,"top":0.7773344,"width":0.051030584,"height":0.061053474},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Companies already experimenting with Claude / OpenAI","depth":23,"bounds":{"left":0.41805187,"top":0.8443735,"width":0.04637633,"height":0.061053474},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)","depth":23,"bounds":{"left":0.41805187,"top":0.9114126,"width":0.051363032,"height":0.0885874},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"🤕 Pain point or problem","depth":23,"bounds":{"left":0.41805187,"top":1.0,"width":0.03706782,"height":-0.0897845},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny data is locked in the platform","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"AI tools lack access to high-value conversation context","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Customers must manually export/copy transcripts","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"No easy way to combine calls with CRM + docs + other info - 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The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to securely control who and what data is accessible externally","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ensure MCP only exposes data user has access to - based on team visibility settings. 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This will make it easier for customers to find us and to connect Jiminny","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide customers with example workflows to get started quickly","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"See examples in table below.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to use predefined Jiminny skills for common workflows","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"See examples in table below.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny UI widget for OpenAI","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The reason to have this is to make certain Jiminny use cases much easier than pure chat.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to run long-running analyses asynchronously and retrieve progress/results","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Some requests/questions require analysis of Hugh volumes of data. 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calls.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ability to write coaching notes in Jiminny through MCP","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"What we want to do for prompts/worflows and skills","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Capability","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Type","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"User value / job to be done","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Example Jiminny use 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Semantic search is the riskiest here; it could surface results across team boundaries if not properly scoped.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"No data minimisation or scoping controls. We're exposing transcripts, CRM fields, contact details with job titles, deal context - significant PII and commercial data leaving the platform. Can customers configure which data types are exposed? Can transcripts be PII-stripped before they leave Jiminny? Given we position GDPR consent as a differentiator, we should be thinking about this.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"GDPR downstream processing is unaddressed. Piping transcripts to OpenAI/Gemini introduces new data processors. Customers need to acknowledge responsibility for downstream processing during setup. We should also consider whether call participants' recording consent covers onward sharing to third-party AI - it almost certainly doesn't by default. This is one for me to pick up.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Audit logs should go deeper. Current spec (what data, which system, when) is a start. Add: identity behind the key, specific queries/filters used, and failed access attempts (especially cross-visibility-boundary attempts). Enterprise compliance teams will expect this.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Downgrade handling. Doc says Scale tier and above, but doesn't say what happens on downgrade. Keys should auto-deactivate.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Happy to help draft a dedicated Security & Compliance section if useful.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Galya Dimitrova","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"15 Apr","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Thanks for the input @James Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Expand","depth":21,"bounds":{"left":0.46991357,"top":0.19313647,"width":0.013297873,"height":0.031923383},"role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXButton","text":"Listen","depth":21,"bounds":{"left":0.47523272,"top":0.3367917,"width":0.013297873,"height":0.031923383},"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXButton","text":"Show more options","depth":20,"bounds":{"left":0.47523272,"top":0.3312051,"width":0.013297873,"height":0.031923383},"role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Show thinking","depth":26,"bounds":{"left":0.3828125,"top":0.33918595,"width":0.030917553,"height":0.014764565},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"Gemini said","depth":20,"bounds":{"left":0.3804854,"top":0.38068634,"width":0.0003324468,"height":0.0007980846},"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Gemini said","depth":21,"bounds":{"left":0.3804854,"top":0.38268158,"width":0.04105718,"height":0.01915403},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Thank you for sharing the draft PRD for the","depth":24,"bounds":{"left":0.3695146,"top":0.39026338,"width":0.10521942,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny MCP Connector","depth":24,"bounds":{"left":0.3695146,"top":0.41101357,"width":0.0625,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":". It looks like a highly strategic initiative to transition Jiminny from a standalone platform into a foundational data layer for AI-driven revenue workflows.","depth":24,"bounds":{"left":0.3695146,"top":0.41101357,"width":0.11319814,"height":0.07861133},"help_text":"","role_description":"text","subrole":"AXUnknown"}]...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST [URL_WITH_CREDENTIALS] Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.
You said
Jiminny MCP Connector
Epic
Link to Epik in Jira
Document status
DRAFT
Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.
👤 Target user
Revenue teams using AI tools (Sales, CS, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
Build an AI Knowledge Connector (MCP-based) that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides structured insights (summaries, action items, scoring etc.)
Supports deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
Metric
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of MCP/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
User interaction and design
Simple setup flow:
Generate API key / connect MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus: usable by non-technical users
Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
Importance
Notes
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH
For each deal expose:
calls/meetings/emails
Identified risks
contacts related to the deal with their job title
linked account details
all CRM field information that we have in DI + fields used for CRM filling
example - get_deal_summary
request -
{
"tool": "get_deal_summary",
"arguments": {
"deal_name": "ACME"
}
}
responce:
{
"deal_name": "ACME",
"stage": "Negotiation",
"recent_activity": [
"Demo call on 2026-04-01",
"Pricing discussion on 2026-04-03"
],
"risks": [
"Pricing concerns",
"Security review pending"
], "next_steps": [
"Send follow-up to VP Sales",
"Share security documentation"
]
}
Ensure the AI can easily understand the schema of the data coming from Jiminny
HIGH
Models perform much better when tools have:
clear names
rich descriptions
predictable schemas
Provide clear, actionable error states for customers when MCP connections fail
HIGH
Error messages should guide agents toward solutions with specific suggestions and next steps.
Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”
Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”
Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”
Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”
Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”
Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”
No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”
Allow customers to securely control who and what data is accessible externally
HIGH
Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well.
Private meetings shouldn’t be exposed via MCP
HIGH
When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP.
Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows
HIGH
Ensure data is structured to be easily combined with:
CRM (accounts, deals)
external docs
Use consistent identifiers:
account IDs
deal IDs
MCP is available only for Scale tier and above
HIGH
Customers who are on Capture tier should be able to access any of their data through MCP.
This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working.
Provide visibility into how external AI tools access Jiminny data
HIGH
Audit logs:
what data was accessed by which external system when
identity - who made the request
specific queries/filters used
failed access attempts (especially cross-visibility-boundary attempts)
Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it.
example from Atlassian -
Provide customers with example prompts to get started quickly
HIGH
MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments.
See examples in table below.
Expose Jiminny as a Connector in Claude
medium
Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny
Provide customers with example workflows to get started quickly
medium
A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.
See examples in table below.
Allow customers to use predefined Jiminny skills for common workflows
medium
Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.
Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com
See examples in table below.
Jiminny UI widget for OpenAI
medium
The reason to have this is to make certain Jiminny use cases much easier than pure chat.
Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.
For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.
Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.
Allow customers to run long-running analyses asynchronously and retrieve progress/results
low
Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to:
start the job
let it run in the background
return a status like “in progress”
let the client come back for the result later
For example - User asks in Claude:
“Analyse all calls from this quarter and tell me the top 5 objections by segment.”
That might require:
searching lots of calls
pulling many summaries/transcripts
aggregating results
Instead of making Claude wait 45+ seconds, Jiminny could:
create an analysis job
return: job_id = 123, status = running
We need this in order to support:
large-scale analysis
multi-call aggregation across lots of data
playlist generation across many calls
heavy recap/report generation
Creating a snippet through the MCP
low
Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team.
example request:
{
"tool": "create_snippet",
"arguments": {
"call_id": "call_123",
"start_time": "00:18:24",
"end_time": "00:19:10",
"title": "Pricing objection"
}
}
example response:
{
"snippet_id": "snip_987",
"title": "Pricing objection",
"share_url": "https://app.jiminny.com/snippets/snip_987"
}
Ability to add recordings or snippets to a playlist
low
Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes
Allow AI tools to read reusable Jiminny resources
low
fathom
Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be:
current deal snapshot
account summary
weekly rep recap
approved talk tracks / objection handling guidance
curated playlists or snippet libraries
This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls.
Ability to write coaching notes in Jiminny through MCP
low
What we want to do for prompts/worflows and skills
Capability
Type
User value / job to be done
Example Jiminny use case
Inputs
Steps performed
Output
Draft follow-up email
Prompt
Help reps quickly create tailored follow-ups after calls
Use the latest customer call to draft an email with recap and next steps
Call ID
optional tone
optional recipient type
Retrieve call summary and action items from Jiminny.
Pull key points, commitments, and next steps.
Apply the chosen tone and structure.
Draft the email in a consistent format.
Customer-ready follow-up email draft
Summarise call
Prompt
Help users quickly extract the most important points from a conversation
Generate a concise summary of one call for internal or external use
Call ID
optional summary style
optional audience
Retrieve transcript, metadata, and structured insights.
Identify key topics, decisions, risks, and next steps.
Format output for the chosen audience.
Short or structured call summary with actions and risks
Generate deal recap
Prompt
Help reps and managers get a fast overview of a deal
Summarise the current state of a deal based on recent calls and linked CRM context
Deal ID or account name
optional date range
Retrieve calls and meetings linked to the deal.
Pull deal metadata and CRM-linked context.
Identify recent changes, blockers, risks, and next steps.
Summarise into a concise recap.
Deal recap with status, blockers, risks, and recommended next steps
Prepare weekly recap
Prompt
Help managers or reps review what changed over a time period
Weekly recap of calls, themes, and actions for one rep, team, or account
Rep ID
team ID or account ID
time period
Retrieve relevant calls and summaries for the selected time period.
Aggregate themes, action items, and changes.
Highlight notable risks, opportunities, and follow-ups.
Weekly recap with top themes, risks, opportunities, and actions
Prepare deal review
Workflow
Help reps and managers review deal status using multiple sources of context
Review a deal before pipeline review or forecast meeting
Deal ID
optional date range
Retrieve all recent calls, emails, and meetings tied to the deal.
Pull structured call summaries, score, action items, and key points.
Pull linked CRM context such as stage, contacts, and account details.
Aggregate open risks, unresolved objections, stakeholder gaps, and next steps.
Format into a review-ready structure.
Deal review document with current status, risks, stakeholders, blockers, and next steps
Weekly team recap
Workflow
Help managers monitor activity and risk across a group of reps or accounts
End-of-week team summary for sales leadership
Team ID
date range
Retrieve calls and structured insights across the team.
Group by rep, account, or deal.
Identify recurring themes, major risks, strong calls, and missed follow-ups.
Highlight calls or deals that need attention.
Team-level recap with patterns, key risks, notable wins, and action areas
Objection analysis across calls
Workflow
Help teams identify patterns across multiple conversations
Analyse pricing or competitor objections across recent calls
Account ID
deal ID
rep ID
keyword/topic
date range
Search calls matching the topic or keyword.
Retrieve summaries, transcripts, and snippets where relevant.
Identify recurring objections and how they were handled.
Assess whether objections were resolved or remain open.
Summarise patterns and examples.
Objection analysis with themes, frequency, examples, and suggested follow-up actions
Build onboarding playlist
Workflow
Help enablement teams curate useful learning content faster
Build a playlist of strong discovery, demo, or pricing calls for new hires
Topic
call type
rep/team filter
optional time period
Search for calls matching the selected criteria.
Rank or filter calls based on score, topics, or key moments.
Identify useful snippets or full recordings.
Add selected calls/snippets into a playlist structure.
Generate short descriptions for each item.
Curated onboarding playlist with selected calls/snippets and descriptions
Deal Inspection Skill
Skill
Give users a consistent, opinionated way to assess a deal
Claude reviews a deal using a repeatable Jiminny methodology
Deal ID or account name
optional review scope
Determine whether the request is suitable for a deal inspection.
Retrieve relevant calls, summaries, CRM context, stakeholders, and risks.
Apply a standard Jiminny review framework to assess deal health.
Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs.
Present findings in a consistent structure with recommended actions.
Structured deal inspection with health assessment, red flags, and recommendations
Manager Coaching Skill
Skill
Help managers review calls consistently and identify coaching moments
Analyse a rep’s call and surface strengths, gaps, and examples for coaching
Call ID
optional rep ID
optional coaching focus
Retrieve transcript, score, summary, and key moments from the call.
Evaluate the conversation against a coaching framework.
Identify what went well and where the rep could improve.
Suggest moments to clip as snippets.
Recommend coaching actions or playlist additions.
Coaching review with strengths, gaps, snippet suggestions, and recommended coaching actions
CSM / QBR Prep Skill
Skill
Help CS or AM teams prepare structured customer reviews from conversations
Generate an account review or QBR-style summary from recent customer interactions
Account ID, optional time period
Retrieve all recent calls, emails, and account-linked context.
Aggregate themes, requests, blockers, sentiment shifts, and opportunities.
Apply a repeatable account review structure.
Summarise outcomes, open issues, and suggested next steps.
Structured QBR/account review summary with themes, risks, opportunities, and actions
Snippet Builder Skill
Skill
Help users consistently capture and label useful moments from calls
Identify useful moments in a call and convert them into reusable snippets
Call ID, optional focus area such as pricing, objection, competitor, discovery
Retrieve transcript, recording timeline, and call insights.
Detect moments matching the requested focus area.
Propose snippet start/end times and titles.
Tag snippets by type or theme.
Optionally save them or add them to a playlist.
One or more proposed or saved snippets with title, timing, and tags
💡 Technical details
Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate.
Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users.
Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns.
Open Questions
Track unresolved issues or decisions that need to be made. Update as answers become available.
Question
Answer
Date Answered
Competitor research
Competitor
MCP Availability
Core Capabilities
Advanced Capabilities
Positioning
Key Strength
Key Weakness
Gong
✅ Mature MCP
List/search calls
Get transcripts
get Call metadata
Deal intelligence
Scorecards, analytics
CRM context
Enterprise data layer
Deep data + ecosystem integrations
Limited opinionated AI workflows
Avoma
✅ Mature MCP
list_meetings
get_meeting
get_transcript
key topics
Deal fields/Deal level analysis
Coaching/Scoring
CRM context
pushing insights into Notion / Confluence
AI-ready meeting intelligence
Strongest “outputs” layer
Mostly pull-based interactions
Fathom
✅ MCP
list/search meetings
get transcript
get summary/action items
Team/org context
Webhooks (event-based triggers)
get_weekly_recap
aggregated action items across meetings
creating webhooks directly from the MCP server
Lightweight meeting data access
Includes summaries + actions
Limited analytics depth
Grain
✅ Basic MCP
get meetings
download transcripts
Notes
Deals
Scorecards
Data access utility
Simple + accessible
Low sophistication, sometimes browser-based
Outreach
⚠️ MCP (workflow-focused)
Search sequences
Emails
Meetings (Kaia)
account / prospect / opportunity lookups
Workflow automation
Multi-system orchestration
Revenue workflow engine
End-to-end GTM workflows
Less depth in conversation data
Attention
⚠️ Emerging MCP
Meeting data
Transcripts
CRM-linked context
AI insights
Sales assistant workflows
create snippets
AI sales assistant + CRM
Strong CRM linkage
Less focus on external knowledge layer
Planhat -
Related content
Ask Jiminny Anything on Call level
Galya Dimitrova
Hubspot app
Galya Dimitrova
Product Vision & Strategy
Galya Dimitrova
Automated CRM Filling
Simona Georgieva (Deactivated)
Competitive analysis
Galya Dimitrova
AI agent ideas
Steliyan Georgiev
Comments
James Graham
9 Apr
A few areas around Security & Compliance that need more depth before this moves beyond draft:
Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate.
Team visibility enforcement should be its own HIGH requirement. The current note under access control ("ensure MCP only exposes data user has access to") is doing too much work as a single line. Every MCP tool response (especially search_calls and deal-level aggregations) must be filtered through the same visibility engine as the UI. Semantic search is the riskiest here; it could surface results across team boundaries if not properly scoped.
No data minimisation or scoping controls. We're exposing transcripts, CRM fields, contact details with job titles, deal context - significant PII and commercial data leaving the platform. Can customers configure which data types are exposed? Can transcripts be PII-stripped before they leave Jiminny? Given we position GDPR consent as a differentiator, we should be thinking about this.
GDPR downstream processing is unaddressed. Piping transcripts to OpenAI/Gemini introduces new data processors. Customers need to acknowledge responsibility for downstream processing during setup. We should also consider whether call participants' recording consent covers onward sharing to third-party AI - it almost certainly doesn't by default. This is one for me to pick up.
Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users.
Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns.
Audit logs should go deeper. Current spec (what data, which system, when) is a start. Add: identity behind the key, specific queries/filters used, and failed access attempts (especially cross-visibility-boundary attempts). Enterprise compliance teams will expect this.
Downgrade handling. Doc says Scale tier and above, but doesn't say what happens on downgrade. Keys should auto-deactivate.
Happy to help draft a dedicated Security & Compliance section i...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST [URL_WITH_CREDENTIALS] Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.
You said
Jiminny MCP Connector
Epic
Link to Epik in Jira
Document status
DRAFT
Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.
👤 Target user
Revenue teams using AI tools (Sales, CS, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
Build an AI Knowledge Connector (MCP-based) that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides structured insights (summaries, action items, scoring etc.)
Supports deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
Metric
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of MCP/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
User interaction and design
Simple setup flow:
Generate API key / connect MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus: usable by non-technical users
Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
Importance
Notes
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH
For each deal expose:
calls/meetings/emails
Identified risks
contacts related to the deal with their job title
linked account details
all CRM field information that we have in DI + fields used for CRM filling
example - get_deal_summary
request -
{
"tool": "get_deal_summary",
"arguments": {
"deal_name": "ACME"
}
}
responce:
{
"deal_name": "ACME",
"stage": "Negotiation",
"recent_activity": [
"Demo call on 2026-04-01",
"Pricing discussion on 2026-04-03"
],
"risks": [
"Pricing concerns",
"Security review pending"
], "next_steps": [
"Send follow-up to VP Sales",
"Share security documentation"
]
}
Ensure the AI can easily understand the schema of the data coming from Jiminny
HIGH
Models perform much better when tools have:
clear names
rich descriptions
predictable schemas
Provide clear, actionable error states for customers when MCP connections fail
HIGH
Error messages should guide agents toward solutions with specific suggestions and next steps.
Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”
Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”
Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”
Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”
Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”
Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”
No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”
Allow customers to securely control who and what data is accessible externally
HIGH
Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well.
Private meetings shouldn’t be exposed via MCP
HIGH
When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP.
Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows
HIGH
Ensure data is structured to be easily combined with:
CRM (accounts, deals)
external docs
Use consistent identifiers:
account IDs
deal IDs
MCP is available only for Scale tier and above
HIGH
Customers who are on Capture tier should be able to access any of their data through MCP.
This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working.
Provide visibility into how external AI tools access Jiminny data
HIGH
Audit logs:
what data was accessed by which external system when
identity - who made the request
specific queries/filters used
failed access attempts (especially cross-visibility-boundary attempts)
Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it.
example from Atlassian -
Provide customers with example prompts to get started quickly
HIGH
MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments.
See examples in table below.
Expose Jiminny as a Connector in Claude
medium
Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny
Provide customers with example workflows to get started quickly
medium
A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.
See examples in table below.
Allow customers to use predefined Jiminny skills for common workflows
medium
Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.
Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com
See examples in table below.
Jiminny UI widget for OpenAI
medium
The reason to have this is to make certain Jiminny use cases much easier than pure chat.
Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.
For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.
Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.
Allow customers to run long-running analyses asynchronously and retrieve progress/results
low
Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to:
start the job
let it run in the background
return a status like “in progress”
let the client come back for the result later
For example - User asks in Claude:
“Analyse all calls from this quarter and tell me the top 5 objections by segment.”
That might require:
searching lots of calls
pulling many summaries/transcripts
aggregating results
Instead of making Claude wait 45+ seconds, Jiminny could:
create an analysis job
return: job_id = 123, status = running
We need this in order to support:
large-scale analysis
multi-call aggregation across lots of data
playlist generation across many calls
heavy recap/report generation
Creating a snippet through the MCP
low
Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team.
example request:
{
"tool": "create_snippet",
"arguments": {
"call_id": "call_123",
"start_time": "00:18:24",
"end_time": "00:19:10",
"title": "Pricing objection"
}
}
example response:
{
"snippet_id": "snip_987",
"title": "Pricing objection",
"share_url": "https://app.jiminny.com/snippets/snip_987"
}
Ability to add recordings or snippets to a playlist
low
Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes
Allow AI tools to read reusable Jiminny resources
low
fathom
Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be:
current deal snapshot
account summary
weekly rep recap
approved talk tracks / objection handling guidance
curated playlists or snippet libraries
This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls.
Ability to write coaching notes in Jiminny through MCP
low
What we want to do for prompts/worflows and skills
Capability
Type
User value / job to be done
Example Jiminny use case
Inputs
Steps performed
Output
Draft follow-up email
Prompt
Help reps quickly create tailored follow-ups after calls
Use the latest customer call to draft an email with recap and next steps
Call ID
optional tone
optional recipient type
Retrieve call summary and action items from Jiminny.
Pull key points, commitments, and next steps.
Apply the chosen tone and structure.
Draft the email in a consistent format.
Customer-ready follow-up email draft
Summarise call
Prompt
Help users quickly extract the most important points from a conversation
Generate a concise summary of one call for internal or external use
Call ID
optional summary style
optional audience
Retrieve transcript, metadata, and structured insights.
Identify key topics, decisions, risks, and next steps.
Format output for the chosen audience.
Short or structured call summary with actions and risks
Generate deal recap
Prompt
Help reps and managers get a fast overview of a deal
Summarise the current state of a deal based on recent calls and linked CRM context
Deal ID or account name
optional date range
Retrieve calls and meetings linked to the deal.
Pull deal metadata and CRM-linked context.
Identify recent changes, blockers, risks, and next steps.
Summarise into a concise recap.
Deal recap with status, blockers, risks, and recommended next steps
Prepare weekly recap
Prompt
Help managers or reps review what changed over a time period
Weekly recap of calls, themes, and actions for one rep, team, or account
Rep ID
team ID or account ID
time period
Retrieve relevant calls and summaries for the selected time period.
Aggregate themes, action items, and changes.
Highlight notable risks, opportunities, and follow-ups.
Weekly recap with top themes, risks, opportunities, and actions
Prepare deal review
Workflow
Help reps and managers review deal status using multiple sources of context
Review a deal before pipeline review or forecast meeting
Deal ID
optional date range
Retrieve all recent calls, emails, and meetings tied to the deal.
Pull structured call summaries, score, action items, and key points.
Pull linked CRM context such as stage, contacts, and account details.
Aggregate open risks, unresolved objections, stakeholder gaps, and next steps.
Format into a review-ready structure.
Deal review document with current status, risks, stakeholders, blockers, and next steps
Weekly team recap
Workflow
Help managers monitor activity and risk across a group of reps or accounts
End-of-week team summary for sales leadership
Team ID
date range
Retrieve calls and structured insights across the team.
Group by rep, account, or deal.
Identify recurring themes, major risks, strong calls, and missed follow-ups.
Highlight calls or deals that need attention.
Team-level recap with patterns, key risks, notable wins, and action areas
Objection analysis across calls
Workflow
Help teams identify patterns across multiple conversations
Analyse pricing or competitor objections across recent calls
Account ID
deal ID
rep ID
keyword/topic
date range
Search calls matching the topic or keyword.
Retrieve summaries, transcripts, and snippets where relevant.
Identify recurring objections and how they were handled.
Assess whether objections were resolved or remain open.
Summarise patterns and examples.
Objection analysis with themes, frequency, examples, and suggested follow-up actions
Build onboarding playlist
Workflow
Help enablement teams curate useful learning content faster
Build a playlist of strong discovery, demo, or pricing calls for new hires
Topic
call type
rep/team filter
optional time period
Search for calls matching the selected criteria.
Rank or filter calls based on score, topics, or key moments.
Identify useful snippets or full recordings.
Add selected calls/snippets into a playlist structure.
Generate short descriptions for each item.
Curated onboarding playlist with selected calls/snippets and descriptions
Deal Inspection Skill
Skill
Give users a consistent, opinionated way to assess a deal
Claude reviews a deal using a repeatable Jiminny methodology
Deal ID or account name
optional review scope
Determine whether the request is suitable for a deal inspection.
Retrieve relevant calls, summaries, CRM context, stakeholders, and risks.
Apply a standard Jiminny review framework to assess deal health.
Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs.
Present findings in a consistent structure with recommended actions.
Structured deal inspection with health assessment, red flags, and recommendations
Manager Coaching Skill
Skill
Help managers review calls consistently and identify coaching moments
Analyse a rep’s call and surface strengths, gaps, and examples for coaching
Call ID
optional rep ID
optional coaching focus
Retrieve transcript, score, summary, and key moments from the call.
Evaluate the conversation against a coaching framework.
Identify what went well and where the rep could improve.
Suggest moments to clip as snippets.
Recommend coaching actions or playlist additions.
Coaching review with strengths, gaps, snippet suggestions, and recommended coaching actions
CSM / QBR Prep Skill
Skill
Help CS or AM teams prepare structured customer reviews from conversations
Generate an account review or QBR-style summary from recent customer interactions
Account ID, optional time period
Retrieve all recent calls, emails, and account-linked context.
Aggregate themes, requests, blockers, sentiment shifts, and opportunities.
Apply a repeatable account review structure.
Summarise outcomes, open issues, and suggested next steps.
Structured QBR/account review summary with themes, risks, opportunities, and actions
Snippet Builder Skill
Skill
Help users consistently capture and label useful moments from calls
Identify useful moments in a call and convert them into reusable snippets
Call ID, optional focus area such as pricing, objection, competitor, discovery
Retrieve transcript, recording timeline, and call insights.
Detect moments matching the requested focus area.
Propose snippet start/end times and titles.
Tag snippets by type or theme.
Optionally save them or add them to a playlist.
One or more proposed or saved snippets with title, timing, and tags
💡 Technical details
Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate.
Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users.
Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns.
Open Questions
Track unresolved issues or decisions that need to be made. Update as answers become available.
Question
Answer
Date Answered
Competitor research
Competitor
MCP Availability
Core Capabilities
Advanced Capabilities
Positioning
Key Strength
Key Weakness
Gong
✅ Mature MCP
List/search calls
Get transcripts
get Call metadata
Deal intelligence
Scorecards, analytics
CRM context
Enterprise data layer
Deep data + ecosystem integrations
Limited opinionated AI workflows
Avoma
✅ Mature MCP
list_meetings
get_meeting
get_transcript
key topics
Deal fields/Deal level analysis
Coaching/Scoring
CRM context
pushing insights into Notion / Confluence
AI-ready meeting intelligence
Strongest “outputs” layer
Mostly pull-based interactions
Fathom
✅ MCP
list/search meetings
get transcript
get summary/action items
Team/org context
Webhooks (event-based triggers)
get_weekly_recap
aggregated action items across meetings
creating webhooks directly from the MCP server
Lightweight meeting data access
Includes summaries + actions
Limited analytics depth
Grain
✅ Basic MCP
get meetings
download transcripts
Notes
Deals
Scorecards
Data access utility
Simple + accessible
Low sophistication, sometimes browser-based
Outreach
⚠️ MCP (workflow-focused)
Search sequences
Emails
Meetings (Kaia)
account / prospect / opportunity lookups
Workflow automation
Multi-system orchestration
Revenue workflow engine
End-to-end GTM workflows
Less depth in conversation data
Attention
⚠️ Emerging MCP
Meeting data
Transcripts
CRM-linked context
AI insights
Sales assistant workflows
create snippets
AI sales assistant + CRM
Strong CRM linkage
Less focus on external knowledge layer
Planhat -
Related content
Ask Jiminny Anything on Call level
Galya Dimitrova
Hubspot app
Galya Dimitrova
Product Vision & Strategy
Galya Dimitrova
Automated CRM Filling
Simona Georgieva (Deactivated)
Competitive analysis
Galya Dimitrova
AI agent ideas
Steliyan Georgiev
Comments
James Graham
9 Apr
A few areas around Security & Compliance that need more depth before this moves beyond draft:
Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate.
Team visibility enforcement should be its own HIGH requirement. The current note under access control ("ensure MCP only exposes data user has access to") is doing too much work as a single line. Every MCP tool response (especially search_calls and deal-level aggregations) must be filtered through the same visibility engine as the UI. Semantic search is the riskiest here; it could surface results across team boundaries if not properly scoped.
No data minimisation or scoping controls. We're exposing transcripts, CRM fields, contact details with job titles, deal context - significant PII and commercial data leaving the platform. Can customers configure which data types are exposed? Can transcripts be PII-stripped before they leave Jiminny? Given we position GDPR consent as a differentiator, we should be thinking about this.
GDPR downstream processing is unaddressed. Piping transcripts to OpenAI/Gemini introduces new data processors. Customers need to acknowledge responsibility for downstream processing during setup. We should also consider whether call participants' recording consent covers onward sharing to third-party AI - it almost certainly doesn't by default. This is one for me to pick up.
Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users.
Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns.
Audit logs should go deeper. Current spec (what data, which system, when) is a start. Add: identity behind the key, specific queries/filters used, and failed access attempts (especially cross-visibility-boundary attempts). Enterprise compliance teams will expect this.
Downgrade handling. Doc says Scale tier and above, but doesn't say what happens on downgrade. Keys should auto-deactivate.
Happy to help draft a dedicated Security & Compliance section i...
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Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product. 👤 Target user Revenue teams using AI tools (Sales, CS, RevOps) Companies already experimenting with Claude / OpenAI Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc) 🤕 Pain point or problem Jiminny data is locked in the platform AI tools lack access to high-value conversation context Customers must manually export/copy transcripts No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people Peer pressure as almost all competitors have this 💥 Impact and benefits For customers Better AI outputs using real customer conversations Ability to build automated workflows across tools For Jiminny Increased product stickiness (embedded in workflows) Competitive parity with Gong / Avoma etc. 💡 Solution ideas Build an AI Knowledge Connector (MCP-based) that: Exposes Jiminny data (calls, transcripts, metadata) to AI tools Provides structured insights (summaries, action items, scoring etc.) Supports deal-level context Allows integration into customer-owned AI stacks (Claude + Zapier, etc.) Success metrics List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets). Goal Metric Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows % of customers connecting external AI tools Number of MCP/API calls per customer - and which ones are being used the most Decrease the manual copying of transcripts decrease by X % User interaction and design Simple setup flow: Generate API key / connect MCP Clear instructions for Claude / Zapier setup Provide: Example prompts for Claude Pre-built workflow templates for Zapier Clear documentation - with use cases 👉 Focus: usable by non-technical users Detailed Requirements List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification. Requirement Importance Notes Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI) HIGH Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack Allow customers to access call data through AI tools HIGH Expose Jiminny data: list calls (filter by date, user, account, deal) get call get transcript (speaker-separated) get call metadata search calls (keyword + semantic) Support filtering by: account / contact/ lead / deal jiminny user date range Allow customers to search and query calls using AI tools HIGH Support keyword and semantic search across conversations Provide structured insights so customers can generate better outputs HIGH For each call, expose: Summary Action items Key points Activity type Notes Call Score CRM information Allow customers to understand deal status across multiple calls/emails HIGH For each deal expose: calls/meetings/emails Identified risks contacts related to the deal with their job title linked account details all CRM field information that we have in DI + fields used for CRM filling example - get_deal_summary request - { \"tool\": \"get_deal_summary\", \"arguments\": { \"deal_name\": \"ACME\" } } responce: { \"deal_name\": \"ACME\", \"stage\": \"Negotiation\", \"recent_activity\": [ \"Demo call on 2026-04-01\", \"Pricing discussion on 2026-04-03\" ], \"risks\": [ \"Pricing concerns\", \"Security review pending\" ], \"next_steps\": [ \"Send follow-up to VP Sales\", \"Share security documentation\" ] } Ensure the AI can easily understand the schema of the data coming from Jiminny HIGH Models perform much better when tools have: clear names rich descriptions predictable schemas Provide clear, actionable error states for customers when MCP connections fail HIGH Error messages should guide agents toward solutions with specific suggestions and next steps. Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.” Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.” Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.” Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.” Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.” Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.” No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.” Allow customers to securely control who and what data is accessible externally HIGH Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well. Private meetings shouldn’t be exposed via MCP HIGH When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP. Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows HIGH Ensure data is structured to be easily combined with: CRM (accounts, deals) external docs Use consistent identifiers: account IDs deal IDs MCP is available only for Scale tier and above HIGH Customers who are on Capture tier should be able to access any of their data through MCP. This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working. Provide visibility into how external AI tools access Jiminny data HIGH Audit logs: what data was accessed by which external system when identity - who made the request specific queries/filters used failed access attempts (especially cross-visibility-boundary attempts) Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it. example from Atlassian - Provide customers with example prompts to get started quickly HIGH MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments. See examples in table below. Expose Jiminny as a Connector in Claude medium Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny Provide customers with example workflows to get started quickly medium A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome. See examples in table below. Allow customers to use predefined Jiminny skills for common workflows medium Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job. Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com See examples in table below. Jiminny UI widget for OpenAI medium The reason to have this is to make certain Jiminny use cases much easier than pure chat. Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it. For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface. Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section. Allow customers to run long-running analyses asynchronously and retrieve progress/results low Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to: start the job let it run in the background return a status like “in progress” let the client come back for the result later For example - User asks in Claude: “Analyse all calls from this quarter and tell me the top 5 objections by segment.” That might require: searching lots of calls pulling many summaries/transcripts aggregating results Instead of making Claude wait 45+ seconds, Jiminny could: create an analysis job return: job_id = 123, status = running We need this in order to support: large-scale analysis multi-call aggregation across lots of data playlist generation across many calls heavy recap/report generation Creating a snippet through the MCP low Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team. example request: { \"tool\": \"create_snippet\", \"arguments\": { \"call_id\": \"call_123\", \"start_time\": \"00:18:24\", \"end_time\": \"00:19:10\", \"title\": \"Pricing objection\" } } example response: { \"snippet_id\": \"snip_987\", \"title\": \"Pricing objection\", \"share_url\": \"https://app.jiminny.com/snippets/snip_987\" } Ability to add recordings or snippets to a playlist low Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes Allow AI tools to read reusable Jiminny resources low fathom Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be: current deal snapshot account summary weekly rep recap approved talk tracks / objection handling guidance curated playlists or snippet libraries This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls. Ability to write coaching notes in Jiminny through MCP low What we want to do for prompts/worflows and skills Capability Type User value / job to be done Example Jiminny use case Inputs Steps performed Output Draft follow-up email Prompt Help reps quickly create tailored follow-ups after calls Use the latest customer call to draft an email with recap and next steps Call ID optional tone optional recipient type Retrieve call summary and action items from Jiminny. Pull key points, commitments, and next steps. Apply the chosen tone and structure. Draft the email in a consistent format. Customer-ready follow-up email draft Summarise call Prompt Help users quickly extract the most important points from a conversation Generate a concise summary of one call for internal or external use Call ID optional summary style optional audience Retrieve transcript, metadata, and structured insights. Identify key topics, decisions, risks, and next steps. Format output for the chosen audience. Short or structured call summary with actions and risks Generate deal recap Prompt Help reps and managers get a fast overview of a deal Summarise the current state of a deal based on recent calls and linked CRM context Deal ID or account name optional date range Retrieve calls and meetings linked to the deal. Pull deal metadata and CRM-linked context. Identify recent changes, blockers, risks, and next steps. Summarise into a concise recap. Deal recap with status, blockers, risks, and recommended next steps Prepare weekly recap Prompt Help managers or reps review what changed over a time period Weekly recap of calls, themes, and actions for one rep, team, or account Rep ID team ID or account ID time period Retrieve relevant calls and summaries for the selected time period. Aggregate themes, action items, and changes. Highlight notable risks, opportunities, and follow-ups. Weekly recap with top themes, risks, opportunities, and actions Prepare deal review Workflow Help reps and managers review deal status using multiple sources of context Review a deal before pipeline review or forecast meeting Deal ID optional date range Retrieve all recent calls, emails, and meetings tied to the deal. Pull structured call summaries, score, action items, and key points. Pull linked CRM context such as stage, contacts, and account details. Aggregate open risks, unresolved objections, stakeholder gaps, and next steps. Format into a review-ready structure. Deal review document with current status, risks, stakeholders, blockers, and next steps Weekly team recap Workflow Help managers monitor activity and risk across a group of reps or accounts End-of-week team summary for sales leadership Team ID date range Retrieve calls and structured insights across the team. Group by rep, account, or deal. Identify recurring themes, major risks, strong calls, and missed follow-ups. Highlight calls or deals that need attention. Team-level recap with patterns, key risks, notable wins, and action areas Objection analysis across calls Workflow Help teams identify patterns across multiple conversations Analyse pricing or competitor objections across recent calls Account ID deal ID rep ID keyword/topic date range Search calls matching the topic or keyword. Retrieve summaries, transcripts, and snippets where relevant. Identify recurring objections and how they were handled. Assess whether objections were resolved or remain open. Summarise patterns and examples. Objection analysis with themes, frequency, examples, and suggested follow-up actions Build onboarding playlist Workflow Help enablement teams curate useful learning content faster Build a playlist of strong discovery, demo, or pricing calls for new hires Topic call type rep/team filter optional time period Search for calls matching the selected criteria. Rank or filter calls based on score, topics, or key moments. Identify useful snippets or full recordings. Add selected calls/snippets into a playlist structure. Generate short descriptions for each item. Curated onboarding playlist with selected calls/snippets and descriptions Deal Inspection Skill Skill Give users a consistent, opinionated way to assess a deal Claude reviews a deal using a repeatable Jiminny methodology Deal ID or account name optional review scope Determine whether the request is suitable for a deal inspection. Retrieve relevant calls, summaries, CRM context, stakeholders, and risks. Apply a standard Jiminny review framework to assess deal health. Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs. Present findings in a consistent structure with recommended actions. Structured deal inspection with health assessment, red flags, and recommendations Manager Coaching Skill Skill Help managers review calls consistently and identify coaching moments Analyse a rep’s call and surface strengths, gaps, and examples for coaching Call ID optional rep ID optional coaching focus Retrieve transcript, score, summary, and key moments from the call. Evaluate the conversation against a coaching framework. Identify what went well and where the rep could improve. Suggest moments to clip as snippets. Recommend coaching actions or playlist additions. Coaching review with strengths, gaps, snippet suggestions, and recommended coaching actions CSM / QBR Prep Skill Skill Help CS or AM teams prepare structured customer reviews from conversations Generate an account review or QBR-style summary from recent customer interactions Account ID, optional time period Retrieve all recent calls, emails, and account-linked context. Aggregate themes, requests, blockers, sentiment shifts, and opportunities. Apply a repeatable account review structure. Summarise outcomes, open issues, and suggested next steps. Structured QBR/account review summary with themes, risks, opportunities, and actions Snippet Builder Skill Skill Help users consistently capture and label useful moments from calls Identify useful moments in a call and convert them into reusable snippets Call ID, optional focus area such as pricing, objection, competitor, discovery Retrieve transcript, recording timeline, and call insights. Detect moments matching the requested focus area. Propose snippet start/end times and titles. Tag snippets by type or theme. Optionally save them or add them to a playlist. One or more proposed or saved snippets with title, timing, and tags 💡 Technical details Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate. Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users. Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns. Open Questions Track unresolved issues or decisions that need to be made. Update as answers become available. Question Answer Date Answered Competitor research Competitor MCP Availability Core Capabilities Advanced Capabilities Positioning Key Strength Key Weakness Gong ✅ Mature MCP List/search calls Get transcripts get Call metadata Deal intelligence Scorecards, analytics CRM context Enterprise data layer Deep data + ecosystem integrations Limited opinionated AI workflows Avoma ✅ Mature MCP list_meetings get_meeting get_transcript key topics Deal fields/Deal level analysis Coaching/Scoring CRM context pushing insights into Notion / Confluence AI-ready meeting intelligence Strongest “outputs” layer Mostly pull-based interactions Fathom ✅ MCP list/search meetings get transcript get summary/action items Team/org context Webhooks (event-based triggers) get_weekly_recap aggregated action items across meetings creating webhooks directly from the MCP server Lightweight meeting data access Includes summaries + actions Limited analytics depth Grain ✅ Basic MCP get meetings download transcripts Notes Deals Scorecards Data access utility Simple + accessible Low sophistication, sometimes browser-based Outreach ⚠️ MCP (workflow-focused) Search sequences Emails Meetings (Kaia) account / prospect / opportunity lookups Workflow automation Multi-system orchestration Revenue workflow engine End-to-end GTM workflows Less depth in conversation data Attention ⚠️ Emerging MCP Meeting data Transcripts CRM-linked context AI insights Sales assistant workflows create snippets AI sales assistant + CRM Strong CRM linkage Less focus on external knowledge layer Planhat - Related content Ask Jiminny Anything on Call level Galya Dimitrova Hubspot app Galya Dimitrova Product Vision & Strategy Galya Dimitrova Automated CRM Filling Simona Georgieva (Deactivated) Competitive analysis Galya Dimitrova AI agent ideas Steliyan Georgiev Comments James Graham 9 Apr A few areas around Security & Compliance that need more depth before this moves beyond draft: Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate. Team visibility enforcement should be its own HIGH requirement. The current note under access control (\"ensure MCP only exposes data user has access to\") is doing too much work as a single line. Every MCP tool response (especially search_calls and deal-level aggregations) must be filtered through the same visibility engine as the UI. Semantic search is the riskiest here; it could surface results across team boundaries if not properly scoped. No data minimisation or scoping controls. We're exposing transcripts, CRM fields, contact details with job titles, deal context - significant PII and commercial data leaving the platform. Can customers configure which data types are exposed? Can transcripts be PII-stripped before they leave Jiminny? Given we position GDPR consent as a differentiator, we should be thinking about this. GDPR downstream processing is unaddressed. Piping transcripts to OpenAI/Gemini introduces new data processors. Customers need to acknowledge responsibility for downstream processing during setup. We should also consider whether call participants' recording consent covers onward sharing to third-party AI - it almost certainly doesn't by default. This is one for me to pick up. Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users. Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns. Audit logs should go deeper. Current spec (what data, which system, when) is a start. Add: identity behind the key, specific queries/filters used, and failed access attempts (especially cross-visibility-boundary attempts). Enterprise compliance teams will expect this. Downgrade handling. Doc says Scale tier and above, but doesn't say what happens on downgrade. Keys should auto-deactivate. Happy to help draft a dedicated Security & Compliance section if useful. Galya Dimitrova 15 Apr Thanks for the input @James Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. 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each deal expose:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"calls/meetings/emails","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Identified risks","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"contacts related to the deal with their job title","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"linked account details","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"all CRM field information that we have in DI + fields used for CRM filling","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"example - get_deal_summary","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"request 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The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Trying to access private meeting - “Error: Access denied. 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This is not a data or permissions issue — please retry in a few minutes.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to securely control who and what data is accessible externally","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ensure MCP only exposes data user has access to - based on team visibility settings. 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This will make it easier for customers to find us and to connect Jiminny","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide customers with example workflows to get started quickly","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"See examples in table below.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to use predefined Jiminny skills for common workflows","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"See examples in table below.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny UI widget for OpenAI","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The reason to have this is to make certain Jiminny use cases much easier than pure chat.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to run long-running analyses asynchronously and retrieve progress/results","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Some requests/questions require analysis of Hugh volumes of data. 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Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"example request:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"{","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"tool\": \"create_snippet\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"arguments\": {","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"call_id\": \"call_123\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"start_time\": \"00:18:24\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"end_time\": \"00:19:10\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"title\": \"Pricing objection\"","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"example response:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"{","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"snippet_id\": \"snip_987\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"title\": \"Pricing objection\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"share_url\": \"https://app.jiminny.com/snippets/snip_987\"","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ability to add recordings or snippets to a playlist","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow AI tools to read reusable Jiminny resources","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"fathom","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Resources are useful for data that should be read consistently rather than recomputed every time. 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calls.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ability to write coaching notes in Jiminny through MCP","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"What we want to do for prompts/worflows and skills","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Capability","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Type","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"User value / job to be done","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Example Jiminny use 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draft:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Team visibility enforcement should be its own HIGH requirement. The current note under access control (\"ensure MCP only exposes data user has access to\") is doing too much work as a single line. Every MCP tool response (especially search_calls and deal-level aggregations) must be filtered through the same visibility engine as the UI. Semantic search is the riskiest here; it could surface results across team boundaries if not properly scoped.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"No data minimisation or scoping controls. We're exposing transcripts, CRM fields, contact details with job titles, deal context - significant PII and commercial data leaving the platform. Can customers configure which data types are exposed? Can transcripts be PII-stripped before they leave Jiminny? Given we position GDPR consent as a differentiator, we should be thinking about this.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"GDPR downstream processing is unaddressed. Piping transcripts to OpenAI/Gemini introduces new data processors. Customers need to acknowledge responsibility for downstream processing during setup. We should also consider whether call participants' recording consent covers onward sharing to third-party AI - it almost certainly doesn't by default. This is one for me to pick up.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Audit logs should go deeper. Current spec (what data, which system, when) is a start. Add: identity behind the key, specific queries/filters used, and failed access attempts (especially cross-visibility-boundary attempts). Enterprise compliance teams will expect this.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Downgrade handling. Doc says Scale tier and above, but doesn't say what happens on downgrade. Keys should auto-deactivate.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Happy to help draft a dedicated Security & Compliance section if useful.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Galya Dimitrova","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"15 Apr","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Thanks for the input @James Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Expand","depth":21,"role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXButton","text":"Listen","depth":21,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXButton","text":"Show more options","depth":20,"role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Show thinking","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"Gemini said","depth":20,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Gemini said","depth":21,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Thank you for sharing the draft PRD for the","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny MCP Connector","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":". It looks like a highly strategic initiative to transition Jiminny from a standalone platform into a foundational data layer for AI-driven revenue workflows.","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Here is a quick breakdown of the document as it currently stands:","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"Executive Summary","depth":23,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Executive Summary","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Core Objective:","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Build an MCP-based AI Knowledge Connector to expose Jiminny data (transcripts, summaries, deal context) to external AI tools like Claude, OpenAI, and Gemini.","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Target Value:","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Increases product stickiness, reduces manual data entry/copy-pasting, and achieves competitive parity with platforms like Gong and Avoma.","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Key Capabilities:","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"* Call and deal-level data retrieval.","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Semantic and keyword search.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Pre-built prompts, workflows, and skills (e.g., deal inspection, objection analysis).","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Current Bottlenecks & Discussions:","depth":26,"bounds":{"left":0.23159721,"top":0.0,"width":0.19097222,"height":0.022777777},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"James Graham has flagged several critical security and compliance considerations (auth modeling, data minimization, key lifecycle, rate limiting, and GDPR downstream processing). Galya has integrated some of these into the technical requirements but pushed back on the GDPR point, noting that customers already export this data manually or via standard APIs.","depth":26,"bounds":{"left":0.23159721,"top":0.0,"width":0.21875,"height":0.31166667},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Since you shared the document without a specific prompt, how would you like to proceed—would you like help drafting a response to Galya's GDPR question, refining the technical requirements based on James's feedback, or summarizing this into a pitch for stakeholders?","depth":24,"bounds":{"left":0.20729166,"top":0.25111112,"width":0.2375,"height":0.16722222},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXTextArea","text":"Enter a prompt for Gemini\nencrypted","depth":20,"value":"Enter a prompt for Gemini\nencrypted","help_text":"","role_description":"text entry area","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Enter a prompt for Gemini","depth":21,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"encrypted","depth":21,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Open upload file menu","depth":20,"role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Tools","depth":18,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Open mode picker","depth":20,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false}]...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST [URL_WITH_CREDENTIALS] Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.
You said
Jiminny MCP Connector
Epic
Link to Epik in Jira
Document status
DRAFT
Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.
👤 Target user
Revenue teams using AI tools (Sales, CS, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
Build an AI Knowledge Connector (MCP-based) that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides structured insights (summaries, action items, scoring etc.)
Supports deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
Metric
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of MCP/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
User interaction and design
Simple setup flow:
Generate API key / connect MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus: usable by non-technical users
Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
Importance
Notes
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH
For each deal expose:
calls/meetings/emails
Identified risks
contacts related to the deal with their job title
linked account details
all CRM field information that we have in DI + fields used for CRM filling
example - get_deal_summary
request -
{
"tool": "get_deal_summary",
"arguments": {
"deal_name": "ACME"
}
}
responce:
{
"deal_name": "ACME",
"stage": "Negotiation",
"recent_activity": [
"Demo call on 2026-04-01",
"Pricing discussion on 2026-04-03"
],
"risks": [
"Pricing concerns",
"Security review pending"
], "next_steps": [
"Send follow-up to VP Sales",
"Share security documentation"
]
}
Ensure the AI can easily understand the schema of the data coming from Jiminny
HIGH
Models perform much better when tools have:
clear names
rich descriptions
predictable schemas
Provide clear, actionable error states for customers when MCP connections fail
HIGH
Error messages should guide agents toward solutions with specific suggestions and next steps.
Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”
Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”
Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”
Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”
Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”
Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”
No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”
Allow customers to securely control who and what data is accessible externally
HIGH
Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well.
Private meetings shouldn’t be exposed via MCP
HIGH
When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP.
Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows
HIGH
Ensure data is structured to be easily combined with:
CRM (accounts, deals)
external docs
Use consistent identifiers:
account IDs
deal IDs
MCP is available only for Scale tier and above
HIGH
Customers who are on Capture tier should be able to access any of their data through MCP.
This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working.
Provide visibility into how external AI tools access Jiminny data
HIGH
Audit logs:
what data was accessed by which external system when
identity - who made the request
specific queries/filters used
failed access attempts (especially cross-visibility-boundary attempts)
Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it.
example from Atlassian -
Provide customers with example prompts to get started quickly
HIGH
MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments.
See examples in table below.
Expose Jiminny as a Connector in Claude
medium
Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny
Provide customers with example workflows to get started quickly
medium
A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.
See examples in table below.
Allow customers to use predefined Jiminny skills for common workflows
medium
Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.
Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com
See examples in table below.
Jiminny UI widget for OpenAI
medium
The reason to have this is to make certain Jiminny use cases much easier than pure chat.
Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.
For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.
Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.
Allow customers to run long-running analyses asynchronously and retrieve progress/results
low
Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to:
start the job
let it run in the background
return a status like “in progress”
let the client come back for the result later
For example - User asks in Claude:
“Analyse all calls from this quarter and tell me the top 5 objections by segment.”
That might require:
searching lots of calls
pulling many summaries/transcripts
aggregating results
Instead of making Claude wait 45+ seconds, Jiminny could:
create an analysis job
return: job_id = 123, status = running
We need this in order to support:
large-scale analysis
multi-call aggregation across lots of data
playlist generation across many calls
heavy recap/report generation
Creating a snippet through the MCP
low
Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team.
example request:
{
"tool": "create_snippet",
"arguments": {
"call_id": "call_123",
"start_time": "00:18:24",
"end_time": "00:19:10",
"title": "Pricing objection"
}
}
example response:
{
"snippet_id": "snip_987",
"title": "Pricing objection",
"share_url": "https://app.jiminny.com/snippets/snip_987"
}
Ability to add recordings or snippets to a playlist
low
Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes
Allow AI tools to read reusable Jiminny resources
low
fathom
Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be:
current deal snapshot
account summary
weekly rep recap
approved talk tracks / objection handling guidance
curated playlists or snippet libraries
This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls.
Ability to write coaching notes in Jiminny through MCP
low
What we want to do for prompts/worflows and skills
Capability
Type
User value / job to be done
Example Jiminny use case
Inputs
Steps performed
Output
Draft follow-up email
Prompt
Help reps quickly create tailored follow-ups after calls
Use the latest customer call to draft an email with recap and next steps
Call ID
optional tone
optional recipient type
Retrieve call summary and action items from Jiminny.
Pull key points, commitments, and next steps.
Apply the chosen tone and structure.
Draft the email in a consistent format.
Customer-ready follow-up email draft
Summarise call
Prompt
Help users quickly extract the most important points from a conversation
Generate a concise summary of one call for internal or external use
Call ID
optional summary style
optional audience
Retrieve transcript, metadata, and structured insights.
Identify key topics, decisions, risks, and next steps.
Format output for the chosen audience.
Short or structured call summary with actions and risks
Generate deal recap
Prompt
Help reps and managers get a fast overview of a deal
Summarise the current state of a deal based on recent calls and linked CRM context
Deal ID or account name
optional date range
Retrieve calls and meetings linked to the deal.
Pull deal metadata and CRM-linked context.
Identify recent changes, blockers, risks, and next steps.
Summarise into a concise recap.
Deal recap with status, blockers, risks, and recommended next steps
Prepare weekly recap
Prompt
Help managers or reps review what changed over a time period
Weekly recap of calls, themes, and actions for one rep, team, or account
Rep ID
team ID or account ID
time period
Retrieve relevant calls and summaries for the selected time period.
Aggregate themes, action items, and changes.
Highlight notable risks, opportunities, and follow-ups.
Weekly recap with top themes, risks, opportunities, and actions
Prepare deal review
Workflow
Help reps and managers review deal status using multiple sources of context
Review a deal before pipeline review or forecast meeting
Deal ID
optional date range
Retrieve all recent calls, emails, and meetings tied to the deal.
Pull structured call summaries, score, action items, and key points.
Pull linked CRM context such as stage, contacts, and account details.
Aggregate open risks, unresolved objections, stakeholder gaps, and next steps.
Format into a review-ready structure.
Deal review document with current status, risks, stakeholders, blockers, and next steps
Weekly team recap
Workflow
Help managers monitor activity and risk across a group of reps or accounts
End-of-week team summary for sales leadership
Team ID
date range
Retrieve calls and structured insights across the team.
Group by rep, account, or deal.
Identify recurring themes, major risks, strong calls, and missed follow-ups.
Highlight calls or deals that need attention.
Team-level recap with patterns, key risks, notable wins, and action areas
Objection analysis across calls
Workflow
Help teams identify patterns across multiple conversations
Analyse pricing or competitor objections across recent calls
Account ID
deal ID
rep ID
keyword/topic
date range
Search calls matching the topic or keyword.
Retrieve summaries, transcripts, and snippets where relevant.
Identify recurring objections and how they were handled.
Assess whether objections were resolved or remain open.
Summarise patterns and examples.
Objection analysis with themes, frequency, examples, and suggested follow-up actions
Build onboarding playlist
Workflow
Help enablement teams curate useful learning content faster
Build a playlist of strong discovery, demo, or pricing calls for new hires
Topic
call type
rep/team filter
optional time period
Search for calls matching the selected criteria.
Rank or filter calls based on score, topics, or key moments.
Identify useful snippets or full recordings.
Add selected calls/snippets into a playlist structure.
Generate short descriptions for each item.
Curated onboarding playlist with selected calls/snippets and descriptions
Deal Inspection Skill
Skill
Give users a consistent, opinionated way to assess a deal
Claude reviews a deal using a repeatable Jiminny methodology
Deal ID or account name
optional review scope
Determine whether the request is suitable for a deal inspection.
Retrieve relevant calls, summaries, CRM context, stakeholders, and risks.
Apply a standard Jiminny review framework to assess deal health.
Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs.
Present findings in a consistent structure with recommended actions.
Structured deal inspection with health assessment, red flags, and recommendations
Manager Coaching Skill
Skill
Help managers review calls consistently and identify coaching moments
Analyse a rep’s call and surface strengths, gaps, and examples for coaching
Call ID
optional rep ID
optional coaching focus
Retrieve transcript, score, summary, and key moments from the call.
Evaluate the conversation against a coaching framework.
Identify what went well and where the rep could improve.
Suggest moments to clip as snippets.
Recommend coaching actions or playlist additions.
Coaching review with strengths, gaps, snippet suggestions, and recommended coaching actions
CSM / QBR Prep Skill
Skill
Help CS or AM teams prepare structured customer reviews from conversations
Generate an account review or QBR-style summary from recent customer interactions
Account ID, optional time period
Retrieve all recent calls, emails, and account-linked context.
Aggregate themes, requests, blockers, sentiment shifts, and opportunities.
Apply a repeatable account review structure.
Summarise outcomes, open issues, and suggested next steps.
Structured QBR/account review summary with themes, risks, opportunities, and actions
Snippet Builder Skill
Skill
Help users consistently capture and label useful moments from calls
Identify useful moments in a call and convert them into reusable snippets
Call ID, optional focus area such as pricing, objection, competitor, discovery
Retrieve transcript, recording timeline, and call insights.
Detect moments matching the requested focus area.
Propose snippet start/end times and titles.
Tag snippets by type or theme.
Optionally save them or add them to a playlist.
One or more proposed or saved snippets with title, timing, and tags
💡 Technical details
Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate.
Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users.
Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns.
Open Questions
Track unresolved issues or decisions that need to be made. Update as answers become available.
Question
Answer
Date Answered
Competitor research
Competitor
MCP Availability
Core Capabilities
Advanced Capabilities
Positioning
Key Strength
Key Weakness
Gong
✅ Mature MCP
List/search calls
Get transcripts
get Call metadata
Deal intelligence
Scorecards, analytics
CRM context
Enterprise data layer
Deep data + ecosystem integrations
Limited opinionated AI workflows
Avoma
✅ Mature MCP
list_meetings
get_meeting
get_transcript
key topics
Deal fields/Deal level analysis
Coaching/Scoring
CRM context
pushing insights into Notion / Confluence
AI-ready meeting intelligence
Strongest “outputs” layer
Mostly pull-based interactions
Fathom
✅ MCP
list/search meetings
get transcript
get summary/action items
Team/org context
Webhooks (event-based triggers)
get_weekly_recap
aggregated action items across meetings
creating webhooks directly from the MCP server
Lightweight meeting data access
Includes summaries + actions
Limited analytics depth
Grain
✅ Basic MCP
get meetings
download transcripts
Notes
Deals
Scorecards
Data access utility
Simple + accessible
Low sophistication, sometimes browser-based
Outreach
⚠️ MCP (workflow-focused)
Search sequences
Emails
Meetings (Kaia)
account / prospect / opportunity lookups
Workflow automation
Multi-system orchestration
Revenue workflow engine
End-to-end GTM workflows
Less depth in conversation data
Attention
⚠️ Emerging MCP
Meeting data
Transcripts
CRM-linked context
AI insights
Sales assistant workflows
create snippets
AI sales assistant + CRM
Strong CRM linkage
Less focus on external knowledge layer
Planhat -
Related content
Ask Jiminny Anything on Call level
Galya Dimitrova
Hubspot app
Galya Dimitrova
Product Vision & Strategy
Galya Dimitrova
Automated CRM Filling
Simona Georgieva (Deactivated)
Competitive analysis
Galya Dimitrova
AI agent ideas
Steliyan Georgiev
Comments
James Graham
9 Apr
A few areas around Security & Compliance that need more depth before this moves beyond draft:
Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate.
Team visibility enforcement should be its own HIGH requirement. The current note under access control ("ensure MCP only exposes data user has access to") is doing too much work as a single line. Every MCP tool response (especially search_calls and deal-level aggregations) must be filtered through the same visibility engine as the UI. Semantic search is the riskiest here; it could surface results across team boundaries if not properly scoped.
No data minimisation or scoping controls. We're exposing transcripts, CRM fields, contact details with job titles, deal context - significant PII and commercial data leaving the platform. Can customers configure which data types are exposed? Can transcripts be PII-stripped before they leave Jiminny? Given we position GDPR consent as a differentiator, we should be thinking about this.
GDPR downstream processing is unaddressed. Piping transcripts to OpenAI/Gemini introduces new data processors. Customers need to acknowledge responsibility for downstream processing during setup. We should also consider whether call participants' recording consent covers onward sharing to third-party AI - it almost certainly doesn't by default. This is one for me to pick up.
Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users.
Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns.
Audit logs should go deeper. Current spec (what data, which system, when) is a start. Add: identity behind the key, specific queries/filters used, and failed access attempts (especially cross-visibility-boundary attempts). Enterprise compliance teams will expect this.
Downgrade handling. Doc says Scale tier and above, but doesn't say what happens on downgrade. Keys should auto-deactivate.
Happy to help draft a dedicated Security & Compliance section i...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST [URL_WITH_CREDENTIALS] Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.
You said
Jiminny MCP Connector
Epic
Link to Epik in Jira
Document status
DRAFT
Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.
👤 Target user
Revenue teams using AI tools (Sales, CS, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
Build an AI Knowledge Connector (MCP-based) that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides structured insights (summaries, action items, scoring etc.)
Supports deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
Metric
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of MCP/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
User interaction and design
Simple setup flow:
Generate API key / connect MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus: usable by non-technical users
Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
Importance
Notes
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH
For each deal expose:
calls/meetings/emails
Identified risks
contacts related to the deal with their job title
linked account details
all CRM field information that we have in DI + fields used for CRM filling
example - get_deal_summary
request -
{
"tool": "get_deal_summary",
"arguments": {
"deal_name": "ACME"
}
}
responce:
{
"deal_name": "ACME",
"stage": "Negotiation",
"recent_activity": [
"Demo call on 2026-04-01",
"Pricing discussion on 2026-04-03"
],
"risks": [
"Pricing concerns",
"Security review pending"
], "next_steps": [
"Send follow-up to VP Sales",
"Share security documentation"
]
}
Ensure the AI can easily understand the schema of the data coming from Jiminny
HIGH
Models perform much better when tools have:
clear names
rich descriptions
predictable schemas
Provide clear, actionable error states for customers when MCP connections fail
HIGH
Error messages should guide agents toward solutions with specific suggestions and next steps.
Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”
Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”
Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”
Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”
Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”
Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”
No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”
Allow customers to securely control who and what data is accessible externally
HIGH
Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well.
Private meetings shouldn’t be exposed via MCP
HIGH
When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP.
Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows
HIGH
Ensure data is structured to be easily combined with:
CRM (accounts, deals)
external docs
Use consistent identifiers:
account IDs
deal IDs
MCP is available only for Scale tier and above
HIGH
Customers who are on Capture tier should be able to access any of their data through MCP.
This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working.
Provide visibility into how external AI tools access Jiminny data
HIGH
Audit logs:
what data was accessed by which external system when
identity - who made the request
specific queries/filters used
failed access attempts (especially cross-visibility-boundary attempts)
Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it.
example from Atlassian -
Provide customers with example prompts to get started quickly
HIGH
MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments.
See examples in table below.
Expose Jiminny as a Connector in Claude
medium
Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny
Provide customers with example workflows to get started quickly
medium
A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.
See examples in table below.
Allow customers to use predefined Jiminny skills for common workflows
medium
Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.
Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com
See examples in table below.
Jiminny UI widget for OpenAI
medium
The reason to have this is to make certain Jiminny use cases much easier than pure chat.
Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.
For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.
Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.
Allow customers to run long-running analyses asynchronously and retrieve progress/results
low
Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to:
start the job
let it run in the background
return a status like “in progress”
let the client come back for the result later
For example - User asks in Claude:
“Analyse all calls from this quarter and tell me the top 5 objections by segment.”
That might require:
searching lots of calls
pulling many summaries/transcripts
aggregating results
Instead of making Claude wait 45+ seconds, Jiminny could:
create an analysis job
return: job_id = 123, status = running
We need this in order to support:
large-scale analysis
multi-call aggregation across lots of data
playlist generation across many calls
heavy recap/report generation
Creating a snippet through the MCP
low
Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team.
example request:
{
"tool": "create_snippet",
"arguments": {
"call_id": "call_123",
"start_time": "00:18:24",
"end_time": "00:19:10",
"title": "Pricing objection"
}
}
example response:
{
"snippet_id": "snip_987",
"title": "Pricing objection",
"share_url": "https://app.jiminny.com/snippets/snip_987"
}
Ability to add recordings or snippets to a playlist
low
Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes
Allow AI tools to read reusable Jiminny resources
low
fathom
Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be:
current deal snapshot
account summary
weekly rep recap
approved talk tracks / objection handling guidance
curated playlists or snippet libraries
This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls.
Ability to write coaching notes in Jiminny through MCP
low
What we want to do for prompts/worflows and skills
Capability
Type
User value / job to be done
Example Jiminny use case
Inputs
Steps performed
Output
Draft follow-up email
Prompt
Help reps quickly create tailored follow-ups after calls
Use the latest customer call to draft an email with recap and next steps
Call ID
optional tone
optional recipient type
Retrieve call summary and action items from Jiminny.
Pull key points, commitments, and next steps.
Apply the chosen tone and structure.
Draft the email in a consistent format.
Customer-ready follow-up email draft
Summarise call
Prompt
Help users quickly extract the most important points from a conversation
Generate a concise summary of one call for internal or external use
Call ID
optional summary style
optional audience
Retrieve transcript, metadata, and structured insights.
Identify key topics, decisions, risks, and next steps.
Format output for the chosen audience.
Short or structured call summary with actions and risks
Generate deal recap
Prompt
Help reps and managers get a fast overview of a deal
Summarise the current state of a deal based on recent calls and linked CRM context
Deal ID or account name
optional date range
Retrieve calls and meetings linked to the deal.
Pull deal metadata and CRM-linked context.
Identify recent changes, blockers, risks, and next steps.
Summarise into a concise recap.
Deal recap with status, blockers, risks, and recommended next steps
Prepare weekly recap
Prompt
Help managers or reps review what changed over a time period
Weekly recap of calls, themes, and actions for one rep, team, or account
Rep ID
team ID or account ID
time period
Retrieve relevant calls and summaries for the selected time period.
Aggregate themes, action items, and changes.
Highlight notable risks, opportunities, and follow-ups.
Weekly recap with top themes, risks, opportunities, and actions
Prepare deal review
Workflow
Help reps and managers review deal status using multiple sources of context
Review a deal before pipeline review or forecast meeting
Deal ID
optional date range
Retrieve all recent calls, emails, and meetings tied to the deal.
Pull structured call summaries, score, action items, and key points.
Pull linked CRM context such as stage, contacts, and account details.
Aggregate open risks, unresolved objections, stakeholder gaps, and next steps.
Format into a review-ready structure.
Deal review document with current status, risks, stakeholders, blockers, and next steps
Weekly team recap
Workflow
Help managers monitor activity and risk across a group of reps or accounts
End-of-week team summary for sales leadership
Team ID
date range
Retrieve calls and structured insights across the team.
Group by rep, account, or deal.
Identify recurring themes, major risks, strong calls, and missed follow-ups.
Highlight calls or deals that need attention.
Team-level recap with patterns, key risks, notable wins, and action areas
Objection analysis across calls
Workflow
Help teams identify patterns across multiple conversations
Analyse pricing or competitor objections across recent calls
Account ID
deal ID
rep ID
keyword/topic
date range
Search calls matching the topic or keyword.
Retrieve summaries, transcripts, and snippets where relevant.
Identify recurring objections and how they were handled.
Assess whether objections were resolved or remain open.
Summarise patterns and examples.
Objection analysis with themes, frequency, examples, and suggested follow-up actions
Build onboarding playlist
Workflow
Help enablement teams curate useful learning content faster
Build a playlist of strong discovery, demo, or pricing calls for new hires
Topic
call type
rep/team filter
optional time period
Search for calls matching the selected criteria.
Rank or filter calls based on score, topics, or key moments.
Identify useful snippets or full recordings.
Add selected calls/snippets into a playlist structure.
Generate short descriptions for each item.
Curated onboarding playlist with selected calls/snippets and descriptions
Deal Inspection Skill
Skill
Give users a consistent, opinionated way to assess a deal
Claude reviews a deal using a repeatable Jiminny methodology
Deal ID or account name
optional review scope
Determine whether the request is suitable for a deal inspection.
Retrieve relevant calls, summaries, CRM context, stakeholders, and risks.
Apply a standard Jiminny review framework to assess deal health.
Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs.
Present findings in a consistent structure with recommended actions.
Structured deal inspection with health assessment, red flags, and recommendations
Manager Coaching Skill
Skill
Help managers review calls consistently and identify coaching moments
Analyse a rep’s call and surface strengths, gaps, and examples for coaching
Call ID
optional rep ID
optional coaching focus
Retrieve transcript, score, summary, and key moments from the call.
Evaluate the conversation against a coaching framework.
Identify what went well and where the rep could improve.
Suggest moments to clip as snippets.
Recommend coaching actions or playlist additions.
Coaching review with strengths, gaps, snippet suggestions, and recommended coaching actions
CSM / QBR Prep Skill
Skill
Help CS or AM teams prepare structured customer reviews from conversations
Generate an account review or QBR-style summary from recent customer interactions
Account ID, optional time period
Retrieve all recent calls, emails, and account-linked context.
Aggregate themes, requests, blockers, sentiment shifts, and opportunities.
Apply a repeatable account review structure.
Summarise outcomes, open issues, and suggested next steps.
Structured QBR/account review summary with themes, risks, opportunities, and actions
Snippet Builder Skill
Skill
Help users consistently capture and label useful moments from calls
Identify useful moments in a call and convert them into reusable snippets
Call ID, optional focus area such as pricing, objection, competitor, discovery
Retrieve transcript, recording timeline, and call insights.
Detect moments matching the requested focus area.
Propose snippet start/end times and titles.
Tag snippets by type or theme.
Optionally save them or add them to a playlist.
One or more proposed or saved snippets with title, timing, and tags
💡 Technical details
Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate.
Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users.
Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns.
Open Questions
Track unresolved issues or decisions that need to be made. Update as answers become available.
Question
Answer
Date Answered
Competitor research
Competitor
MCP Availability
Core Capabilities
Advanced Capabilities
Positioning
Key Strength
Key Weakness
Gong
✅ Mature MCP
List/search calls
Get transcripts
get Call metadata
Deal intelligence
Scorecards, analytics
CRM context...
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Objective Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows. Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product. 👤 Target user Revenue teams using AI tools (Sales, CS, RevOps) Companies already experimenting with Claude / OpenAI Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc) 🤕 Pain point or problem Jiminny data is locked in the platform AI tools lack access to high-value conversation context Customers must manually export/copy transcripts No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people Peer pressure as almost all competitors have this 💥 Impact and benefits For customers Better AI outputs using real customer conversations Ability to build automated workflows across tools For Jiminny Increased product stickiness (embedded in workflows) Competitive parity with Gong / Avoma etc. 💡 Solution ideas Build an AI Knowledge Connector (MCP-based) that: Exposes Jiminny data (calls, transcripts, metadata) to AI tools Provides structured insights (summaries, action items, scoring etc.) Supports deal-level context Allows integration into customer-owned AI stacks (Claude + Zapier, etc.) Success metrics List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets). Goal Metric Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows % of customers connecting external AI tools Number of MCP/API calls per customer - and which ones are being used the most Decrease the manual copying of transcripts decrease by X % User interaction and design Simple setup flow: Generate API key / connect MCP Clear instructions for Claude / Zapier setup Provide: Example prompts for Claude Pre-built workflow templates for Zapier Clear documentation - with use cases 👉 Focus: usable by non-technical users Detailed Requirements List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification. Requirement Importance Notes Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI) HIGH Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack Allow customers to access call data through AI tools HIGH Expose Jiminny data: list calls (filter by date, user, account, deal) get call get transcript (speaker-separated) get call metadata search calls (keyword + semantic) Support filtering by: account / contact/ lead / deal jiminny user date range Allow customers to search and query calls using AI tools HIGH Support keyword and semantic search across conversations Provide structured insights so customers can generate better outputs HIGH For each call, expose: Summary Action items Key points Activity type Notes Call Score CRM information Allow customers to understand deal status across multiple calls/emails HIGH For each deal expose: calls/meetings/emails Identified risks contacts related to the deal with their job title linked account details all CRM field information that we have in DI + fields used for CRM filling example - get_deal_summary request - { \"tool\": \"get_deal_summary\", \"arguments\": { \"deal_name\": \"ACME\" } } responce: { \"deal_name\": \"ACME\", \"stage\": \"Negotiation\", \"recent_activity\": [ \"Demo call on 2026-04-01\", \"Pricing discussion on 2026-04-03\" ], \"risks\": [ \"Pricing concerns\", \"Security review pending\" ], \"next_steps\": [ \"Send follow-up to VP Sales\", \"Share security documentation\" ] } Ensure the AI can easily understand the schema of the data coming from Jiminny HIGH Models perform much better when tools have: clear names rich descriptions predictable schemas Provide clear, actionable error states for customers when MCP connections fail HIGH Error messages should guide agents toward solutions with specific suggestions and next steps. Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.” Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.” Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.” Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.” Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.” Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.” No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.” Allow customers to securely control who and what data is accessible externally HIGH Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well. Private meetings shouldn’t be exposed via MCP HIGH When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP. Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows HIGH Ensure data is structured to be easily combined with: CRM (accounts, deals) external docs Use consistent identifiers: account IDs deal IDs MCP is available only for Scale tier and above HIGH Customers who are on Capture tier should be able to access any of their data through MCP. This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working. Provide visibility into how external AI tools access Jiminny data HIGH Audit logs: what data was accessed by which external system when identity - who made the request specific queries/filters used failed access attempts (especially cross-visibility-boundary attempts) Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it. example from Atlassian - Provide customers with example prompts to get started quickly HIGH MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments. See examples in table below. Expose Jiminny as a Connector in Claude medium Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny Provide customers with example workflows to get started quickly medium A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome. See examples in table below. Allow customers to use predefined Jiminny skills for common workflows medium Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job. Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com See examples in table below. Jiminny UI widget for OpenAI medium The reason to have this is to make certain Jiminny use cases much easier than pure chat. Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it. For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface. Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section. Allow customers to run long-running analyses asynchronously and retrieve progress/results low Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to: start the job let it run in the background return a status like “in progress” let the client come back for the result later For example - User asks in Claude: “Analyse all calls from this quarter and tell me the top 5 objections by segment.” That might require: searching lots of calls pulling many summaries/transcripts aggregating results Instead of making Claude wait 45+ seconds, Jiminny could: create an analysis job return: job_id = 123, status = running We need this in order to support: large-scale analysis multi-call aggregation across lots of data playlist generation across many calls heavy recap/report generation Creating a snippet through the MCP low Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team. example request: { \"tool\": \"create_snippet\", \"arguments\": { \"call_id\": \"call_123\", \"start_time\": \"00:18:24\", \"end_time\": \"00:19:10\", \"title\": \"Pricing objection\" } } example response: { \"snippet_id\": \"snip_987\", \"title\": \"Pricing objection\", \"share_url\": \"https://app.jiminny.com/snippets/snip_987\" } Ability to add recordings or snippets to a playlist low Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes Allow AI tools to read reusable Jiminny resources low fathom Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be: current deal snapshot account summary weekly rep recap approved talk tracks / objection handling guidance curated playlists or snippet libraries This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls. Ability to write coaching notes in Jiminny through MCP low What we want to do for prompts/worflows and skills Capability Type User value / job to be done Example Jiminny use case Inputs Steps performed Output Draft follow-up email Prompt Help reps quickly create tailored follow-ups after calls Use the latest customer call to draft an email with recap and next steps Call ID optional tone optional recipient type Retrieve call summary and action items from Jiminny. Pull key points, commitments, and next steps. Apply the chosen tone and structure. Draft the email in a consistent format. Customer-ready follow-up email draft Summarise call Prompt Help users quickly extract the most important points from a conversation Generate a concise summary of one call for internal or external use Call ID optional summary style optional audience Retrieve transcript, metadata, and structured insights. Identify key topics, decisions, risks, and next steps. Format output for the chosen audience. Short or structured call summary with actions and risks Generate deal recap Prompt Help reps and managers get a fast overview of a deal Summarise the current state of a deal based on recent calls and linked CRM context Deal ID or account name optional date range Retrieve calls and meetings linked to the deal. Pull deal metadata and CRM-linked context. Identify recent changes, blockers, risks, and next steps. Summarise into a concise recap. Deal recap with status, blockers, risks, and recommended next steps Prepare weekly recap Prompt Help managers or reps review what changed over a time period Weekly recap of calls, themes, and actions for one rep, team, or account Rep ID team ID or account ID time period Retrieve relevant calls and summaries for the selected time period. Aggregate themes, action items, and changes. Highlight notable risks, opportunities, and follow-ups. Weekly recap with top themes, risks, opportunities, and actions Prepare deal review Workflow Help reps and managers review deal status using multiple sources of context Review a deal before pipeline review or forecast meeting Deal ID optional date range Retrieve all recent calls, emails, and meetings tied to the deal. Pull structured call summaries, score, action items, and key points. Pull linked CRM context such as stage, contacts, and account details. Aggregate open risks, unresolved objections, stakeholder gaps, and next steps. Format into a review-ready structure. Deal review document with current status, risks, stakeholders, blockers, and next steps Weekly team recap Workflow Help managers monitor activity and risk across a group of reps or accounts End-of-week team summary for sales leadership Team ID date range Retrieve calls and structured insights across the team. Group by rep, account, or deal. Identify recurring themes, major risks, strong calls, and missed follow-ups. Highlight calls or deals that need attention. Team-level recap with patterns, key risks, notable wins, and action areas Objection analysis across calls Workflow Help teams identify patterns across multiple conversations Analyse pricing or competitor objections across recent calls Account ID deal ID rep ID keyword/topic date range Search calls matching the topic or keyword. Retrieve summaries, transcripts, and snippets where relevant. Identify recurring objections and how they were handled. Assess whether objections were resolved or remain open. Summarise patterns and examples. Objection analysis with themes, frequency, examples, and suggested follow-up actions Build onboarding playlist Workflow Help enablement teams curate useful learning content faster Build a playlist of strong discovery, demo, or pricing calls for new hires Topic call type rep/team filter optional time period Search for calls matching the selected criteria. Rank or filter calls based on score, topics, or key moments. Identify useful snippets or full recordings. Add selected calls/snippets into a playlist structure. Generate short descriptions for each item. Curated onboarding playlist with selected calls/snippets and descriptions Deal Inspection Skill Skill Give users a consistent, opinionated way to assess a deal Claude reviews a deal using a repeatable Jiminny methodology Deal ID or account name optional review scope Determine whether the request is suitable for a deal inspection. Retrieve relevant calls, summaries, CRM context, stakeholders, and risks. Apply a standard Jiminny review framework to assess deal health. Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs. Present findings in a consistent structure with recommended actions. Structured deal inspection with health assessment, red flags, and recommendations Manager Coaching Skill Skill Help managers review calls consistently and identify coaching moments Analyse a rep’s call and surface strengths, gaps, and examples for coaching Call ID optional rep ID optional coaching focus Retrieve transcript, score, summary, and key moments from the call. Evaluate the conversation against a coaching framework. Identify what went well and where the rep could improve. Suggest moments to clip as snippets. Recommend coaching actions or playlist additions. Coaching review with strengths, gaps, snippet suggestions, and recommended coaching actions CSM / QBR Prep Skill Skill Help CS or AM teams prepare structured customer reviews from conversations Generate an account review or QBR-style summary from recent customer interactions Account ID, optional time period Retrieve all recent calls, emails, and account-linked context. Aggregate themes, requests, blockers, sentiment shifts, and opportunities. Apply a repeatable account review structure. Summarise outcomes, open issues, and suggested next steps. Structured QBR/account review summary with themes, risks, opportunities, and actions Snippet Builder Skill Skill Help users consistently capture and label useful moments from calls Identify useful moments in a call and convert them into reusable snippets Call ID, optional focus area such as pricing, objection, competitor, discovery Retrieve transcript, recording timeline, and call insights. Detect moments matching the requested focus area. Propose snippet start/end times and titles. Tag snippets by type or theme. Optionally save them or add them to a playlist. One or more proposed or saved snippets with title, timing, and tags 💡 Technical details Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate. Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users. Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns. Open Questions Track unresolved issues or decisions that need to be made. Update as answers become available. Question Answer Date Answered Competitor research Competitor MCP Availability Core Capabilities Advanced Capabilities Positioning Key Strength Key Weakness Gong ✅ Mature MCP List/search calls Get transcripts get Call metadata Deal intelligence Scorecards, analytics CRM context Enterprise data layer Deep data + ecosystem integrations Limited opinionated AI workflows Avoma ✅ Mature MCP list_meetings get_meeting get_transcript key topics Deal fields/Deal level analysis Coaching/Scoring CRM context pushing insights into Notion / Confluence AI-ready meeting intelligence Strongest “outputs” layer Mostly pull-based interactions Fathom ✅ MCP list/search meetings get transcript get summary/action items Team/org context Webhooks (event-based triggers) get_weekly_recap aggregated action items across meetings creating webhooks directly from the MCP server Lightweight meeting data access Includes summaries + actions Limited analytics depth Grain ✅ Basic MCP get meetings download transcripts Notes Deals Scorecards Data access utility Simple + accessible Low sophistication, sometimes browser-based Outreach ⚠️ MCP (workflow-focused) Search sequences Emails Meetings (Kaia) account / prospect / opportunity lookups Workflow automation Multi-system orchestration Revenue workflow engine End-to-end GTM workflows Less depth in conversation data Attention ⚠️ Emerging MCP Meeting data Transcripts CRM-linked context AI insights Sales assistant workflows create snippets AI sales assistant + CRM Strong CRM linkage Less focus on external knowledge layer Planhat - Related content Ask Jiminny Anything on Call level Galya Dimitrova Hubspot app Galya Dimitrova Product Vision & Strategy Galya Dimitrova Automated CRM Filling Simona Georgieva (Deactivated) Competitive analysis Galya Dimitrova AI agent ideas Steliyan Georgiev Comments James Graham 9 Apr A few areas around Security & Compliance that need more depth before this moves beyond draft: Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate. Team visibility enforcement should be its own HIGH requirement. The current note under access control (\"ensure MCP only exposes data user has access to\") is doing too much work as a single line. Every MCP tool response (especially search_calls and deal-level aggregations) must be filtered through the same visibility engine as the UI. Semantic search is the riskiest here; it could surface results across team boundaries if not properly scoped. No data minimisation or scoping controls. We're exposing transcripts, CRM fields, contact details with job titles, deal context - significant PII and commercial data leaving the platform. Can customers configure which data types are exposed? Can transcripts be PII-stripped before they leave Jiminny? Given we position GDPR consent as a differentiator, we should be thinking about this. GDPR downstream processing is unaddressed. Piping transcripts to OpenAI/Gemini introduces new data processors. Customers need to acknowledge responsibility for downstream processing during setup. We should also consider whether call participants' recording consent covers onward sharing to third-party AI - it almost certainly doesn't by default. This is one for me to pick up. Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users. Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns. Audit logs should go deeper. Current spec (what data, which system, when) is a start. Add: identity behind the key, specific queries/filters used, and failed access attempts (especially cross-visibility-boundary attempts). Enterprise compliance teams will expect this. Downgrade handling. Doc says Scale tier and above, but doesn't say what happens on downgrade. Keys should auto-deactivate. Happy to help draft a dedicated Security & Compliance section if useful. Galya Dimitrova 15 Apr Thanks for the input @James Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.","depth":21,"bounds":{"left":0.41805187,"top":0.19313647,"width":0.051861703,"height":0.11173184},"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"You said","depth":23,"bounds":{"left":0.36053857,"top":0.19553073,"width":0.019946808,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny MCP Connector","depth":23,"bounds":{"left":0.41805187,"top":0.1963288,"width":0.03125,"height":0.038707104},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Epic","depth":23,"bounds":{"left":0.41805187,"top":0.30806065,"width":0.010305851,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Link to Epik in Jira","depth":23,"bounds":{"left":0.41805187,"top":0.33040702,"width":0.043218084,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Document status","depth":23,"bounds":{"left":0.41805187,"top":0.3527534,"width":0.04105718,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"DRAFT","depth":23,"bounds":{"left":0.41805187,"top":0.37509975,"width":0.016289894,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Objective","depth":23,"bounds":{"left":0.41805187,"top":0.39744613,"width":0.024268618,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.","depth":23,"bounds":{"left":0.41805187,"top":0.4197925,"width":0.051529255,"height":0.17278531},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.","depth":23,"bounds":{"left":0.41805187,"top":0.59856343,"width":0.049700797,"height":0.10574621},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"👤 Target user","depth":23,"bounds":{"left":0.41805187,"top":0.75458896,"width":0.033909574,"height":0.016759777},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Revenue teams using AI tools (Sales, CS, RevOps)","depth":23,"bounds":{"left":0.41805187,"top":0.7773344,"width":0.051030584,"height":0.061053474},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Companies already experimenting with Claude / OpenAI","depth":23,"bounds":{"left":0.41805187,"top":0.8443735,"width":0.04637633,"height":0.061053474},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)","depth":23,"bounds":{"left":0.41805187,"top":0.9114126,"width":0.051363032,"height":0.0885874},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"🤕 Pain point or problem","depth":23,"bounds":{"left":0.41805187,"top":1.0,"width":0.03706782,"height":-0.0897845},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny data is locked in the platform","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"AI tools lack access to high-value conversation context","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Customers must manually export/copy transcripts","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"No easy way to combine calls with CRM + docs + other info - 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-","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"{","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"tool\": \"get_deal_summary\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"arguments\": {","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"deal_name\": \"ACME\"","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"responce:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"{","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"deal_name\": \"ACME\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"stage\": \"Negotiation\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"recent_activity\": [","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"Demo call on 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The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to securely control who and what data is accessible externally","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ensure MCP only exposes data user has access to - based on team visibility settings. 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This will make it easier for customers to find us and to connect Jiminny","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide customers with example workflows to get started quickly","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"See examples in table below.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to use predefined Jiminny skills for common workflows","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"See examples in table below.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny UI widget for OpenAI","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The reason to have this is to make certain Jiminny use cases much easier than pure chat.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to run long-running analyses asynchronously and retrieve progress/results","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Some requests/questions require analysis of Hugh volumes of data. 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Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"example request:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"{","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"tool\": \"create_snippet\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"arguments\": {","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"call_id\": \"call_123\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"start_time\": \"00:18:24\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"end_time\": \"00:19:10\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"title\": \"Pricing objection\"","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"example response:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"{","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"snippet_id\": \"snip_987\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"title\": \"Pricing objection\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"share_url\": \"https://app.jiminny.com/snippets/snip_987\"","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ability to add recordings or snippets to a playlist","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to create libraries of calls automatically. 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In Jiminny terms, that could be:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"current deal snapshot","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"account summary","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"weekly rep recap","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"approved talk tracks / objection handling guidance","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"curated playlists or snippet libraries","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ability to write coaching notes in Jiminny through MCP","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"What we want to do for prompts/worflows and skills","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Capability","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Type","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"User value / job to be done","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Example Jiminny use 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calls","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Identify useful moments in a call and convert them into reusable snippets","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Call ID, optional focus area such as pricing, objection, competitor, discovery","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Retrieve transcript, recording timeline, and call insights.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Detect moments matching the requested focus area.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Propose snippet start/end times and titles.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Tag snippets by type or 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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST [URL_WITH_CREDENTIALS] Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.
You said
Jiminny MCP Connector
Epic
Link to Epik in Jira
Document status
DRAFT
Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.
👤 Target user
Revenue teams using AI tools (Sales, CS, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
Build an AI Knowledge Connector (MCP-based) that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides structured insights (summaries, action items, scoring etc.)
Supports deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
Metric
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of MCP/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
User interaction and design
Simple setup flow:
Generate API key / connect MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus: usable by non-technical users
Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
Importance
Notes
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH
For each deal expose:
calls/meetings/emails
Identified risks
contacts related to the deal with their job title
linked account details
all CRM field information that we have in DI + fields used for CRM filling
example - get_deal_summary
request -
{
"tool": "get_deal_summary",
"arguments": {
"deal_name": "ACME"
}
}
responce:
{
"deal_name": "ACME",
"stage": "Negotiation",
"recent_activity": [
"Demo call on 2026-04-01",
"Pricing discussion on 2026-04-03"
],
"risks": [
"Pricing concerns",
"Security review pending"
], "next_steps": [
"Send follow-up to VP Sales",
"Share security documentation"
]
}
Ensure the AI can easily understand the schema of the data coming from Jiminny
HIGH
Models perform much better when tools have:
clear names
rich descriptions
predictable schemas
Provide clear, actionable error states for customers when MCP connections fail
HIGH
Error messages should guide agents toward solutions with specific suggestions and next steps.
Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”
Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”
Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”
Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”
Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”
Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”
No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”
Allow customers to securely control who and what data is accessible externally
HIGH
Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well.
Private meetings shouldn’t be exposed via MCP
HIGH
When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP.
Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows
HIGH
Ensure data is structured to be easily combined with:
CRM (accounts, deals)
external docs
Use consistent identifiers:
account IDs
deal IDs
MCP is available only for Scale tier and above
HIGH
Customers who are on Capture tier should be able to access any of their data through MCP.
This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working.
Provide visibility into how external AI tools access Jiminny data
HIGH
Audit logs:
what data was accessed by which external system when
identity - who made the request
specific queries/filters used
failed access attempts (especially cross-visibility-boundary attempts)
Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it.
example from Atlassian -
Provide customers with example prompts to get started quickly
HIGH
MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments.
See examples in table below.
Expose Jiminny as a Connector in Claude
medium
Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny
Provide customers with example workflows to get started quickly
medium
A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.
See examples in table below.
Allow customers to use predefined Jiminny skills for common workflows
medium
Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.
Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com
See examples in table below.
Jiminny UI widget for OpenAI
medium
The reason to have this is to make certain Jiminny use cases much easier than pure chat.
Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.
For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.
Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.
Allow customers to run long-running analyses asynchronously and retrieve progress/results
low
Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to:
start the job
let it run in the background
return a status like “in progress”
let the client come back for the result later
For example - User asks in Claude:
“Analyse all calls from this quarter and tell me the top 5 objections by segment.”
That might require:
searching lots of calls
pulling many summaries/transcripts
aggregating results
Instead of making Claude wait 45+ seconds, Jiminny could:
create an analysis job
return: job_id = 123, status = running
We need this in order to support:
large-scale analysis
multi-call aggregation across lots of data
playlist generation across many calls
heavy recap/report generation
Creating a snippet through the MCP
low
Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team.
example request:
{
"tool": "create_snippet",
"arguments": {
"call_id": "call_123",
"start_time": "00:18:24",
"end_time": "00:19:10",
"title": "Pricing objection"
}
}
example response:
{
"snippet_id": "snip_987",
"title": "Pricing objection",
"share_url": "https://app.jiminny.com/snippets/snip_987"
}
Ability to add recordings or snippets to a playlist
low
Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes
Allow AI tools to read reusable Jiminny resources
low
fathom
Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be:
current deal snapshot
account summary
weekly rep recap
approved talk tracks / objection handling guidance
curated playlists or snippet libraries
This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls.
Ability to write coaching notes in Jiminny through MCP
low
What we want to do for prompts/worflows and skills
Capability
Type
User value / job to be done
Example Jiminny use case
Inputs
Steps performed
Output
Draft follow-up email
Prompt
Help reps quickly create tailored follow-ups after calls
Use the latest customer call to draft an email with recap and next steps
Call ID
optional tone
optional recipient type
Retrieve call summary and action items from Jiminny.
Pull key points, commitments, and next steps.
Apply the chosen tone and structure.
Draft the email in a consistent format.
Customer-ready follow-up email draft
Summarise call
Prompt
Help users quickly extract the most important points from a conversation
Generate a concise summary of one call for internal or external use
Call ID
optional summary style
optional audience
Retrieve transcript, metadata, and structured insights.
Identify key topics, decisions, risks, and next steps.
Format output for the chosen audience.
Short or structured call summary with actions and risks
Generate deal recap
Prompt
Help reps and managers get a fast overview of a deal
Summarise the current state of a deal based on recent calls and linked CRM context
Deal ID or account name
optional date range
Retrieve calls and meetings linked to the deal.
Pull deal metadata and CRM-linked context.
Identify recent changes, blockers, risks, and next steps.
Summarise into a concise recap.
Deal recap with status, blockers, risks, and recommended next steps
Prepare weekly recap
Prompt
Help managers or reps review what changed over a time period
Weekly recap of calls, themes, and actions for one rep, team, or account
Rep ID
team ID or account ID
time period
Retrieve relevant calls and summaries for the selected time period.
Aggregate themes, action items, and changes.
Highlight notable risks, opportunities, and follow-ups.
Weekly recap with top themes, risks, opportunities, and actions
Prepare deal review
Workflow
Help reps and managers review deal status using multiple sources of context
Review a deal before pipeline review or forecast meeting
Deal ID
optional date range
Retrieve all recent calls, emails, and meetings tied to the deal.
Pull structured call summaries, score, action items, and key points.
Pull linked CRM context such as stage, contacts, and account details.
Aggregate open risks, unresolved objections, stakeholder gaps, and next steps.
Format into a review-ready structure.
Deal review document with current status, risks, stakeholders, blockers, and next steps
Weekly team recap
Workflow
Help managers monitor activity and risk across a group of reps or accounts
End-of-week team summary for sales leadership
Team ID
date range
Retrieve calls and structured insights across the team.
Group by rep, account, or deal.
Identify recurring themes, major risks, strong calls, and missed follow-ups.
Highlight calls or deals that need attention.
Team-level recap with patterns, key risks, notable wins, and action areas
Objection analysis across calls
Workflow
Help teams identify patterns across multiple conversations
Analyse pricing or competitor objections across recent calls
Account ID
deal ID
rep ID
keyword/topic
date range
Search calls matching the topic or keyword.
Retrieve summaries, transcripts, and snippets where relevant.
Identify recurring objections and how they were handled.
Assess whether objections were resolved or remain open.
Summarise patterns and examples.
Objection analysis with themes, frequency, examples, and suggested follow-up actions
Build onboarding playlist
Workflow
Help enablement teams curate useful learning content faster
Build a playlist of strong discovery, demo, or pricing calls for new hires
Topic
call type
rep/team filter
optional time period
Search for calls matching the selected criteria.
Rank or filter calls based on score, topics, or key moments.
Identify useful snippets or full recordings.
Add selected calls/snippets into a playlist structure.
Generate short descriptions for each item.
Curated onboarding playlist with selected calls/snippets and descriptions
Deal Inspection Skill
Skill
Give users a consistent, opinionated way to assess a deal
Claude reviews a deal using a repeatable Jiminny methodology
Deal ID or account name
optional review scope
Determine whether the request is suitable for a deal inspection.
Retrieve relevant calls, summaries, CRM context, stakeholders, and risks.
Apply a standard Jiminny review framework to assess deal health.
Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs.
Present findings in a consistent structure with recommended actions.
Structured deal inspection with health assessment, red flags, and recommendations
Manager Coaching Skill
Skill
Help managers review calls consistently and identify coaching moments
Analyse a rep’s call and surface strengths, gaps, and examples for coaching
Call ID
optional rep ID
optional coaching focus
Retrieve transcript, score, summary, and key moments from the call.
Evaluate the conversation against a coaching framework.
Identify what went well and where the rep could improve.
Suggest moments to clip as snippets.
Recommend coaching actions or playlist additions.
Coaching review with strengths, gaps, snippet suggestions, and recommended coaching actions
CSM / QBR Prep Skill
Skill
Help CS or AM teams prepare structured customer reviews from conversations
Generate an account review or QBR-style summary from recent customer interactions
Account ID, optional time period
Retrieve all recent calls, emails, and account-linked context.
Aggregate themes, requests, blockers, sentiment shifts, and opportunities.
Apply a repeatable account review structure.
Summarise outcomes, open issues, and suggested next steps.
Structured QBR/account review summary with themes, risks, opportunities, and actions
Snippet Builder Skill
Skill
Help users consistently capture and label useful moments from calls
Identify useful moments in a call and convert them into reusable snippets
Call ID, optional focus area such as pricing, objection, competitor, discovery
Retrieve transcript, recording timeline, and call insights.
Detect moments matching the requested focus area.
Propose snippet start/end times and titles.
Tag snippets by type or theme.
Optionally save them or add them to a playlist.
One or more proposed or saved snippets with title, timing, and tags
💡 Technical details
Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate.
Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users.
Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns.
Open Questions
Track unresolved issues or decisions that need to be made. Update as answers become available.
Question
Answer
Date Answered
Competitor research
Competitor
MCP Availability
Core Capabilities
Advanced Capabilities
Positioning
Key Strength
Key Weakness
Gong
✅ Mature MCP
List/search calls
Get transcripts
get Call metadata
Deal intelligence
Scorecards, analytics
CRM context...
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/Users/lukas/.screenpipe/data/data/2026-04-22/1776 /Users/lukas/.screenpipe/data/data/2026-04-22/1776868754502_m1.jpg...
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Firefox
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Jiminny MCP Connector - Product - Confluence — Wor Jiminny MCP Connector - Product - Confluence — Work...
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True
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jiminny.atlassian.net/wiki/spaces/PROD/pages/37282 jiminny.atlassian.net/wiki/spaces/PROD/pages/3728244737/Jiminny+MCP+Connector...
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monitor_1
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST [URL_WITH_CREDENTIALS] Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.
You said
Jiminny MCP Connector
Epic
Link to Epik in Jira
Document status
DRAFT
Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.
👤 Target user
Revenue teams using AI tools (Sales, CS, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
Build an AI Knowledge Connector (MCP-based) that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides structured insights (summaries, action items, scoring etc.)
Supports deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
Metric
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of MCP/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
User interaction and design
Simple setup flow:
Generate API key / connect MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus: usable by non-technical users
Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
Importance
Notes
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH
For each deal expose:
calls/meetings/emails
Identified risks
contacts related to the deal with their job title
linked account details
all CRM field information that we have in DI + fields used for CRM filling
example - get_deal_summary
request -
{
"tool": "get_deal_summary",
"arguments": {
"deal_name": "ACME"
}
}
responce:
{
"deal_name": "ACME",
"stage": "Negotiation",
"recent_activity": [
"Demo call on 2026-04-01",
"Pricing discussion on 2026-04-03"
],
"risks": [
"Pricing concerns",
"Security review pending"
], "next_steps": [
"Send follow-up to VP Sales",
"Share security documentation"
]
}
Ensure the AI can easily understand the schema of the data coming from Jiminny
HIGH
Models perform much better when tools have:
clear names
rich descriptions
predictable schemas
Provide clear, actionable error states for customers when MCP connections fail
HIGH
Error messages should guide agents toward solutions with specific suggestions and next steps.
Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”
Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”
Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”
Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”
Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”
Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”
No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”
Allow customers to securely control who and what data is accessible externally
HIGH
Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well.
Private meetings shouldn’t be exposed via MCP
HIGH
When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP.
Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows
HIGH
Ensure data is structured to be easily combined with:
CRM (accounts, deals)
external docs
Use consistent identifiers:
account IDs
deal IDs
MCP is available only for Scale tier and above
HIGH
Customers who are on Capture tier should be able to access any of their data through MCP.
This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working.
Provide visibility into how external AI tools access Jiminny data
HIGH
Audit logs:
what data was accessed by which external system when
identity - who made the request
specific queries/filters used
failed access attempts (especially cross-visibility-boundary attempts)
Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it.
example from Atlassian -
Provide customers with example prompts to get started quickly
HIGH
MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments.
See examples in table below.
Expose Jiminny as a Connector in Claude
medium
Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny
Provide customers with example workflows to get started quickly
medium
A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.
See examples in table below.
Allow customers to use predefined Jiminny skills for common workflows
medium
Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.
Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com
See examples in table below.
Jiminny UI widget for OpenAI
medium
The reason to have this is to make certain Jiminny use cases much easier than pure chat.
Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.
For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.
Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.
Allow customers to run long-running analyses asynchronously and retrieve progress/results
low
Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to:
start the job
let it run in the background
return a status like “in progress”
let the client come back for the result later
For example - User asks in Claude:
“Analyse all calls from this quarter and tell me the top 5 objections by segment.”
That might require:
searching lots of calls
pulling many summaries/transcripts
aggregating results
Instead of making Claude wait 45+ seconds, Jiminny could:
create an analysis job
return: job_id = 123, status = running
We need this in order to support:
large-scale analysis
multi-call aggregation across lots of data
playlist generation across many calls
heavy recap/report generation
Creating a snippet through the MCP
low
Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team.
example request:
{
"tool": "create_snippet",
"arguments": {
"call_id": "call_123",
"start_time": "00:18:24",
"end_time": "00:19:10",
"title": "Pricing objection"
}
}
example response:
{
"snippet_id": "snip_987",
"title": "Pricing objection",
"share_url": "https://app.jiminny.com/snippets/snip_987"
}
Ability to add recordings or snippets to a playlist
low
Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes
Allow AI tools to read reusable Jiminny resources
low
fathom
Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be:
current deal snapshot
account summary
weekly rep recap
approved talk tracks / objection handling guidance
curated playlists or snippet libraries
This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls.
Ability to write coaching notes in Jiminny through MCP
low
What we want to do for prompts/worflows and skills
Capability
Type
User value / job to be done
Example Jiminny use case
Inputs
Steps performed
Output
Draft follow-up email
Prompt...
|
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Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product. 👤 Target user Revenue teams using AI tools (Sales, CS, RevOps) Companies already experimenting with Claude / OpenAI Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc) 🤕 Pain point or problem Jiminny data is locked in the platform AI tools lack access to high-value conversation context Customers must manually export/copy transcripts No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people Peer pressure as almost all competitors have this 💥 Impact and benefits For customers Better AI outputs using real customer conversations Ability to build automated workflows across tools For Jiminny Increased product stickiness (embedded in workflows) Competitive parity with Gong / Avoma etc. 💡 Solution ideas Build an AI Knowledge Connector (MCP-based) that: Exposes Jiminny data (calls, transcripts, metadata) to AI tools Provides structured insights (summaries, action items, scoring etc.) Supports deal-level context Allows integration into customer-owned AI stacks (Claude + Zapier, etc.) Success metrics List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets). Goal Metric Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows % of customers connecting external AI tools Number of MCP/API calls per customer - and which ones are being used the most Decrease the manual copying of transcripts decrease by X % User interaction and design Simple setup flow: Generate API key / connect MCP Clear instructions for Claude / Zapier setup Provide: Example prompts for Claude Pre-built workflow templates for Zapier Clear documentation - with use cases 👉 Focus: usable by non-technical users Detailed Requirements List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification. Requirement Importance Notes Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI) HIGH Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack Allow customers to access call data through AI tools HIGH Expose Jiminny data: list calls (filter by date, user, account, deal) get call get transcript (speaker-separated) get call metadata search calls (keyword + semantic) Support filtering by: account / contact/ lead / deal jiminny user date range Allow customers to search and query calls using AI tools HIGH Support keyword and semantic search across conversations Provide structured insights so customers can generate better outputs HIGH For each call, expose: Summary Action items Key points Activity type Notes Call Score CRM information Allow customers to understand deal status across multiple calls/emails HIGH For each deal expose: calls/meetings/emails Identified risks contacts related to the deal with their job title linked account details all CRM field information that we have in DI + fields used for CRM filling example - get_deal_summary request - { \"tool\": \"get_deal_summary\", \"arguments\": { \"deal_name\": \"ACME\" } } responce: { \"deal_name\": \"ACME\", \"stage\": \"Negotiation\", \"recent_activity\": [ \"Demo call on 2026-04-01\", \"Pricing discussion on 2026-04-03\" ], \"risks\": [ \"Pricing concerns\", \"Security review pending\" ], \"next_steps\": [ \"Send follow-up to VP Sales\", \"Share security documentation\" ] } Ensure the AI can easily understand the schema of the data coming from Jiminny HIGH Models perform much better when tools have: clear names rich descriptions predictable schemas Provide clear, actionable error states for customers when MCP connections fail HIGH Error messages should guide agents toward solutions with specific suggestions and next steps. Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.” Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.” Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.” Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.” Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.” Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.” No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.” Allow customers to securely control who and what data is accessible externally HIGH Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well. Private meetings shouldn’t be exposed via MCP HIGH When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP. Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows HIGH Ensure data is structured to be easily combined with: CRM (accounts, deals) external docs Use consistent identifiers: account IDs deal IDs MCP is available only for Scale tier and above HIGH Customers who are on Capture tier should be able to access any of their data through MCP. This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working. Provide visibility into how external AI tools access Jiminny data HIGH Audit logs: what data was accessed by which external system when identity - who made the request specific queries/filters used failed access attempts (especially cross-visibility-boundary attempts) Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it. example from Atlassian - Provide customers with example prompts to get started quickly HIGH MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments. See examples in table below. Expose Jiminny as a Connector in Claude medium Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny Provide customers with example workflows to get started quickly medium A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome. See examples in table below. Allow customers to use predefined Jiminny skills for common workflows medium Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job. Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com See examples in table below. Jiminny UI widget for OpenAI medium The reason to have this is to make certain Jiminny use cases much easier than pure chat. Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it. For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface. Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section. Allow customers to run long-running analyses asynchronously and retrieve progress/results low Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to: start the job let it run in the background return a status like “in progress” let the client come back for the result later For example - User asks in Claude: “Analyse all calls from this quarter and tell me the top 5 objections by segment.” That might require: searching lots of calls pulling many summaries/transcripts aggregating results Instead of making Claude wait 45+ seconds, Jiminny could: create an analysis job return: job_id = 123, status = running We need this in order to support: large-scale analysis multi-call aggregation across lots of data playlist generation across many calls heavy recap/report generation Creating a snippet through the MCP low Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team. example request: { \"tool\": \"create_snippet\", \"arguments\": { \"call_id\": \"call_123\", \"start_time\": \"00:18:24\", \"end_time\": \"00:19:10\", \"title\": \"Pricing objection\" } } example response: { \"snippet_id\": \"snip_987\", \"title\": \"Pricing objection\", \"share_url\": \"https://app.jiminny.com/snippets/snip_987\" } Ability to add recordings or snippets to a playlist low Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes Allow AI tools to read reusable Jiminny resources low fathom Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be: current deal snapshot account summary weekly rep recap approved talk tracks / objection handling guidance curated playlists or snippet libraries This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls. Ability to write coaching notes in Jiminny through MCP low What we want to do for prompts/worflows and skills Capability Type User value / job to be done Example Jiminny use case Inputs Steps performed Output Draft follow-up email Prompt Help reps quickly create tailored follow-ups after calls Use the latest customer call to draft an email with recap and next steps Call ID optional tone optional recipient type Retrieve call summary and action items from Jiminny. Pull key points, commitments, and next steps. Apply the chosen tone and structure. Draft the email in a consistent format. Customer-ready follow-up email draft Summarise call Prompt Help users quickly extract the most important points from a conversation Generate a concise summary of one call for internal or external use Call ID optional summary style optional audience Retrieve transcript, metadata, and structured insights. Identify key topics, decisions, risks, and next steps. Format output for the chosen audience. Short or structured call summary with actions and risks Generate deal recap Prompt Help reps and managers get a fast overview of a deal Summarise the current state of a deal based on recent calls and linked CRM context Deal ID or account name optional date range Retrieve calls and meetings linked to the deal. Pull deal metadata and CRM-linked context. Identify recent changes, blockers, risks, and next steps. Summarise into a concise recap. Deal recap with status, blockers, risks, and recommended next steps Prepare weekly recap Prompt Help managers or reps review what changed over a time period Weekly recap of calls, themes, and actions for one rep, team, or account Rep ID team ID or account ID time period Retrieve relevant calls and summaries for the selected time period. Aggregate themes, action items, and changes. Highlight notable risks, opportunities, and follow-ups. Weekly recap with top themes, risks, opportunities, and actions Prepare deal review Workflow Help reps and managers review deal status using multiple sources of context Review a deal before pipeline review or forecast meeting Deal ID optional date range Retrieve all recent calls, emails, and meetings tied to the deal. Pull structured call summaries, score, action items, and key points. Pull linked CRM context such as stage, contacts, and account details. Aggregate open risks, unresolved objections, stakeholder gaps, and next steps. Format into a review-ready structure. Deal review document with current status, risks, stakeholders, blockers, and next steps Weekly team recap Workflow Help managers monitor activity and risk across a group of reps or accounts End-of-week team summary for sales leadership Team ID date range Retrieve calls and structured insights across the team. Group by rep, account, or deal. Identify recurring themes, major risks, strong calls, and missed follow-ups. Highlight calls or deals that need attention. Team-level recap with patterns, key risks, notable wins, and action areas Objection analysis across calls Workflow Help teams identify patterns across multiple conversations Analyse pricing or competitor objections across recent calls Account ID deal ID rep ID keyword/topic date range Search calls matching the topic or keyword. Retrieve summaries, transcripts, and snippets where relevant. Identify recurring objections and how they were handled. Assess whether objections were resolved or remain open. Summarise patterns and examples. Objection analysis with themes, frequency, examples, and suggested follow-up actions Build onboarding playlist Workflow Help enablement teams curate useful learning content faster Build a playlist of strong discovery, demo, or pricing calls for new hires Topic call type rep/team filter optional time period Search for calls matching the selected criteria. Rank or filter calls based on score, topics, or key moments. Identify useful snippets or full recordings. Add selected calls/snippets into a playlist structure. Generate short descriptions for each item. Curated onboarding playlist with selected calls/snippets and descriptions Deal Inspection Skill Skill Give users a consistent, opinionated way to assess a deal Claude reviews a deal using a repeatable Jiminny methodology Deal ID or account name optional review scope Determine whether the request is suitable for a deal inspection. Retrieve relevant calls, summaries, CRM context, stakeholders, and risks. Apply a standard Jiminny review framework to assess deal health. Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs. Present findings in a consistent structure with recommended actions. Structured deal inspection with health assessment, red flags, and recommendations Manager Coaching Skill Skill Help managers review calls consistently and identify coaching moments Analyse a rep’s call and surface strengths, gaps, and examples for coaching Call ID optional rep ID optional coaching focus Retrieve transcript, score, summary, and key moments from the call. Evaluate the conversation against a coaching framework. Identify what went well and where the rep could improve. Suggest moments to clip as snippets. Recommend coaching actions or playlist additions. Coaching review with strengths, gaps, snippet suggestions, and recommended coaching actions CSM / QBR Prep Skill Skill Help CS or AM teams prepare structured customer reviews from conversations Generate an account review or QBR-style summary from recent customer interactions Account ID, optional time period Retrieve all recent calls, emails, and account-linked context. Aggregate themes, requests, blockers, sentiment shifts, and opportunities. Apply a repeatable account review structure. Summarise outcomes, open issues, and suggested next steps. Structured QBR/account review summary with themes, risks, opportunities, and actions Snippet Builder Skill Skill Help users consistently capture and label useful moments from calls Identify useful moments in a call and convert them into reusable snippets Call ID, optional focus area such as pricing, objection, competitor, discovery Retrieve transcript, recording timeline, and call insights. Detect moments matching the requested focus area. Propose snippet start/end times and titles. Tag snippets by type or theme. Optionally save them or add them to a playlist. One or more proposed or saved snippets with title, timing, and tags 💡 Technical details Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate. Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users. Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns. Open Questions Track unresolved issues or decisions that need to be made. Update as answers become available. Question Answer Date Answered Competitor research Competitor MCP Availability Core Capabilities Advanced Capabilities Positioning Key Strength Key Weakness Gong ✅ Mature MCP List/search calls Get transcripts get Call metadata Deal intelligence Scorecards, analytics CRM context Enterprise data layer Deep data + ecosystem integrations Limited opinionated AI workflows Avoma ✅ Mature MCP list_meetings get_meeting get_transcript key topics Deal fields/Deal level analysis Coaching/Scoring CRM context pushing insights into Notion / Confluence AI-ready meeting intelligence Strongest “outputs” layer Mostly pull-based interactions Fathom ✅ MCP list/search meetings get transcript get summary/action items Team/org context Webhooks (event-based triggers) get_weekly_recap aggregated action items across meetings creating webhooks directly from the MCP server Lightweight meeting data access Includes summaries + actions Limited analytics depth Grain ✅ Basic MCP get meetings download transcripts Notes Deals Scorecards Data access utility Simple + accessible Low sophistication, sometimes browser-based Outreach ⚠️ MCP (workflow-focused) Search sequences Emails Meetings (Kaia) account / prospect / opportunity lookups Workflow automation Multi-system orchestration Revenue workflow engine End-to-end GTM workflows Less depth in conversation data Attention ⚠️ Emerging MCP Meeting data Transcripts CRM-linked context AI insights Sales assistant workflows create snippets AI sales assistant + CRM Strong CRM linkage Less focus on external knowledge layer Planhat - Related content Ask Jiminny Anything on Call level Galya Dimitrova Hubspot app Galya Dimitrova Product Vision & Strategy Galya Dimitrova Automated CRM Filling Simona Georgieva (Deactivated) Competitive analysis Galya Dimitrova AI agent ideas Steliyan Georgiev Comments James Graham 9 Apr A few areas around Security & Compliance that need more depth before this moves beyond draft: Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate. Team visibility enforcement should be its own HIGH requirement. The current note under access control (\"ensure MCP only exposes data user has access to\") is doing too much work as a single line. Every MCP tool response (especially search_calls and deal-level aggregations) must be filtered through the same visibility engine as the UI. Semantic search is the riskiest here; it could surface results across team boundaries if not properly scoped. No data minimisation or scoping controls. We're exposing transcripts, CRM fields, contact details with job titles, deal context - significant PII and commercial data leaving the platform. Can customers configure which data types are exposed? Can transcripts be PII-stripped before they leave Jiminny? Given we position GDPR consent as a differentiator, we should be thinking about this. GDPR downstream processing is unaddressed. Piping transcripts to OpenAI/Gemini introduces new data processors. Customers need to acknowledge responsibility for downstream processing during setup. We should also consider whether call participants' recording consent covers onward sharing to third-party AI - it almost certainly doesn't by default. This is one for me to pick up. Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users. Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns. Audit logs should go deeper. Current spec (what data, which system, when) is a start. Add: identity behind the key, specific queries/filters used, and failed access attempts (especially cross-visibility-boundary attempts). Enterprise compliance teams will expect this. Downgrade handling. Doc says Scale tier and above, but doesn't say what happens on downgrade. Keys should auto-deactivate. Happy to help draft a dedicated Security & Compliance section if useful. Galya Dimitrova 15 Apr Thanks for the input @James Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. 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each deal expose:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"calls/meetings/emails","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Identified risks","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"contacts related to the deal with their job title","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"linked account details","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"all CRM field information that we have in DI + fields used for CRM filling","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"example - get_deal_summary","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"request 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The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Trying to access private meeting - “Error: Access denied. 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This is not a data or permissions issue — please retry in a few minutes.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to securely control who and what data is accessible externally","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ensure MCP only exposes data user has access to - based on team visibility settings. 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This will make it easier for customers to find us and to connect Jiminny","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide customers with example workflows to get started quickly","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"See examples in table below.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to use predefined Jiminny skills for common workflows","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"See examples in table below.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny UI widget for OpenAI","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The reason to have this is to make certain Jiminny use cases much easier than pure chat.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to run long-running analyses asynchronously and retrieve progress/results","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"start the job","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"let it run in the background","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"return a status like “in progress”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"let the client come back for the result later","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"For example - User asks in Claude:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"“Analyse all calls from this quarter and tell me the top 5 objections by segment.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"That might require:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"searching lots of calls","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"pulling many summaries/transcripts","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"aggregating results","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Instead of making Claude wait 45+ seconds, Jiminny could:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"create an analysis job","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"return: job_id = 123, status = running","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"We need this in order to support:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"large-scale analysis","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"multi-call aggregation across lots of data","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"playlist generation across many calls","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"heavy recap/report generation","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Creating a snippet through the MCP","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"example request:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"{","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"tool\": \"create_snippet\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"arguments\": {","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"call_id\": \"call_123\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"start_time\": \"00:18:24\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"end_time\": \"00:19:10\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"title\": \"Pricing objection\"","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"example response:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"{","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"snippet_id\": \"snip_987\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"title\": \"Pricing objection\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"share_url\": \"https://app.jiminny.com/snippets/snip_987\"","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ability to add recordings or snippets to a playlist","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow AI tools to read reusable Jiminny resources","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"fathom","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"current deal snapshot","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"account summary","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"weekly rep recap","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"approved talk tracks / objection handling guidance","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"curated playlists or snippet libraries","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ability to write coaching notes in Jiminny through MCP","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"What we want to do for prompts/worflows and skills","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Capability","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Type","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"User value / job to be done","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Example Jiminny use case","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Inputs","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Steps performed","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Output","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Draft follow-up email","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Prompt","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"}]...
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7411094662693919321
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click
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accessibility
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST [URL_WITH_CREDENTIALS] Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.
You said
Jiminny MCP Connector
Epic
Link to Epik in Jira
Document status
DRAFT
Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.
👤 Target user
Revenue teams using AI tools (Sales, CS, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
Build an AI Knowledge Connector (MCP-based) that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides structured insights (summaries, action items, scoring etc.)
Supports deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
Metric
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of MCP/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
User interaction and design
Simple setup flow:
Generate API key / connect MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus: usable by non-technical users
Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
Importance
Notes
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH
For each deal expose:
calls/meetings/emails
Identified risks
contacts related to the deal with their job title
linked account details
all CRM field information that we have in DI + fields used for CRM filling
example - get_deal_summary
request -
{
"tool": "get_deal_summary",
"arguments": {
"deal_name": "ACME"
}
}
responce:
{
"deal_name": "ACME",
"stage": "Negotiation",
"recent_activity": [
"Demo call on 2026-04-01",
"Pricing discussion on 2026-04-03"
],
"risks": [
"Pricing concerns",
"Security review pending"
], "next_steps": [
"Send follow-up to VP Sales",
"Share security documentation"
]
}
Ensure the AI can easily understand the schema of the data coming from Jiminny
HIGH
Models perform much better when tools have:
clear names
rich descriptions
predictable schemas
Provide clear, actionable error states for customers when MCP connections fail
HIGH
Error messages should guide agents toward solutions with specific suggestions and next steps.
Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”
Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”
Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”
Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”
Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”
Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”
No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”
Allow customers to securely control who and what data is accessible externally
HIGH
Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well.
Private meetings shouldn’t be exposed via MCP
HIGH
When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP.
Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows
HIGH
Ensure data is structured to be easily combined with:
CRM (accounts, deals)
external docs
Use consistent identifiers:
account IDs
deal IDs
MCP is available only for Scale tier and above
HIGH
Customers who are on Capture tier should be able to access any of their data through MCP.
This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working.
Provide visibility into how external AI tools access Jiminny data
HIGH
Audit logs:
what data was accessed by which external system when
identity - who made the request
specific queries/filters used
failed access attempts (especially cross-visibility-boundary attempts)
Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it.
example from Atlassian -
Provide customers with example prompts to get started quickly
HIGH
MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments.
See examples in table below.
Expose Jiminny as a Connector in Claude
medium
Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny
Provide customers with example workflows to get started quickly
medium
A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.
See examples in table below.
Allow customers to use predefined Jiminny skills for common workflows
medium
Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.
Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com
See examples in table below.
Jiminny UI widget for OpenAI
medium
The reason to have this is to make certain Jiminny use cases much easier than pure chat.
Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.
For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.
Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.
Allow customers to run long-running analyses asynchronously and retrieve progress/results
low
Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to:
start the job
let it run in the background
return a status like “in progress”
let the client come back for the result later
For example - User asks in Claude:
“Analyse all calls from this quarter and tell me the top 5 objections by segment.”
That might require:
searching lots of calls
pulling many summaries/transcripts
aggregating results
Instead of making Claude wait 45+ seconds, Jiminny could:
create an analysis job
return: job_id = 123, status = running
We need this in order to support:
large-scale analysis
multi-call aggregation across lots of data
playlist generation across many calls
heavy recap/report generation
Creating a snippet through the MCP
low
Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team.
example request:
{
"tool": "create_snippet",
"arguments": {
"call_id": "call_123",
"start_time": "00:18:24",
"end_time": "00:19:10",
"title": "Pricing objection"
}
}
example response:
{
"snippet_id": "snip_987",
"title": "Pricing objection",
"share_url": "https://app.jiminny.com/snippets/snip_987"
}
Ability to add recordings or snippets to a playlist
low
Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes
Allow AI tools to read reusable Jiminny resources
low
fathom
Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be:
current deal snapshot
account summary
weekly rep recap
approved talk tracks / objection handling guidance
curated playlists or snippet libraries
This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls.
Ability to write coaching notes in Jiminny through MCP
low
What we want to do for prompts/worflows and skills
Capability
Type
User value / job to be done
Example Jiminny use case
Inputs
Steps performed
Output
Draft follow-up email
Prompt...
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2026-04-22T14:39:45.326131+00:00
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/Users/lukas/.screenpipe/data/data/2026-04-22/1776 /Users/lukas/.screenpipe/data/data/2026-04-22/1776868785326_m2.jpg...
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Jiminny MCP Connector - Product - Confluence — Wor Jiminny MCP Connector - Product - Confluence — Work...
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jiminny.atlassian.net/wiki/spaces/PROD/pages/37282 jiminny.atlassian.net/wiki/spaces/PROD/pages/3728244737/Jiminny+MCP+Connector...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST [URL_WITH_CREDENTIALS] Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.
You said
Jiminny MCP Connector
Epic
Link to Epik in Jira
Document status
DRAFT
Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.
👤 Target user
Revenue teams using AI tools (Sales, CS, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
Build an AI Knowledge Connector (MCP-based) that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides structured insights (summaries, action items, scoring etc.)
Supports deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
Metric
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of MCP/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
User interaction and design
Simple setup flow:
Generate API key / connect MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus: usable by non-technical users
Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
Importance
Notes
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH
For each deal expose:
calls/meetings/emails
Identified risks
contacts related to the deal with their job title
linked account details
all CRM field information that we have in DI + fields used for CRM filling
example - get_deal_summary
request -
{
"tool": "get_deal_summary",
"arguments": {
"deal_name": "ACME"
}
}
responce:
{
"deal_name": "ACME",
"stage": "Negotiation",
"recent_activity": [
"Demo call on 2026-04-01",
"Pricing discussion on 2026-04-03"
],
"risks": [
"Pricing concerns",
"Security review pending"
], "next_steps": [
"Send follow-up to VP Sales",
"Share security documentation"
]
}
Ensure the AI can easily understand the schema of the data coming from Jiminny
HIGH
Models perform much better when tools have:
clear names
rich descriptions
predictable schemas
Provide clear, actionable error states for customers when MCP connections fail
HIGH
Error messages should guide agents toward solutions with specific suggestions and next steps.
Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”
Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”
Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”
Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”
Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”
Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”
No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”
Allow customers to securely control who and what data is accessible externally
HIGH
Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well.
Private meetings shouldn’t be exposed via MCP
HIGH
When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP.
Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows
HIGH
Ensure data is structured to be easily combined with:
CRM (accounts, deals)
external docs
Use consistent identifiers:
account IDs
deal IDs
MCP is available only for Scale tier and above
HIGH
Customers who are on Capture tier should be able to access any of their data through MCP.
This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working.
Provide visibility into how external AI tools access Jiminny data
HIGH
Audit logs:
what data was accessed by which external system when
identity - who made the request
specific queries/filters used
failed access attempts (especially cross-visibility-boundary attempts)
Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it.
example from Atlassian -
Provide customers with example prompts to get started quickly
HIGH
MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments.
See examples in table below.
Expose Jiminny as a Connector in Claude
medium
Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny
Provide customers with example workflows to get started quickly
medium
A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.
See examples in table below.
Allow customers to use predefined Jiminny skills for common workflows
medium
Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.
Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com
See examples in table below.
Jiminny UI widget for OpenAI
medium
The reason to have this is to make certain Jiminny use cases much easier than pure chat.
Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.
For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.
Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.
Allow customers to run long-running analyses asynchronously and retrieve progress/results
low
Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to:
start the job
let it run in the background
return a status like “in progress”
let the client come back for the result later
For example - User asks in Claude:
“Analyse all calls from this quarter and tell me the top 5 objections by segment.”
That might require:
searching lots of calls
pulling many summaries/transcripts
aggregating results
Instead of making Claude wait 45+ seconds, Jiminny could:
create an analysis job
return: job_id = 123, status = running
We need this in order to support:
large-scale analysis
multi-call aggregation across lots of data
playlist generation across many calls
heavy recap/report generation
Creating a snippet through the MCP
low
Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team.
example request:
{
"tool": "create_snippet",
"arguments": {
"call_id": "call_123",
"start_time": "00:18:24",
"end_time": "00:19:10",
"title": "Pricing objection"
}
}
example response:
{
"snippet_id": "snip_987",
"title": "Pricing objection",
"share_url": "https://app.jiminny.com/snippets/snip_987"
}
Ability to add recordings or snippets to a playlist
low
Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes
Allow AI tools to read reusable Jiminny resources
low
fathom
Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be:
current deal snapshot
account summary
weekly rep recap
approved talk tracks / objection handling guidance
curated playlists or snippet libraries
This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls.
Ability to write coaching notes in Jiminny through MCP
low
What we want to do for prompts/worflows and skills
Capability
Type
User value / job to be done
Example Jiminny use case
Inputs
Steps performed
Output
Draft follow-up email
Prompt
Help reps quickly create tailored follow-ups after calls
Use the latest customer call to draft an email with recap and next steps
Call ID
optional tone
optional recipient type
Retrieve call summary and action items from Jiminny.
Pull key points, commitments, and next steps.
Apply the chosen tone and structure.
Draft the email in a consistent format.
Customer-ready follow-up email draft
Summarise call
Prompt
Help users quickly extract the most important points from a conversation
Generate a concise summary of one call for internal or external use
Call ID
optional summary style
optional audience
Retrieve transcript, metadata, and structured insights.
Identify key topics, decisions, risks, and next steps.
Format output for the chosen audience.
Short or structured call summary with actions and risks
Generate deal recap
Prompt
Help reps and managers get a fast overview of a deal
Summarise the current state of a deal based on recent calls and linked CRM context
Deal ID or account name
optional date range
Retrieve calls and meetings linked to the deal.
Pull deal metadata and CRM-linked context.
Identify recent changes, blockers, risks, and next steps.
Summarise into a concise recap.
Deal recap with status, blockers, risks, and recommended next steps
Prepare weekly recap
Prompt
Help managers or reps review what changed over a time period
Weekly recap of calls, themes, and actions for one rep, team, or account
Rep ID
team ID or account ID
time period
Retrieve relevant calls and summaries for the selected time period.
Aggregate themes, action items, and changes.
Highlight notable risks, opportunities, and follow-ups.
Weekly recap with top themes, risks, opportunities, and actions
Prepare deal review
Workflow
Help reps and managers review deal status using multiple sources of context
Review a deal before pipeline review or forecast meeting
Deal ID
optional date range
Retrieve all recent calls, emails, and meetings tied to the deal.
Pull structured call summaries, score, action items, and key points.
Pull linked CRM context such as stage, contacts, and account details.
Aggregate open risks, unresolved objections, stakeholder gaps, and next steps.
Format into a review-ready structure.
Deal review document with current status, risks, stakeholders, blockers, and next steps
Weekly team recap
Workflow
Help managers monitor activity and risk across a group of reps or accounts
End-of-week team summary for sales leadership
Team ID
date range
Retrieve calls and structured insights across the team.
Group by rep, account, or deal.
Identify recurring themes, major risks, strong calls, and missed follow-ups.
Highlight calls or deals that need attention.
Team-level recap with patterns, key risks, notable wins, and action areas
Objection analysis across calls
Workflow
Help teams identify patterns across multiple conversations
Analyse pricing or competitor objections across recent calls
Account ID
deal ID
rep ID
keyword/topic
date range
Search calls matching the topic or keyword.
Retrieve summaries, transcripts, and snippets where relevant.
Identify recurring objections and how they were handled.
Assess whether objections were resolved or remain open.
Summarise patterns and examples.
Objection analysis with themes, frequency, examples, and suggested follow-up actions
Build onboarding playlist
Workflow
Help enablement teams curate useful learning content faster
Build a playlist of strong discovery, demo, or pricing calls for new hires
Topic
call type
rep/team filter
optional time period
Search for calls matching the selected criteria.
Rank or filter calls based on score, topics, or key moments.
Identify useful snippets or full recordings.
Add selected calls/snippets into a playlist structure.
Generate short descriptions for each item.
Curated onboarding playlist with selected calls/snippets and descriptions
Deal Inspection Skill
Skill
Give users a consistent, opinionated way to assess a deal
Claude reviews a deal using a repeatable Jiminny methodology
Deal ID or account name
optional review scope
Determine whether the request is suitable for a deal inspection.
Retrieve relevant calls, summaries, CRM context, stakeholders, and risks.
Apply a standard Jiminny review framework to assess deal health.
Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs.
Present findings in a consistent structure with recommended actions.
Structured deal inspection with health assessment, red flags, and recommendations
Manager Coaching Skill
Skill
Help managers review calls consistently and identify coaching moments
Analyse a rep’s call and surface strengths, gaps, and examples for coaching
Call ID
optional rep ID
optional coaching focus
Retrieve transcript, score, summary, and key moments from the call.
Evaluate the conversation against a coaching framework.
Identify what went well and where the rep could improve.
Suggest moments to clip as snippets.
Recommend coaching actions or playlist additions.
Coaching review with strengths, gaps, snippet suggestions, and recommended coaching actions
CSM / QBR Prep Skill
Skill
Help CS or AM teams prepare structured customer reviews from conversations
Generate an account review or QBR-style summary from recent customer interactions
Account ID, optional time period
Retrieve all recent calls, emails, and account-linked context.
Aggregate themes, requests, blockers, sentiment shifts, and opportunities.
Apply a repeatable account review structure.
Summarise outcomes, open issues, and suggested next steps.
Structured QBR/account review summary with themes, risks, opportunities, and actions
Snippet Builder Skill
Skill
Help users consistently capture and label useful moments from calls
Identify useful moments in a call and convert them into reusable snippets
Call ID, optional focus area such as pricing, objection, competitor, discovery
Retrieve transcript, recording timeline, and call insights.
Detect moments matching the requested focus area.
Propose snippet start/end times and titles.
Tag snippets by type or theme.
Optionally save them or add them to a playlist.
One or more proposed or saved snippets with title, timing, and tags
💡 Technical details
Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate.
Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users.
Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns.
Open Questions
Track unresolved issues or decisions that need to be made. Update as answers become available.
Question
Answer
Date Answered
Competitor research
Competitor
MCP Availability
Core Capabilities
Advanced Capabilities
Positioning
Key Strength
Key Weakness
Gong
✅ Mature MCP
List/search calls
Get transcripts
get Call metadata
Deal intelligence
Scorecards, analytics
CRM context
Enterprise data layer
Deep data + ecosystem integrations
Limited opinionated AI workflows
Avoma
✅ Mature MCP
list_meetings
get_meeting
get_transcript
key topics
Deal fields/Deal level analysis
Coaching/Scoring
CRM context
pushing insights into Notion / Confluence
AI-ready meeting intelligence
Strongest “outputs” layer
Mostly pull-based interactions
Fathom
✅ MCP
list/search meetings
get transcript
get summary/action items
Team/org context
Webhooks (event-based triggers)
get_weekly_recap
aggregated action items across meetings
creating webhooks directly from the MCP server
Lightweight meeting data access
Includes summaries + actions
Limited analytics depth
Grain
✅ Basic MCP
get meetings
download transcripts
Notes
Deals
Scorecards
Data access utility
Simple + accessible
Low sophistication, sometimes browser-based
Outreach
⚠️ MCP (workflow-focused)
Search sequences
Emails
Meetings (Kaia)
account / prospect / opportunity lookups
Workflow automation
Multi-system orchestration
Revenue workflow engine
End-to-end GTM workflows
Less depth in conversation data
Attention
⚠️ Emerging MCP
Meeting data
Transcripts
CRM-linked context
AI insights
Sales assistant workflows
create snippets
AI sales assistant + CRM
Strong CRM linkage
Less focus on external knowledge layer
Planhat -
Related content
Ask Jiminny Anything on Call level
Galya Dimitrova
Hubspot app
Galya Dimitrova
Product Vision & Strategy
Galya Dimitrova
Automated CRM Filling
Simona Georgieva (Deactivated)
Competitive analysis
Galya Dimitrova
AI agent ideas
Steliyan Georgiev
Comments
James Graham
9 Apr
A few areas around Security & Compliance that need more depth before this moves beyond draft:
Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate.
Team visibility enforcement should be its own HIGH requirement. The current note under access control ("ensure MCP only exposes data user has access to") is doing too much work as a single line. Every MCP tool response (especially search_calls and deal-level aggregations) must be filtered through the same visibility engine as the UI. Semantic search is the riskiest here; it could surface results across team boundaries if not properly scoped.
No data minimisation or scoping controls. We're exposing transcripts, CRM fields, contact details with job titles, deal context - significant PII and commercial data leaving the platform. Can customers configure which data types are exposed? Can transcripts be PII-stripped before they leave Jiminny? Given we position GDPR consent as a differentiator, we should be thinking about this.
GDPR downstream processing is unaddressed. Piping transcripts to OpenAI/Gemini introduces new data processors. Customers need to acknowledge responsibility for downstream processing during setup. We should also consider whether call participants' recording consent covers onward sharing to third-party AI - it almost certainly doesn't by default. This is one for me to pick up.
Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users.
Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns.
Audit logs should go deeper. Current spec (what data, which system, when) is a start. Add: identity behind the key, specific queries/filters used, and failed access attempts (especially cross-visibility-boundary attempts). Enterprise compliance teams will expect this.
Downgrade handling. Doc says Scale tier and above, but doesn't say what happens on downgrade. Keys should auto-deactivate.
Happy to help draft a dedicated Security & Compliance section i...
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Gemini","depth":15,"bounds":{"left":0.36053857,"top":0.14764565,"width":0.0003324468,"height":0.0007980846},"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Conversation with Gemini","depth":16,"bounds":{"left":0.36053857,"top":0.15003991,"width":0.1200133,"height":0.025538707},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Copy prompt","depth":21,"bounds":{"left":0.3834774,"top":0.18355946,"width":0.013297873,"height":0.031923383},"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXButton","text":"Edit","depth":21,"bounds":{"left":0.39810506,"top":0.18355946,"width":0.013297873,"height":0.031923383},"role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXHeading","text":"You said Jiminny MCP Connector Epic Link to Epik in Jira Document status DRAFT Objective Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows. Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product. 👤 Target user Revenue teams using AI tools (Sales, CS, RevOps) Companies already experimenting with Claude / OpenAI Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc) 🤕 Pain point or problem Jiminny data is locked in the platform AI tools lack access to high-value conversation context Customers must manually export/copy transcripts No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people Peer pressure as almost all competitors have this 💥 Impact and benefits For customers Better AI outputs using real customer conversations Ability to build automated workflows across tools For Jiminny Increased product stickiness (embedded in workflows) Competitive parity with Gong / Avoma etc. 💡 Solution ideas Build an AI Knowledge Connector (MCP-based) that: Exposes Jiminny data (calls, transcripts, metadata) to AI tools Provides structured insights (summaries, action items, scoring etc.) Supports deal-level context Allows integration into customer-owned AI stacks (Claude + Zapier, etc.) Success metrics List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets). Goal Metric Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows % of customers connecting external AI tools Number of MCP/API calls per customer - and which ones are being used the most Decrease the manual copying of transcripts decrease by X % User interaction and design Simple setup flow: Generate API key / connect MCP Clear instructions for Claude / Zapier setup Provide: Example prompts for Claude Pre-built workflow templates for Zapier Clear documentation - with use cases 👉 Focus: usable by non-technical users Detailed Requirements List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification. Requirement Importance Notes Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI) HIGH Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack Allow customers to access call data through AI tools HIGH Expose Jiminny data: list calls (filter by date, user, account, deal) get call get transcript (speaker-separated) get call metadata search calls (keyword + semantic) Support filtering by: account / contact/ lead / deal jiminny user date range Allow customers to search and query calls using AI tools HIGH Support keyword and semantic search across conversations Provide structured insights so customers can generate better outputs HIGH For each call, expose: Summary Action items Key points Activity type Notes Call Score CRM information Allow customers to understand deal status across multiple calls/emails HIGH For each deal expose: calls/meetings/emails Identified risks contacts related to the deal with their job title linked account details all CRM field information that we have in DI + fields used for CRM filling example - get_deal_summary request - { \"tool\": \"get_deal_summary\", \"arguments\": { \"deal_name\": \"ACME\" } } responce: { \"deal_name\": \"ACME\", \"stage\": \"Negotiation\", \"recent_activity\": [ \"Demo call on 2026-04-01\", \"Pricing discussion on 2026-04-03\" ], \"risks\": [ \"Pricing concerns\", \"Security review pending\" ], \"next_steps\": [ \"Send follow-up to VP Sales\", \"Share security documentation\" ] } Ensure the AI can easily understand the schema of the data coming from Jiminny HIGH Models perform much better when tools have: clear names rich descriptions predictable schemas Provide clear, actionable error states for customers when MCP connections fail HIGH Error messages should guide agents toward solutions with specific suggestions and next steps. Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.” Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.” Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.” Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.” Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.” Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.” No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.” Allow customers to securely control who and what data is accessible externally HIGH Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well. Private meetings shouldn’t be exposed via MCP HIGH When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP. Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows HIGH Ensure data is structured to be easily combined with: CRM (accounts, deals) external docs Use consistent identifiers: account IDs deal IDs MCP is available only for Scale tier and above HIGH Customers who are on Capture tier should be able to access any of their data through MCP. This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working. Provide visibility into how external AI tools access Jiminny data HIGH Audit logs: what data was accessed by which external system when identity - who made the request specific queries/filters used failed access attempts (especially cross-visibility-boundary attempts) Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it. example from Atlassian - Provide customers with example prompts to get started quickly HIGH MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments. See examples in table below. Expose Jiminny as a Connector in Claude medium Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny Provide customers with example workflows to get started quickly medium A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome. See examples in table below. Allow customers to use predefined Jiminny skills for common workflows medium Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job. Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com See examples in table below. Jiminny UI widget for OpenAI medium The reason to have this is to make certain Jiminny use cases much easier than pure chat. Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it. For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface. Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section. Allow customers to run long-running analyses asynchronously and retrieve progress/results low Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to: start the job let it run in the background return a status like “in progress” let the client come back for the result later For example - User asks in Claude: “Analyse all calls from this quarter and tell me the top 5 objections by segment.” That might require: searching lots of calls pulling many summaries/transcripts aggregating results Instead of making Claude wait 45+ seconds, Jiminny could: create an analysis job return: job_id = 123, status = running We need this in order to support: large-scale analysis multi-call aggregation across lots of data playlist generation across many calls heavy recap/report generation Creating a snippet through the MCP low Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team. example request: { \"tool\": \"create_snippet\", \"arguments\": { \"call_id\": \"call_123\", \"start_time\": \"00:18:24\", \"end_time\": \"00:19:10\", \"title\": \"Pricing objection\" } } example response: { \"snippet_id\": \"snip_987\", \"title\": \"Pricing objection\", \"share_url\": \"https://app.jiminny.com/snippets/snip_987\" } Ability to add recordings or snippets to a playlist low Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes Allow AI tools to read reusable Jiminny resources low fathom Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be: current deal snapshot account summary weekly rep recap approved talk tracks / objection handling guidance curated playlists or snippet libraries This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls. Ability to write coaching notes in Jiminny through MCP low What we want to do for prompts/worflows and skills Capability Type User value / job to be done Example Jiminny use case Inputs Steps performed Output Draft follow-up email Prompt Help reps quickly create tailored follow-ups after calls Use the latest customer call to draft an email with recap and next steps Call ID optional tone optional recipient type Retrieve call summary and action items from Jiminny. Pull key points, commitments, and next steps. Apply the chosen tone and structure. Draft the email in a consistent format. Customer-ready follow-up email draft Summarise call Prompt Help users quickly extract the most important points from a conversation Generate a concise summary of one call for internal or external use Call ID optional summary style optional audience Retrieve transcript, metadata, and structured insights. Identify key topics, decisions, risks, and next steps. Format output for the chosen audience. Short or structured call summary with actions and risks Generate deal recap Prompt Help reps and managers get a fast overview of a deal Summarise the current state of a deal based on recent calls and linked CRM context Deal ID or account name optional date range Retrieve calls and meetings linked to the deal. Pull deal metadata and CRM-linked context. Identify recent changes, blockers, risks, and next steps. Summarise into a concise recap. Deal recap with status, blockers, risks, and recommended next steps Prepare weekly recap Prompt Help managers or reps review what changed over a time period Weekly recap of calls, themes, and actions for one rep, team, or account Rep ID team ID or account ID time period Retrieve relevant calls and summaries for the selected time period. Aggregate themes, action items, and changes. Highlight notable risks, opportunities, and follow-ups. Weekly recap with top themes, risks, opportunities, and actions Prepare deal review Workflow Help reps and managers review deal status using multiple sources of context Review a deal before pipeline review or forecast meeting Deal ID optional date range Retrieve all recent calls, emails, and meetings tied to the deal. Pull structured call summaries, score, action items, and key points. Pull linked CRM context such as stage, contacts, and account details. Aggregate open risks, unresolved objections, stakeholder gaps, and next steps. Format into a review-ready structure. Deal review document with current status, risks, stakeholders, blockers, and next steps Weekly team recap Workflow Help managers monitor activity and risk across a group of reps or accounts End-of-week team summary for sales leadership Team ID date range Retrieve calls and structured insights across the team. Group by rep, account, or deal. Identify recurring themes, major risks, strong calls, and missed follow-ups. Highlight calls or deals that need attention. Team-level recap with patterns, key risks, notable wins, and action areas Objection analysis across calls Workflow Help teams identify patterns across multiple conversations Analyse pricing or competitor objections across recent calls Account ID deal ID rep ID keyword/topic date range Search calls matching the topic or keyword. Retrieve summaries, transcripts, and snippets where relevant. Identify recurring objections and how they were handled. Assess whether objections were resolved or remain open. Summarise patterns and examples. Objection analysis with themes, frequency, examples, and suggested follow-up actions Build onboarding playlist Workflow Help enablement teams curate useful learning content faster Build a playlist of strong discovery, demo, or pricing calls for new hires Topic call type rep/team filter optional time period Search for calls matching the selected criteria. Rank or filter calls based on score, topics, or key moments. Identify useful snippets or full recordings. Add selected calls/snippets into a playlist structure. Generate short descriptions for each item. Curated onboarding playlist with selected calls/snippets and descriptions Deal Inspection Skill Skill Give users a consistent, opinionated way to assess a deal Claude reviews a deal using a repeatable Jiminny methodology Deal ID or account name optional review scope Determine whether the request is suitable for a deal inspection. Retrieve relevant calls, summaries, CRM context, stakeholders, and risks. Apply a standard Jiminny review framework to assess deal health. Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs. Present findings in a consistent structure with recommended actions. Structured deal inspection with health assessment, red flags, and recommendations Manager Coaching Skill Skill Help managers review calls consistently and identify coaching moments Analyse a rep’s call and surface strengths, gaps, and examples for coaching Call ID optional rep ID optional coaching focus Retrieve transcript, score, summary, and key moments from the call. Evaluate the conversation against a coaching framework. Identify what went well and where the rep could improve. Suggest moments to clip as snippets. Recommend coaching actions or playlist additions. Coaching review with strengths, gaps, snippet suggestions, and recommended coaching actions CSM / QBR Prep Skill Skill Help CS or AM teams prepare structured customer reviews from conversations Generate an account review or QBR-style summary from recent customer interactions Account ID, optional time period Retrieve all recent calls, emails, and account-linked context. Aggregate themes, requests, blockers, sentiment shifts, and opportunities. Apply a repeatable account review structure. Summarise outcomes, open issues, and suggested next steps. Structured QBR/account review summary with themes, risks, opportunities, and actions Snippet Builder Skill Skill Help users consistently capture and label useful moments from calls Identify useful moments in a call and convert them into reusable snippets Call ID, optional focus area such as pricing, objection, competitor, discovery Retrieve transcript, recording timeline, and call insights. Detect moments matching the requested focus area. Propose snippet start/end times and titles. Tag snippets by type or theme. Optionally save them or add them to a playlist. One or more proposed or saved snippets with title, timing, and tags 💡 Technical details Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate. Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users. Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns. Open Questions Track unresolved issues or decisions that need to be made. Update as answers become available. Question Answer Date Answered Competitor research Competitor MCP Availability Core Capabilities Advanced Capabilities Positioning Key Strength Key Weakness Gong ✅ Mature MCP List/search calls Get transcripts get Call metadata Deal intelligence Scorecards, analytics CRM context Enterprise data layer Deep data + ecosystem integrations Limited opinionated AI workflows Avoma ✅ Mature MCP list_meetings get_meeting get_transcript key topics Deal fields/Deal level analysis Coaching/Scoring CRM context pushing insights into Notion / Confluence AI-ready meeting intelligence Strongest “outputs” layer Mostly pull-based interactions Fathom ✅ MCP list/search meetings get transcript get summary/action items Team/org context Webhooks (event-based triggers) get_weekly_recap aggregated action items across meetings creating webhooks directly from the MCP server Lightweight meeting data access Includes summaries + actions Limited analytics depth Grain ✅ Basic MCP get meetings download transcripts Notes Deals Scorecards Data access utility Simple + accessible Low sophistication, sometimes browser-based Outreach ⚠️ MCP (workflow-focused) Search sequences Emails Meetings (Kaia) account / prospect / opportunity lookups Workflow automation Multi-system orchestration Revenue workflow engine End-to-end GTM workflows Less depth in conversation data Attention ⚠️ Emerging MCP Meeting data Transcripts CRM-linked context AI insights Sales assistant workflows create snippets AI sales assistant + CRM Strong CRM linkage Less focus on external knowledge layer Planhat - Related content Ask Jiminny Anything on Call level Galya Dimitrova Hubspot app Galya Dimitrova Product Vision & Strategy Galya Dimitrova Automated CRM Filling Simona Georgieva (Deactivated) Competitive analysis Galya Dimitrova AI agent ideas Steliyan Georgiev Comments James Graham 9 Apr A few areas around Security & Compliance that need more depth before this moves beyond draft: Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate. Team visibility enforcement should be its own HIGH requirement. The current note under access control (\"ensure MCP only exposes data user has access to\") is doing too much work as a single line. Every MCP tool response (especially search_calls and deal-level aggregations) must be filtered through the same visibility engine as the UI. Semantic search is the riskiest here; it could surface results across team boundaries if not properly scoped. No data minimisation or scoping controls. We're exposing transcripts, CRM fields, contact details with job titles, deal context - significant PII and commercial data leaving the platform. Can customers configure which data types are exposed? Can transcripts be PII-stripped before they leave Jiminny? Given we position GDPR consent as a differentiator, we should be thinking about this. GDPR downstream processing is unaddressed. Piping transcripts to OpenAI/Gemini introduces new data processors. Customers need to acknowledge responsibility for downstream processing during setup. We should also consider whether call participants' recording consent covers onward sharing to third-party AI - it almost certainly doesn't by default. This is one for me to pick up. Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users. Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns. Audit logs should go deeper. Current spec (what data, which system, when) is a start. Add: identity behind the key, specific queries/filters used, and failed access attempts (especially cross-visibility-boundary attempts). Enterprise compliance teams will expect this. Downgrade handling. Doc says Scale tier and above, but doesn't say what happens on downgrade. Keys should auto-deactivate. Happy to help draft a dedicated Security & Compliance section if useful. Galya Dimitrova 15 Apr Thanks for the input @James Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.","depth":21,"bounds":{"left":0.41805187,"top":0.19313647,"width":0.051861703,"height":0.11173184},"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"You said","depth":23,"bounds":{"left":0.36053857,"top":0.19553073,"width":0.019946808,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny MCP Connector","depth":23,"bounds":{"left":0.41805187,"top":0.1963288,"width":0.03125,"height":0.038707104},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Epic","depth":23,"bounds":{"left":0.41805187,"top":0.30806065,"width":0.010305851,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Link to Epik in Jira","depth":23,"bounds":{"left":0.41805187,"top":0.33040702,"width":0.043218084,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Document status","depth":23,"bounds":{"left":0.41805187,"top":0.3527534,"width":0.04105718,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"DRAFT","depth":23,"bounds":{"left":0.41805187,"top":0.37509975,"width":0.016289894,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Objective","depth":23,"bounds":{"left":0.41805187,"top":0.39744613,"width":0.024268618,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.","depth":23,"bounds":{"left":0.41805187,"top":0.4197925,"width":0.051529255,"height":0.17278531},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.","depth":23,"bounds":{"left":0.41805187,"top":0.59856343,"width":0.049700797,"height":0.10574621},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"👤 Target user","depth":23,"bounds":{"left":0.41805187,"top":0.75458896,"width":0.033909574,"height":0.016759777},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Revenue teams using AI tools (Sales, CS, RevOps)","depth":23,"bounds":{"left":0.41805187,"top":0.7773344,"width":0.051030584,"height":0.061053474},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Companies already experimenting with Claude / OpenAI","depth":23,"bounds":{"left":0.41805187,"top":0.8443735,"width":0.04637633,"height":0.061053474},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)","depth":23,"bounds":{"left":0.41805187,"top":0.9114126,"width":0.051363032,"height":0.0885874},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"🤕 Pain point or problem","depth":23,"bounds":{"left":0.41805187,"top":1.0,"width":0.03706782,"height":-0.0897845},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny data is locked in the platform","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"AI tools lack access to high-value conversation context","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Customers must manually export/copy transcripts","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Peer pressure as almost all competitors have this","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"💥 Impact and benefits","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"For customers","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Better AI outputs using real customer conversations","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ability to build automated workflows across tools","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"For Jiminny","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Increased product stickiness (embedded in workflows)","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Competitive parity with Gong / Avoma etc.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"💡 Solution ideas","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Build an AI Knowledge Connector (MCP-based) that:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Exposes Jiminny data (calls, transcripts, metadata) to AI tools","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provides structured insights (summaries, action items, scoring etc.)","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Supports deal-level context","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Success metrics","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"List the key goals and how you will measure success. 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The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Trying to access private meeting - “Error: Access denied. 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This is not a data or permissions issue — please retry in a few minutes.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to securely control who and what data is accessible externally","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ensure MCP only exposes data user has access to - based on team visibility settings. 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This will make it easier for customers to find us and to connect Jiminny","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide customers with example workflows to get started quickly","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"See examples in table below.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to use predefined Jiminny skills for common workflows","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"See examples in table below.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny UI widget for OpenAI","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The reason to have this is to make certain Jiminny use cases much easier than pure chat.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to run long-running analyses asynchronously and retrieve progress/results","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Some requests/questions require analysis of Hugh volumes of data. 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Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"example request:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"{","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"tool\": \"create_snippet\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"arguments\": {","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"call_id\": \"call_123\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"start_time\": \"00:18:24\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"end_time\": \"00:19:10\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"title\": \"Pricing objection\"","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"example response:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"{","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"snippet_id\": \"snip_987\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"title\": \"Pricing objection\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"share_url\": \"https://app.jiminny.com/snippets/snip_987\"","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ability to add recordings or snippets to a playlist","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow AI tools to read reusable Jiminny resources","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"fathom","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"current deal snapshot","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"account summary","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"weekly rep recap","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"approved talk tracks / objection handling guidance","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"curated playlists or snippet libraries","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ability to write coaching notes in Jiminny through MCP","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"What we want to do for prompts/worflows and skills","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Capability","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Type","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"User value / job to be done","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Example Jiminny use 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Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Team visibility enforcement should be its own HIGH requirement. The current note under access control (\"ensure MCP only exposes data user has access to\") is doing too much work as a single line. Every MCP tool response (especially search_calls and deal-level aggregations) must be filtered through the same visibility engine as the UI. Semantic search is the riskiest here; it could surface results across team boundaries if not properly scoped.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"No data minimisation or scoping controls. We're exposing transcripts, CRM fields, contact details with job titles, deal context - significant PII and commercial data leaving the platform. Can customers configure which data types are exposed? Can transcripts be PII-stripped before they leave Jiminny? Given we position GDPR consent as a differentiator, we should be thinking about this.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"GDPR downstream processing is unaddressed. Piping transcripts to OpenAI/Gemini introduces new data processors. Customers need to acknowledge responsibility for downstream processing during setup. We should also consider whether call participants' recording consent covers onward sharing to third-party AI - it almost certainly doesn't by default. This is one for me to pick up.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Audit logs should go deeper. Current spec (what data, which system, when) is a start. Add: identity behind the key, specific queries/filters used, and failed access attempts (especially cross-visibility-boundary attempts). Enterprise compliance teams will expect this.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Downgrade handling. Doc says Scale tier and above, but doesn't say what happens on downgrade. Keys should auto-deactivate.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Happy to help draft a dedicated Security & Compliance section if useful.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Galya Dimitrova","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"15 Apr","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Thanks for the input @James Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Expand","depth":21,"bounds":{"left":0.46991357,"top":0.19313647,"width":0.013297873,"height":0.031923383},"role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXButton","text":"Listen","depth":21,"bounds":{"left":0.47523272,"top":0.3367917,"width":0.013297873,"height":0.031923383},"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXButton","text":"Show more options","depth":20,"bounds":{"left":0.47523272,"top":0.3312051,"width":0.013297873,"height":0.031923383},"role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Show thinking","depth":26,"bounds":{"left":0.3828125,"top":0.33918595,"width":0.030917553,"height":0.014764565},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"Gemini said","depth":20,"bounds":{"left":0.3804854,"top":0.38068634,"width":0.0003324468,"height":0.0007980846},"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Gemini said","depth":21,"bounds":{"left":0.3804854,"top":0.38268158,"width":0.04105718,"height":0.01915403},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Thank you for sharing the draft PRD for the","depth":24,"bounds":{"left":0.3695146,"top":0.39026338,"width":0.10521942,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny MCP Connector","depth":24,"bounds":{"left":0.3695146,"top":0.41101357,"width":0.0625,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":". It looks like a highly strategic initiative to transition Jiminny from a standalone platform into a foundational data layer for AI-driven revenue workflows.","depth":24,"bounds":{"left":0.3695146,"top":0.41101357,"width":0.11319814,"height":0.07861133},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Here is a quick breakdown of the document as it currently stands:","depth":24,"bounds":{"left":0.3695146,"top":0.5067837,"width":0.11635638,"height":0.037110932},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"Executive Summary","depth":23,"bounds":{"left":0.3695146,"top":0.56823623,"width":0.11635638,"height":0.01915403},"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Executive Summary","depth":24,"bounds":{"left":0.3695146,"top":0.5698324,"width":0.051363032,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Core Objective:","depth":26,"bounds":{"left":0.38115028,"top":0.5961692,"width":0.040392287,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Build an MCP-based AI Knowledge Connector to expose Jiminny data (transcripts, summaries, deal context) to external AI tools like Claude, OpenAI, and Gemini.","depth":26,"bounds":{"left":0.38115028,"top":0.5961692,"width":0.10472074,"height":0.09936153},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Target Value:","depth":26,"bounds":{"left":0.38115028,"top":0.70630485,"width":0.03374335,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Increases product stickiness, reduces manual data entry/copy-pasting, and achieves competitive parity with platforms like Gong and Avoma.","depth":26,"bounds":{"left":0.38115028,"top":0.70630485,"width":0.10305851,"height":0.07861133},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Key Capabilities:","depth":26,"bounds":{"left":0.38115028,"top":0.79569036,"width":0.043550532,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"* Call and deal-level data retrieval.","depth":26,"bounds":{"left":0.38115028,"top":0.79569036,"width":0.09458112,"height":0.037110932},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Semantic and keyword search.","depth":28,"bounds":{"left":0.39145613,"top":0.8435754,"width":0.073803194,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Pre-built prompts, workflows, and skills (e.g., deal inspection, objection analysis).","depth":28,"bounds":{"left":0.39145613,"top":0.8707103,"width":0.08976064,"height":0.057861134},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Current Bottlenecks & Discussions:","depth":26,"bounds":{"left":0.38115028,"top":0.9393456,"width":0.09142287,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"James Graham has flagged several critical security and compliance considerations (auth modeling, data minimization, key lifecycle, rate limiting, and GDPR downstream processing). Galya has integrated some of these into the technical requirements but pushed back on the GDPR point, noting that customers already export this data manually or via standard APIs.","depth":26,"bounds":{"left":0.38115028,"top":0.9393456,"width":0.10472074,"height":0.0606544},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Since you shared the document without a specific prompt, how would you like to proceed—would you like help drafting a response to Galya's GDPR question, refining the technical requirements based on James's feedback, or summarizing this into a pitch for stakeholders?","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXTextArea","text":"Enter a prompt for Gemini\nencrypted","depth":20,"bounds":{"left":0.37416887,"top":0.8104549,"width":0.10638298,"height":0.01915403},"value":"Enter a prompt for Gemini\nencrypted","help_text":"","role_description":"text entry area","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Enter a prompt for Gemini","depth":21,"help_text":"","role_description":"text","subrole":"AXUnknown"}]...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST [URL_WITH_CREDENTIALS] Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.
You said
Jiminny MCP Connector
Epic
Link to Epik in Jira
Document status
DRAFT
Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.
👤 Target user
Revenue teams using AI tools (Sales, CS, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
Build an AI Knowledge Connector (MCP-based) that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides structured insights (summaries, action items, scoring etc.)
Supports deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
Metric
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of MCP/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
User interaction and design
Simple setup flow:
Generate API key / connect MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus: usable by non-technical users
Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
Importance
Notes
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH
For each deal expose:
calls/meetings/emails
Identified risks
contacts related to the deal with their job title
linked account details
all CRM field information that we have in DI + fields used for CRM filling
example - get_deal_summary
request -
{
"tool": "get_deal_summary",
"arguments": {
"deal_name": "ACME"
}
}
responce:
{
"deal_name": "ACME",
"stage": "Negotiation",
"recent_activity": [
"Demo call on 2026-04-01",
"Pricing discussion on 2026-04-03"
],
"risks": [
"Pricing concerns",
"Security review pending"
], "next_steps": [
"Send follow-up to VP Sales",
"Share security documentation"
]
}
Ensure the AI can easily understand the schema of the data coming from Jiminny
HIGH
Models perform much better when tools have:
clear names
rich descriptions
predictable schemas
Provide clear, actionable error states for customers when MCP connections fail
HIGH
Error messages should guide agents toward solutions with specific suggestions and next steps.
Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”
Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”
Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”
Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”
Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”
Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”
No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”
Allow customers to securely control who and what data is accessible externally
HIGH
Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well.
Private meetings shouldn’t be exposed via MCP
HIGH
When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP.
Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows
HIGH
Ensure data is structured to be easily combined with:
CRM (accounts, deals)
external docs
Use consistent identifiers:
account IDs
deal IDs
MCP is available only for Scale tier and above
HIGH
Customers who are on Capture tier should be able to access any of their data through MCP.
This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working.
Provide visibility into how external AI tools access Jiminny data
HIGH
Audit logs:
what data was accessed by which external system when
identity - who made the request
specific queries/filters used
failed access attempts (especially cross-visibility-boundary attempts)
Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it.
example from Atlassian -
Provide customers with example prompts to get started quickly
HIGH
MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments.
See examples in table below.
Expose Jiminny as a Connector in Claude
medium
Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny
Provide customers with example workflows to get started quickly
medium
A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.
See examples in table below.
Allow customers to use predefined Jiminny skills for common workflows
medium
Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.
Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com
See examples in table below.
Jiminny UI widget for OpenAI
medium
The reason to have this is to make certain Jiminny use cases much easier than pure chat.
Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.
For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.
Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.
Allow customers to run long-running analyses asynchronously and retrieve progress/results
low
Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to:
start the job
let it run in the background
return a status like “in progress”
let the client come back for the result later
For example - User asks in Claude:
“Analyse all calls from this quarter and tell me the top 5 objections by segment.”
That might require:
searching lots of calls
pulling many summaries/transcripts
aggregating results
Instead of making Claude wait 45+ seconds, Jiminny could:
create an analysis job
return: job_id = 123, status = running
We need this in order to support:
large-scale analysis
multi-call aggregation across lots of data
playlist generation across many calls
heavy recap/report generation
Creating a snippet through the MCP
low
Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team.
example request:
{
"tool": "create_snippet",
"arguments": {
"call_id": "call_123",
"start_time": "00:18:24",
"end_time": "00:19:10",
"title": "Pricing objection"
}
}
example response:
{
"snippet_id": "snip_987",
"title": "Pricing objection",
"share_url": "https://app.jiminny.com/snippets/snip_987"
}
Ability to add recordings or snippets to a playlist
low
Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes
Allow AI tools to read reusable Jiminny resources
low
fathom
Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be:
current deal snapshot
account summary
weekly rep recap
approved talk tracks / objection handling guidance
curated playlists or snippet libraries
This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls.
Ability to write coaching notes in Jiminny through MCP
low
What we want to do for prompts/worflows and skills
Capability
Type
User value / job to be done
Example Jiminny use case
Inputs
Steps performed
Output
Draft follow-up email
Prompt
Help reps quickly create tailored follow-ups after calls
Use the latest customer call to draft an email with recap and next steps
Call ID
optional tone
optional recipient type
Retrieve call summary and action items from Jiminny.
Pull key points, commitments, and next steps.
Apply the chosen tone and structure.
Draft the email in a consistent format.
Customer-ready follow-up email draft
Summarise call
Prompt
Help users quickly extract the most important points from a conversation
Generate a concise summary of one call for internal or external use
Call ID
optional summary style
optional audience
Retrieve transcript, metadata, and structured insights.
Identify key topics, decisions, risks, and next steps.
Format output for the chosen audience.
Short or structured call summary with actions and risks
Generate deal recap
Prompt
Help reps and managers get a fast overview of a deal
Summarise the current state of a deal based on recent calls and linked CRM context
Deal ID or account name
optional date range
Retrieve calls and meetings linked to the deal.
Pull deal metadata and CRM-linked context.
Identify recent changes, blockers, risks, and next steps.
Summarise into a concise recap.
Deal recap with status, blockers, risks, and recommended next steps
Prepare weekly recap
Prompt
Help managers or reps review what changed over a time period
Weekly recap of calls, themes, and actions for one rep, team, or account
Rep ID
team ID or account ID
time period
Retrieve relevant calls and summaries for the selected time period.
Aggregate themes, action items, and changes.
Highlight notable risks, opportunities, and follow-ups.
Weekly recap with top themes, risks, opportunities, and actions
Prepare deal review
Workflow
Help reps and managers review deal status using multiple sources of context
Review a deal before pipeline review or forecast meeting
Deal ID
optional date range
Retrieve all recent calls, emails, and meetings tied to the deal.
Pull structured call summaries, score, action items, and key points.
Pull linked CRM context such as stage, contacts, and account details.
Aggregate open risks, unresolved objections, stakeholder gaps, and next steps.
Format into a review-ready structure.
Deal review document with current status, risks, stakeholders, blockers, and next steps
Weekly team recap
Workflow
Help managers monitor activity and risk across a group of reps or accounts
End-of-week team summary for sales leadership
Team ID
date range
Retrieve calls and structured insights across the team.
Group by rep, account, or deal.
Identify recurring themes, major risks, strong calls, and missed follow-ups.
Highlight calls or deals that need attention.
Team-level recap with patterns, key risks, notable wins, and action areas
Objection analysis across calls
Workflow
Help teams identify patterns across multiple conversations
Analyse pricing or competitor objections across recent calls
Account ID
deal ID
rep ID
keyword/topic
date range
Search calls matching the topic or keyword.
Retrieve summaries, transcripts, and snippets where relevant.
Identify recurring objections and how they were handled.
Assess whether objections were resolved or remain open.
Summarise patterns and examples.
Objection analysis with themes, frequency, examples, and suggested follow-up actions
Build onboarding playlist
Workflow
Help enablement teams curate useful learning content faster
Build a playlist of strong discovery, demo, or pricing calls for new hires
Topic
call type
rep/team filter
optional time period
Search for calls matching the selected criteria.
Rank or filter calls based on score, topics, or key moments.
Identify useful snippets or full recordings.
Add selected calls/snippets into a playlist structure.
Generate short descriptions for each item.
Curated onboarding playlist with selected calls/snippets and descriptions
Deal Inspection Skill
Skill
Give users a consistent, opinionated way to assess a deal
Claude reviews a deal using a repeatable Jiminny methodology
Deal ID or account name
optional review scope
Determine whether the request is suitable for a deal inspection.
Retrieve relevant calls, summaries, CRM context, stakeholders, and risks.
Apply a standard Jiminny review framework to assess deal health.
Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs.
Present findings in a consistent structure with recommended actions.
Structured deal inspection with health assessment, red flags, and recommendations
Manager Coaching Skill
Skill
Help managers review calls consistently and identify coaching moments
Analyse a rep’s call and surface strengths, gaps, and examples for coaching
Call ID
optional rep ID
optional coaching focus
Retrieve transcript, score, summary, and key moments from the call.
Evaluate the conversation against a coaching framework.
Identify what went well and where the rep could improve.
Suggest moments to clip as snippets.
Recommend coaching actions or playlist additions.
Coaching review with strengths, gaps, snippet suggestions, and recommended coaching actions
CSM / QBR Prep Skill
Skill
Help CS or AM teams prepare structured customer reviews from conversations
Generate an account review or QBR-style summary from recent customer interactions
Account ID, optional time period
Retrieve all recent calls, emails, and account-linked context.
Aggregate themes, requests, blockers, sentiment shifts, and opportunities.
Apply a repeatable account review structure.
Summarise outcomes, open issues, and suggested next steps.
Structured QBR/account review summary with themes, risks, opportunities, and actions
Snippet Builder Skill
Skill
Help users consistently capture and label useful moments from calls
Identify useful moments in a call and convert them into reusable snippets
Call ID, optional focus area such as pricing, objection, competitor, discovery
Retrieve transcript, recording timeline, and call insights.
Detect moments matching the requested focus area.
Propose snippet start/end times and titles.
Tag snippets by type or theme.
Optionally save them or add them to a playlist.
One or more proposed or saved snippets with title, timing, and tags
💡 Technical details
Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate.
Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users.
Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns.
Open Questions
Track unresolved issues or decisions that need to be made. Update as answers become available.
Question
Answer
Date Answered
Competitor research
Competitor
MCP Availability
Core Capabilities
Advanced Capabilities
Positioning
Key Strength
Key Weakness
Gong
✅ Mature MCP
List/search calls
Get transcripts
get Call metadata
Deal intelligence
Scorecards, analytics
CRM context
Enterprise data layer
Deep data + ecosystem integrations
Limited opinionated AI workflows
Avoma
✅ Mature MCP
list_meetings
get_meeting
get_transcript
key topics
Deal fields/Deal level analysis
Coaching/Scoring
CRM context
pushing insights into Notion / Confluence
AI-ready meeting intelligence
Strongest “outputs” layer
Mostly pull-based interactions
Fathom
✅ MCP
list/search meetings
get transcript
get summary/action items
Team/org context
Webhooks (event-based triggers)
get_weekly_recap
aggregated action items across meetings
creating webhooks directly from the MCP server
Lightweight meeting data access
Includes summaries + actions
Limited analytics depth
Grain
✅ Basic MCP
get meetings
download transcripts
Notes
Deals
Scorecards
Data access utility
Simple + accessible
Low sophistication, sometimes browser-based
Outreach
⚠️ MCP (workflow-focused)
Search sequences
Emails
Meetings (Kaia)
account / prospect / opportunity lookups
Workflow automation
Multi-system orchestration
Revenue workflow engine
End-to-end GTM workflows
Less depth in conversation data
Attention
⚠️ Emerging MCP
Meeting data
Transcripts
CRM-linked context
AI insights
Sales assistant workflows
create snippets
AI sales assistant + CRM
Strong CRM linkage
Less focus on external knowledge layer
Planhat -
Related content
Ask Jiminny Anything on Call level
Galya Dimitrova
Hubspot app
Galya Dimitrova
Product Vision & Strategy
Galya Dimitrova
Automated CRM Filling
Simona Georgieva (Deactivated)
Competitive analysis
Galya Dimitrova
AI agent ideas
Steliyan Georgiev
Comments
James Graham
9 Apr
A few areas around Security & Compliance that need more depth before this moves beyond draft:
Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate.
Team visibility enforcement should be its own HIGH requirement. The current note under access control ("ensure MCP only exposes data user has access to") is doing too much work as a single line. Every MCP tool response (especially search_calls and deal-level aggregations) must be filtered through the same visibility engine as the UI. Semantic search is the riskiest here; it could surface results across team boundaries if not properly scoped.
No data minimisation or scoping controls. We're exposing transcripts, CRM fields, contact details with job titles, deal context - significant PII and commercial data leaving the platform. Can customers configure which data types are exposed? Can transcripts be PII-stripped before they leave Jiminny? Given we position GDPR consent as a differentiator, we should be thinking about this.
GDPR downstream processing is unaddressed. Piping transcripts to OpenAI/Gemini introduces new data processors. Customers need to acknowledge responsibility for downstream processing during setup. We should also consider whether call participants' recording consent covers onward sharing to third-party AI - it almost certainly doesn't by default. This is one for me to pick up.
Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users.
Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns.
Audit logs should go deeper. Current spec (what data, which system, when) is a start. Add: identity behind the key, specific queries/filters used, and failed access attempts (especially cross-visibility-boundary attempts). Enterprise compliance teams will expect this.
Downgrade handling. Doc says Scale tier and above, but doesn't say what happens on downgrade. Keys should auto-deactivate.
Happy to help draft a dedicated Security & Compliance section i...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST [URL_WITH_CREDENTIALS] Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.
You said
Jiminny MCP Connector
Epic
Link to Epik in Jira
Document status
DRAFT
Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.
👤 Target user
Revenue teams using AI tools (Sales, CS, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
Build an AI Knowledge Connector (MCP-based) that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides structured insights (summaries, action items, scoring etc.)
Supports deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
Metric
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of MCP/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
User interaction and design
Simple setup flow:
Generate API key / connect MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus: usable by non-technical users
Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
Importance
Notes
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH
For each deal expose:
calls/meetings/emails
Identified risks
contacts related to the deal with their job title
linked account details
all CRM field information that we have in DI + fields used for CRM filling
example - get_deal_summary
request -
{
"tool": "get_deal_summary",
"arguments": {
"deal_name": "ACME"
}
}
responce:
{
"deal_name": "ACME",
"stage": "Negotiation",
"recent_activity": [
"Demo call on 2026-04-01",
"Pricing discussion on 2026-04-03"
],
"risks": [
"Pricing concerns",
"Security review pending"
], "next_steps": [
"Send follow-up to VP Sales",
"Share security documentation"
]
}
Ensure the AI can easily understand the schema of the data coming from Jiminny
HIGH
Models perform much better when tools have:
clear names
rich descriptions
predictable schemas
Provide clear, actionable error states for customers when MCP connections fail
HIGH
Error messages should guide agents toward solutions with specific suggestions and next steps.
Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”
Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”
Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”
Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”
Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”
Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”
No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”
Allow customers to securely control who and what data is accessible externally
HIGH
Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well.
Private meetings shouldn’t be exposed via MCP
HIGH
When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP.
Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows
HIGH
Ensure data is structured to be easily combined with:
CRM (accounts, deals)
external docs
Use consistent identifiers:
account IDs
deal IDs
MCP is available only for Scale tier and above
HIGH
Customers who are on Capture tier should be able to access any of their data through MCP.
This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working.
Provide visibility into how external AI tools access Jiminny data
HIGH
Audit logs:
what data was accessed by which external system when
identity - who made the request
specific queries/filters used
failed access attempts (especially cross-visibility-boundary attempts)
Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it.
example from Atlassian -
Provide customers with example prompts to get started quickly
HIGH
MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments.
See examples in table below.
Expose Jiminny as a Connector in Claude
medium
Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny
Provide customers with example workflows to get started quickly
medium
A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.
See examples in table below.
Allow customers to use predefined Jiminny skills for common workflows
medium
Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.
Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com
See examples in table below.
Jiminny UI widget for OpenAI
medium
The reason to have this is to make certain Jiminny use cases much easier than pure chat.
Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.
For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.
Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.
Allow customers to run long-running analyses asynchronously and retrieve progress/results
low
Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to:
start the job
let it run in the background
return a status like “in progress”
let the client come back for the result later
For example - User asks in Claude:
“Analyse all calls from this quarter and tell me the top 5 objections by segment.”
That might require:
searching lots of calls
pulling many summaries/transcripts
aggregating results
Instead of making Claude wait 45+ seconds, Jiminny could:
create an analysis job
return: job_id = 123, status = running
We need this in order to support:
large-scale analysis
multi-call aggregation across lots of data
playlist generation across many calls
heavy recap/report generation
Creating a snippet through the MCP
low
Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team.
example request:
{
"tool": "create_snippet",
"arguments": {
"call_id": "call_123",
"start_time": "00:18:24",
"end_time": "00:19:10",
"title": "Pricing objection"
}
}
example response:
{
"snippet_id": "snip_987",
"title": "Pricing objection",
"share_url": "https://app.jiminny.com/snippets/snip_987"
}
Ability to add recordings or snippets to a playlist
low
Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes
Allow AI tools to read reusable Jiminny resources
low
fathom
Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be:
current deal snapshot
account summary
weekly rep recap
approved talk tracks / objection handling guidance
curated playlists or snippet libraries
This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls.
Ability to write coaching notes in Jiminny through MCP
low
What we want to do for prompts/worflows and skills
Capability
Type
User value / job to be done
Example Jiminny use case
Inputs
Steps performed
Output
Draft follow-up email
Prompt
Help reps quickly create tailored follow-ups after calls
Use the latest customer call to draft an email with recap and next steps
Call ID
optional tone
optional recipient type
Retrieve call summary and action items from Jiminny.
Pull key points, commitments, and next steps.
Apply the chosen tone and structure.
Draft the email in a consistent format.
Customer-ready follow-up email draft
Summarise call
Prompt
Help users quickly extract the most important points from a conversation
Generate a concise summary of one call for internal or external use
Call ID
optional summary style
optional audience
Retrieve transcript, metadata, and structured insights.
Identify key topics, decisions, risks, and next steps.
Format output for the chosen audience.
Short or structured call summary with actions and risks
Generate deal recap
Prompt
Help reps and managers get a fast overview of a deal
Summarise the current state of a deal based on recent calls and linked CRM context
Deal ID or account name
optional date range
Retrieve calls and meetings linked to the deal.
Pull deal metadata and CRM-linked context.
Identify recent changes, blockers, risks, and next steps.
Summarise into a concise recap.
Deal recap with status, blockers, risks, and recommended next steps
Prepare weekly recap
Prompt
Help managers or reps review what changed over a time period
Weekly recap of calls, themes, and actions for one rep, team, or account
Rep ID
team ID or account ID
time period
Retrieve relevant calls and summaries for the selected time period.
Aggregate themes, action items, and changes.
Highlight notable risks, opportunities, and follow-ups.
Weekly recap with top themes, risks, opportunities, and actions
Prepare deal review
Workflow
Help reps and managers review deal status using multiple sources of context
Review a deal before pipeline review or forecast meeting
Deal ID
optional date range
Retrieve all recent calls, emails, and meetings tied to the deal.
Pull structured call summaries, score, action items, and key points.
Pull linked CRM context such as stage, contacts, and account details.
Aggregate open risks, unresolved objections, stakeholder gaps, and next steps.
Format into a review-ready structure.
Deal review document with current status, risks, stakeholders, blockers, and next steps
Weekly team recap
Workflow
Help managers monitor activity and risk across a group of reps or accounts
End-of-week team summary for sales leadership
Team ID
date range
Retrieve calls and structured insights across the team.
Group by rep, account, or deal.
Identify recurring themes, major risks, strong calls, and missed follow-ups.
Highlight calls or deals that need attention.
Team-level recap with patterns, key risks, notable wins, and action areas
Objection analysis across calls
Workflow
Help teams identify patterns across multiple conversations
Analyse pricing or competitor objections across recent calls
Account ID
deal ID
rep ID
keyword/topic
date range
Search calls matching the topic or keyword.
Retrieve summaries, transcripts, and snippets where relevant.
Identify recurring objections and how they were handled.
Assess whether objections were resolved or remain open.
Summarise patterns and examples.
Objection analysis with themes, frequency, examples, and suggested follow-up actions
Build onboarding playlist
Workflow
Help enablement teams curate useful learning content faster
Build a playlist of strong discovery, demo, or pricing calls for new hires
Topic
call type
rep/team filter
optional time period
Search for calls matching the selected criteria.
Rank or filter calls based on score, topics, or key moments.
Identify useful snippets or full recordings.
Add selected calls/snippets into a playlist structure.
Generate short descriptions for each item.
Curated onboarding playlist with selected calls/snippets and descriptions
Deal Inspection Skill
Skill
Give users a consistent, opinionated way to assess a deal
Claude reviews a deal using a repeatable Jiminny methodology
Deal ID or account name
optional review scope
Determine whether the request is suitable for a deal inspection.
Retrieve relevant calls, summaries, CRM context, stakeholders, and risks.
Apply a standard Jiminny review framework to assess deal health.
Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs.
Present findings in a consistent structure with recommended actions.
Structured deal inspection with health assessment, red flags, and recommendations
Manager Coaching Skill
Skill
Help managers review calls consistently and identify coaching moments
Analyse a rep’s call and surface strengths, gaps, and examples for coaching
Call ID
optional rep ID
optional coaching focus
Retrieve transcript, score, summary, and key moments from the call.
Evaluate the conversation against a coaching framework.
Identify what went well and where the rep could improve.
Suggest moments to clip as snippets.
Recommend coaching actions or playlist additions.
Coaching review with strengths, gaps, snippet suggestions, and recommended coaching actions
CSM / QBR Prep Skill
Skill
Help CS or AM teams prepare structured customer reviews from conversations
Generate an account review or QBR-style summary from recent customer interactions
Account ID, optional time period
Retrieve all recent calls, emails, and account-linked context.
Aggregate themes, requests, blockers, sentiment shifts, and opportunities.
Apply a repeatable account review structure.
Summarise outcomes, open issues, and suggested next steps.
Structured QBR/account review summary with themes, risks, opportunities, and actions
Snippet Builder Skill
Skill
Help users consistently capture and label useful moments from calls
Identify useful moments in a call and convert them into reusable snippets
Call ID, optional focus area such as pricing, objection, competitor, discovery
Retrieve transcript, recording timeline, and call insights.
Detect moments matching the requested focus area.
Propose snippet start/end times and titles.
Tag snippets by type or theme.
Optionally save them or add them to a playlist.
One or more proposed or saved snippets with title, timing, and tags
💡 Technical details
Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate.
Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users.
Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns.
Open Questions
Track unresolved issues or decisions that need to be made. Update as answers become available.
Question
Answer
Date Answered
Competitor research
Competitor
MCP Availability
Core Capabilities
Advanced Capabilities
Positioning
Key Strength
Key Weakness
Gong
✅ Mature MCP
List/search calls
Get transcripts
get Call metadata
Deal intelligence
Scorecards, analytics
CRM context
Enterprise data layer
Deep data + ecosystem integrations
Limited opinionated AI workflows
Avoma
✅ Mature MCP
list_meetings
get_meeting
get_transcript
key topics
Deal fields/Deal level analysis
Coaching/Scoring
CRM context
pushing insights into Notion / Confluence
AI-ready meeting intelligence
Strongest “outputs” layer
Mostly pull-based interactions
Fathom
✅ MCP
list/search meetings
get transcript
get summary/action items
Team/org context
Webhooks (event-based triggers)
get_weekly_recap
aggregated action items across meetings
creating webhooks directly from the MCP server
Lightweight meeting data access
Includes summaries + actions
Limited analytics depth
Grain
✅ Basic MCP
get meetings
download transcripts
Notes
Deals
Scorecards
Data access utility
Simple + accessible
Low sophistication, sometimes browser-based
Outreach
⚠️ MCP (workflow-focused)
Search sequences
Emails
Meetings (Kaia)
account / prospect / opportunity lookups
Workflow automation
Multi-system orchestration
Revenue workflow engine
End-to-end GTM workflows
Less depth in conversation data
Attention
⚠️ Emerging MCP
Meeting data
Transcripts
CRM-linked context
AI insights
Sales assistant workflows
create snippets
AI sales assistant + CRM
Strong CRM linkage
Less focus on external knowledge layer
Planhat -
Related content
Ask Jiminny Anything on Call level
Galya Dimitrova
Hubspot app
Galya Dimitrova
Product Vision & Strategy
Galya Dimitrova
Automated CRM Filling
Simona Georgieva (Deactivated)
Competitive analysis
Galya Dimitrova
AI agent ideas
Steliyan Georgiev
Comments
James Graham
9 Apr
A few areas around Security & Compliance that need more depth before this moves beyond draft:
Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate.
Team visibility enforcement should be its own HIGH requirement. The current note under access control ("ensure MCP only exposes data user has access to") is doing too much work as a single line. Every MCP tool response (especially search_calls and deal-level aggregations) must be filtered through the same visibility engine as the UI. Semantic search is the riskiest here; it could surface results across team boundaries if not properly scoped.
No data minimisation or scoping controls. We're exposing transcripts, CRM fields, contact details with job titles, deal context - significant PII and commercial data leaving the platform. Can customers configure which data types are exposed? Can transcripts be PII-stripped before they leave Jiminny? Given we position GDPR consent as a differentiator, we should be thinking about this.
GDPR downstream processing is unaddressed. Piping transcripts to OpenAI/Gemini introduces new data processors. Customers need to acknowledge responsibility for downstream processing during setup. We should also consider whether call participants' recording consent covers onward sharing to third-party AI - it almost certainly doesn't by default. This is one for me to pick up.
Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users.
Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns.
Audit logs should go deeper. Current spec (what data, which system, when) is a start. Add: identity behind the key, specific queries/filters used, and failed access attempts (especially cross-visibility-boundary attempts). Enterprise compliance teams will expect this.
Downgrade handling. Doc says Scale tier and above, but doesn't say what happens on downgrade. Keys should auto-deactivate.
Happy to help draft a dedicated Security & Compliance section i...
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Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product. 👤 Target user Revenue teams using AI tools (Sales, CS, RevOps) Companies already experimenting with Claude / OpenAI Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc) 🤕 Pain point or problem Jiminny data is locked in the platform AI tools lack access to high-value conversation context Customers must manually export/copy transcripts No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people Peer pressure as almost all competitors have this 💥 Impact and benefits For customers Better AI outputs using real customer conversations Ability to build automated workflows across tools For Jiminny Increased product stickiness (embedded in workflows) Competitive parity with Gong / Avoma etc. 💡 Solution ideas Build an AI Knowledge Connector (MCP-based) that: Exposes Jiminny data (calls, transcripts, metadata) to AI tools Provides structured insights (summaries, action items, scoring etc.) Supports deal-level context Allows integration into customer-owned AI stacks (Claude + Zapier, etc.) Success metrics List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets). Goal Metric Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows % of customers connecting external AI tools Number of MCP/API calls per customer - and which ones are being used the most Decrease the manual copying of transcripts decrease by X % User interaction and design Simple setup flow: Generate API key / connect MCP Clear instructions for Claude / Zapier setup Provide: Example prompts for Claude Pre-built workflow templates for Zapier Clear documentation - with use cases 👉 Focus: usable by non-technical users Detailed Requirements List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification. Requirement Importance Notes Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI) HIGH Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack Allow customers to access call data through AI tools HIGH Expose Jiminny data: list calls (filter by date, user, account, deal) get call get transcript (speaker-separated) get call metadata search calls (keyword + semantic) Support filtering by: account / contact/ lead / deal jiminny user date range Allow customers to search and query calls using AI tools HIGH Support keyword and semantic search across conversations Provide structured insights so customers can generate better outputs HIGH For each call, expose: Summary Action items Key points Activity type Notes Call Score CRM information Allow customers to understand deal status across multiple calls/emails HIGH For each deal expose: calls/meetings/emails Identified risks contacts related to the deal with their job title linked account details all CRM field information that we have in DI + fields used for CRM filling example - get_deal_summary request - { \"tool\": \"get_deal_summary\", \"arguments\": { \"deal_name\": \"ACME\" } } responce: { \"deal_name\": \"ACME\", \"stage\": \"Negotiation\", \"recent_activity\": [ \"Demo call on 2026-04-01\", \"Pricing discussion on 2026-04-03\" ], \"risks\": [ \"Pricing concerns\", \"Security review pending\" ], \"next_steps\": [ \"Send follow-up to VP Sales\", \"Share security documentation\" ] } Ensure the AI can easily understand the schema of the data coming from Jiminny HIGH Models perform much better when tools have: clear names rich descriptions predictable schemas Provide clear, actionable error states for customers when MCP connections fail HIGH Error messages should guide agents toward solutions with specific suggestions and next steps. Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.” Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.” Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.” Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.” Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.” Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.” No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.” Allow customers to securely control who and what data is accessible externally HIGH Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well. Private meetings shouldn’t be exposed via MCP HIGH When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP. Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows HIGH Ensure data is structured to be easily combined with: CRM (accounts, deals) external docs Use consistent identifiers: account IDs deal IDs MCP is available only for Scale tier and above HIGH Customers who are on Capture tier should be able to access any of their data through MCP. This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working. Provide visibility into how external AI tools access Jiminny data HIGH Audit logs: what data was accessed by which external system when identity - who made the request specific queries/filters used failed access attempts (especially cross-visibility-boundary attempts) Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it. example from Atlassian - Provide customers with example prompts to get started quickly HIGH MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments. See examples in table below. Expose Jiminny as a Connector in Claude medium Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny Provide customers with example workflows to get started quickly medium A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome. See examples in table below. Allow customers to use predefined Jiminny skills for common workflows medium Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job. Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com See examples in table below. Jiminny UI widget for OpenAI medium The reason to have this is to make certain Jiminny use cases much easier than pure chat. Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it. For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface. Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section. Allow customers to run long-running analyses asynchronously and retrieve progress/results low Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to: start the job let it run in the background return a status like “in progress” let the client come back for the result later For example - User asks in Claude: “Analyse all calls from this quarter and tell me the top 5 objections by segment.” That might require: searching lots of calls pulling many summaries/transcripts aggregating results Instead of making Claude wait 45+ seconds, Jiminny could: create an analysis job return: job_id = 123, status = running We need this in order to support: large-scale analysis multi-call aggregation across lots of data playlist generation across many calls heavy recap/report generation Creating a snippet through the MCP low Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team. example request: { \"tool\": \"create_snippet\", \"arguments\": { \"call_id\": \"call_123\", \"start_time\": \"00:18:24\", \"end_time\": \"00:19:10\", \"title\": \"Pricing objection\" } } example response: { \"snippet_id\": \"snip_987\", \"title\": \"Pricing objection\", \"share_url\": \"https://app.jiminny.com/snippets/snip_987\" } Ability to add recordings or snippets to a playlist low Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes Allow AI tools to read reusable Jiminny resources low fathom Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be: current deal snapshot account summary weekly rep recap approved talk tracks / objection handling guidance curated playlists or snippet libraries This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls. Ability to write coaching notes in Jiminny through MCP low What we want to do for prompts/worflows and skills Capability Type User value / job to be done Example Jiminny use case Inputs Steps performed Output Draft follow-up email Prompt Help reps quickly create tailored follow-ups after calls Use the latest customer call to draft an email with recap and next steps Call ID optional tone optional recipient type Retrieve call summary and action items from Jiminny. Pull key points, commitments, and next steps. Apply the chosen tone and structure. Draft the email in a consistent format. Customer-ready follow-up email draft Summarise call Prompt Help users quickly extract the most important points from a conversation Generate a concise summary of one call for internal or external use Call ID optional summary style optional audience Retrieve transcript, metadata, and structured insights. Identify key topics, decisions, risks, and next steps. Format output for the chosen audience. Short or structured call summary with actions and risks Generate deal recap Prompt Help reps and managers get a fast overview of a deal Summarise the current state of a deal based on recent calls and linked CRM context Deal ID or account name optional date range Retrieve calls and meetings linked to the deal. Pull deal metadata and CRM-linked context. Identify recent changes, blockers, risks, and next steps. Summarise into a concise recap. Deal recap with status, blockers, risks, and recommended next steps Prepare weekly recap Prompt Help managers or reps review what changed over a time period Weekly recap of calls, themes, and actions for one rep, team, or account Rep ID team ID or account ID time period Retrieve relevant calls and summaries for the selected time period. Aggregate themes, action items, and changes. Highlight notable risks, opportunities, and follow-ups. Weekly recap with top themes, risks, opportunities, and actions Prepare deal review Workflow Help reps and managers review deal status using multiple sources of context Review a deal before pipeline review or forecast meeting Deal ID optional date range Retrieve all recent calls, emails, and meetings tied to the deal. Pull structured call summaries, score, action items, and key points. Pull linked CRM context such as stage, contacts, and account details. Aggregate open risks, unresolved objections, stakeholder gaps, and next steps. Format into a review-ready structure. Deal review document with current status, risks, stakeholders, blockers, and next steps Weekly team recap Workflow Help managers monitor activity and risk across a group of reps or accounts End-of-week team summary for sales leadership Team ID date range Retrieve calls and structured insights across the team. Group by rep, account, or deal. Identify recurring themes, major risks, strong calls, and missed follow-ups. Highlight calls or deals that need attention. Team-level recap with patterns, key risks, notable wins, and action areas Objection analysis across calls Workflow Help teams identify patterns across multiple conversations Analyse pricing or competitor objections across recent calls Account ID deal ID rep ID keyword/topic date range Search calls matching the topic or keyword. Retrieve summaries, transcripts, and snippets where relevant. Identify recurring objections and how they were handled. Assess whether objections were resolved or remain open. Summarise patterns and examples. Objection analysis with themes, frequency, examples, and suggested follow-up actions Build onboarding playlist Workflow Help enablement teams curate useful learning content faster Build a playlist of strong discovery, demo, or pricing calls for new hires Topic call type rep/team filter optional time period Search for calls matching the selected criteria. Rank or filter calls based on score, topics, or key moments. Identify useful snippets or full recordings. Add selected calls/snippets into a playlist structure. Generate short descriptions for each item. Curated onboarding playlist with selected calls/snippets and descriptions Deal Inspection Skill Skill Give users a consistent, opinionated way to assess a deal Claude reviews a deal using a repeatable Jiminny methodology Deal ID or account name optional review scope Determine whether the request is suitable for a deal inspection. Retrieve relevant calls, summaries, CRM context, stakeholders, and risks. Apply a standard Jiminny review framework to assess deal health. Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs. Present findings in a consistent structure with recommended actions. Structured deal inspection with health assessment, red flags, and recommendations Manager Coaching Skill Skill Help managers review calls consistently and identify coaching moments Analyse a rep’s call and surface strengths, gaps, and examples for coaching Call ID optional rep ID optional coaching focus Retrieve transcript, score, summary, and key moments from the call. Evaluate the conversation against a coaching framework. Identify what went well and where the rep could improve. Suggest moments to clip as snippets. Recommend coaching actions or playlist additions. Coaching review with strengths, gaps, snippet suggestions, and recommended coaching actions CSM / QBR Prep Skill Skill Help CS or AM teams prepare structured customer reviews from conversations Generate an account review or QBR-style summary from recent customer interactions Account ID, optional time period Retrieve all recent calls, emails, and account-linked context. Aggregate themes, requests, blockers, sentiment shifts, and opportunities. Apply a repeatable account review structure. Summarise outcomes, open issues, and suggested next steps. Structured QBR/account review summary with themes, risks, opportunities, and actions Snippet Builder Skill Skill Help users consistently capture and label useful moments from calls Identify useful moments in a call and convert them into reusable snippets Call ID, optional focus area such as pricing, objection, competitor, discovery Retrieve transcript, recording timeline, and call insights. Detect moments matching the requested focus area. Propose snippet start/end times and titles. Tag snippets by type or theme. Optionally save them or add them to a playlist. One or more proposed or saved snippets with title, timing, and tags 💡 Technical details Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate. Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users. Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns. Open Questions Track unresolved issues or decisions that need to be made. Update as answers become available. Question Answer Date Answered Competitor research Competitor MCP Availability Core Capabilities Advanced Capabilities Positioning Key Strength Key Weakness Gong ✅ Mature MCP List/search calls Get transcripts get Call metadata Deal intelligence Scorecards, analytics CRM context Enterprise data layer Deep data + ecosystem integrations Limited opinionated AI workflows Avoma ✅ Mature MCP list_meetings get_meeting get_transcript key topics Deal fields/Deal level analysis Coaching/Scoring CRM context pushing insights into Notion / Confluence AI-ready meeting intelligence Strongest “outputs” layer Mostly pull-based interactions Fathom ✅ MCP list/search meetings get transcript get summary/action items Team/org context Webhooks (event-based triggers) get_weekly_recap aggregated action items across meetings creating webhooks directly from the MCP server Lightweight meeting data access Includes summaries + actions Limited analytics depth Grain ✅ Basic MCP get meetings download transcripts Notes Deals Scorecards Data access utility Simple + accessible Low sophistication, sometimes browser-based Outreach ⚠️ MCP (workflow-focused) Search sequences Emails Meetings (Kaia) account / prospect / opportunity lookups Workflow automation Multi-system orchestration Revenue workflow engine End-to-end GTM workflows Less depth in conversation data Attention ⚠️ Emerging MCP Meeting data Transcripts CRM-linked context AI insights Sales assistant workflows create snippets AI sales assistant + CRM Strong CRM linkage Less focus on external knowledge layer Planhat - Related content Ask Jiminny Anything on Call level Galya Dimitrova Hubspot app Galya Dimitrova Product Vision & Strategy Galya Dimitrova Automated CRM Filling Simona Georgieva (Deactivated) Competitive analysis Galya Dimitrova AI agent ideas Steliyan Georgiev Comments James Graham 9 Apr A few areas around Security & Compliance that need more depth before this moves beyond draft: Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate. Team visibility enforcement should be its own HIGH requirement. The current note under access control (\"ensure MCP only exposes data user has access to\") is doing too much work as a single line. Every MCP tool response (especially search_calls and deal-level aggregations) must be filtered through the same visibility engine as the UI. Semantic search is the riskiest here; it could surface results across team boundaries if not properly scoped. No data minimisation or scoping controls. We're exposing transcripts, CRM fields, contact details with job titles, deal context - significant PII and commercial data leaving the platform. Can customers configure which data types are exposed? Can transcripts be PII-stripped before they leave Jiminny? Given we position GDPR consent as a differentiator, we should be thinking about this. GDPR downstream processing is unaddressed. Piping transcripts to OpenAI/Gemini introduces new data processors. Customers need to acknowledge responsibility for downstream processing during setup. We should also consider whether call participants' recording consent covers onward sharing to third-party AI - it almost certainly doesn't by default. This is one for me to pick up. Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users. Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns. Audit logs should go deeper. Current spec (what data, which system, when) is a start. Add: identity behind the key, specific queries/filters used, and failed access attempts (especially cross-visibility-boundary attempts). Enterprise compliance teams will expect this. Downgrade handling. Doc says Scale tier and above, but doesn't say what happens on downgrade. Keys should auto-deactivate. Happy to help draft a dedicated Security & Compliance section if useful. Galya Dimitrova 15 Apr Thanks for the input @James Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. 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each deal expose:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"calls/meetings/emails","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Identified risks","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"contacts related to the deal with their job title","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"linked account details","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"all CRM field information that we have in DI + fields used for CRM filling","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"example - get_deal_summary","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"request 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The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Trying to access private meeting - “Error: Access denied. 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This is not a data or permissions issue — please retry in a few minutes.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to securely control who and what data is accessible externally","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ensure MCP only exposes data user has access to - based on team visibility settings. 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This will make it easier for customers to find us and to connect Jiminny","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide customers with example workflows to get started quickly","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"See examples in table below.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to use predefined Jiminny skills for common workflows","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"See examples in table below.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny UI widget for OpenAI","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The reason to have this is to make certain Jiminny use cases much easier than pure chat.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to run long-running analyses asynchronously and retrieve progress/results","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Some requests/questions require analysis of Hugh volumes of data. 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Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"example request:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"{","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"tool\": \"create_snippet\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"arguments\": {","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"call_id\": \"call_123\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"start_time\": \"00:18:24\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"end_time\": \"00:19:10\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"title\": \"Pricing objection\"","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"example response:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"{","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"snippet_id\": \"snip_987\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"title\": \"Pricing objection\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"share_url\": \"https://app.jiminny.com/snippets/snip_987\"","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ability to add recordings or snippets to a playlist","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow AI tools to read reusable Jiminny resources","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"fathom","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Resources are useful for data that should be read consistently rather than recomputed every time. 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calls.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ability to write coaching notes in Jiminny through MCP","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"What we want to do for prompts/worflows and skills","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Capability","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Type","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"User value / job to be done","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Example Jiminny use 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Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Team visibility enforcement should be its own HIGH requirement. The current note under access control (\"ensure MCP only exposes data user has access to\") is doing too much work as a single line. Every MCP tool response (especially search_calls and deal-level aggregations) must be filtered through the same visibility engine as the UI. 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We should also consider whether call participants' recording consent covers onward sharing to third-party AI - it almost certainly doesn't by default. This is one for me to pick up.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. 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Keys should auto-deactivate.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Happy to help draft a dedicated Security & Compliance section if useful.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Galya Dimitrova","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"15 Apr","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Thanks for the input @James Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Expand","depth":21,"role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXButton","text":"Listen","depth":21,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false}]...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST [URL_WITH_CREDENTIALS] Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.
You said
Jiminny MCP Connector
Epic
Link to Epik in Jira
Document status
DRAFT
Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.
👤 Target user
Revenue teams using AI tools (Sales, CS, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
Build an AI Knowledge Connector (MCP-based) that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides structured insights (summaries, action items, scoring etc.)
Supports deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
Metric
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of MCP/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
User interaction and design
Simple setup flow:
Generate API key / connect MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus: usable by non-technical users
Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
Importance
Notes
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH
For each deal expose:
calls/meetings/emails
Identified risks
contacts related to the deal with their job title
linked account details
all CRM field information that we have in DI + fields used for CRM filling
example - get_deal_summary
request -
{
"tool": "get_deal_summary",
"arguments": {
"deal_name": "ACME"
}
}
responce:
{
"deal_name": "ACME",
"stage": "Negotiation",
"recent_activity": [
"Demo call on 2026-04-01",
"Pricing discussion on 2026-04-03"
],
"risks": [
"Pricing concerns",
"Security review pending"
], "next_steps": [
"Send follow-up to VP Sales",
"Share security documentation"
]
}
Ensure the AI can easily understand the schema of the data coming from Jiminny
HIGH
Models perform much better when tools have:
clear names
rich descriptions
predictable schemas
Provide clear, actionable error states for customers when MCP connections fail
HIGH
Error messages should guide agents toward solutions with specific suggestions and next steps.
Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”
Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”
Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”
Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”
Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”
Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”
No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”
Allow customers to securely control who and what data is accessible externally
HIGH
Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well.
Private meetings shouldn’t be exposed via MCP
HIGH
When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP.
Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows
HIGH
Ensure data is structured to be easily combined with:
CRM (accounts, deals)
external docs
Use consistent identifiers:
account IDs
deal IDs
MCP is available only for Scale tier and above
HIGH
Customers who are on Capture tier should be able to access any of their data through MCP.
This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working.
Provide visibility into how external AI tools access Jiminny data
HIGH
Audit logs:
what data was accessed by which external system when
identity - who made the request
specific queries/filters used
failed access attempts (especially cross-visibility-boundary attempts)
Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it.
example from Atlassian -
Provide customers with example prompts to get started quickly
HIGH
MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments.
See examples in table below.
Expose Jiminny as a Connector in Claude
medium
Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny
Provide customers with example workflows to get started quickly
medium
A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.
See examples in table below.
Allow customers to use predefined Jiminny skills for common workflows
medium
Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.
Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com
See examples in table below.
Jiminny UI widget for OpenAI
medium
The reason to have this is to make certain Jiminny use cases much easier than pure chat.
Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.
For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.
Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.
Allow customers to run long-running analyses asynchronously and retrieve progress/results
low
Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to:
start the job
let it run in the background
return a status like “in progress”
let the client come back for the result later
For example - User asks in Claude:
“Analyse all calls from this quarter and tell me the top 5 objections by segment.”
That might require:
searching lots of calls
pulling many summaries/transcripts
aggregating results
Instead of making Claude wait 45+ seconds, Jiminny could:
create an analysis job
return: job_id = 123, status = running
We need this in order to support:
large-scale analysis
multi-call aggregation across lots of data
playlist generation across many calls
heavy recap/report generation
Creating a snippet through the MCP
low
Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team.
example request:
{
"tool": "create_snippet",
"arguments": {
"call_id": "call_123",
"start_time": "00:18:24",
"end_time": "00:19:10",
"title": "Pricing objection"
}
}
example response:
{
"snippet_id": "snip_987",
"title": "Pricing objection",
"share_url": "https://app.jiminny.com/snippets/snip_987"
}
Ability to add recordings or snippets to a playlist
low
Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes
Allow AI tools to read reusable Jiminny resources
low
fathom
Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be:
current deal snapshot
account summary
weekly rep recap
approved talk tracks / objection handling guidance
curated playlists or snippet libraries
This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls.
Ability to write coaching notes in Jiminny through MCP
low
What we want to do for prompts/worflows and skills
Capability
Type
User value / job to be done
Example Jiminny use case
Inputs
Steps performed
Output
Draft follow-up email
Prompt
Help reps quickly create tailored follow-ups after calls
Use the latest customer call to draft an email with recap and next steps
Call ID
optional tone
optional recipient type
Retrieve call summary and action items from Jiminny.
Pull key points, commitments, and next steps.
Apply the chosen tone and structure.
Draft the email in a consistent format.
Customer-ready follow-up email draft
Summarise call
Prompt
Help users quickly extract the most important points from a conversation
Generate a concise summary of one call for internal or external use
Call ID
optional summary style
optional audience
Retrieve transcript, metadata, and structured insights.
Identify key topics, decisions, risks, and next steps.
Format output for the chosen audience.
Short or structured call summary with actions and risks
Generate deal recap
Prompt
Help reps and managers get a fast overview of a deal
Summarise the current state of a deal based on recent calls and linked CRM context
Deal ID or account name
optional date range
Retrieve calls and meetings linked to the deal.
Pull deal metadata and CRM-linked context.
Identify recent changes, blockers, risks, and next steps.
Summarise into a concise recap.
Deal recap with status, blockers, risks, and recommended next steps
Prepare weekly recap
Prompt
Help managers or reps review what changed over a time period
Weekly recap of calls, themes, and actions for one rep, team, or account
Rep ID
team ID or account ID
time period
Retrieve relevant calls and summaries for the selected time period.
Aggregate themes, action items, and changes.
Highlight notable risks, opportunities, and follow-ups.
Weekly recap with top themes, risks, opportunities, and actions
Prepare deal review
Workflow
Help reps and managers review deal status using multiple sources of context
Review a deal before pipeline review or forecast meeting
Deal ID
optional date range
Retrieve all recent calls, emails, and meetings tied to the deal.
Pull structured call summaries, score, action items, and key points.
Pull linked CRM context such as stage, contacts, and account details.
Aggregate open risks, unresolved objections, stakeholder gaps, and next steps.
Format into a review-ready structure.
Deal review document with current status, risks, stakeholders, blockers, and next steps
Weekly team recap
Workflow
Help managers monitor activity and risk across a group of reps or accounts
End-of-week team summary for sales leadership
Team ID
date range
Retrieve calls and structured insights across the team.
Group by rep, account, or deal.
Identify recurring themes, major risks, strong calls, and missed follow-ups.
Highlight calls or deals that need attention.
Team-level recap with patterns, key risks, notable wins, and action areas
Objection analysis across calls
Workflow
Help teams identify patterns across multiple conversations
Analyse pricing or competitor objections across recent calls
Account ID
deal ID
rep ID
keyword/topic
date range
Search calls matching the topic or keyword.
Retrieve summaries, transcripts, and snippets where relevant.
Identify recurring objections and how they were handled.
Assess whether objections were resolved or remain open.
Summarise patterns and examples.
Objection analysis with themes, frequency, examples, and suggested follow-up actions
Build onboarding playlist
Workflow
Help enablement teams curate useful learning content faster
Build a playlist of strong discovery, demo, or pricing calls for new hires
Topic
call type
rep/team filter
optional time period
Search for calls matching the selected criteria.
Rank or filter calls based on score, topics, or key moments.
Identify useful snippets or full recordings.
Add selected calls/snippets into a playlist structure.
Generate short descriptions for each item.
Curated onboarding playlist with selected calls/snippets and descriptions
Deal Inspection Skill
Skill
Give users a consistent, opinionated way to assess a deal
Claude reviews a deal using a repeatable Jiminny methodology
Deal ID or account name
optional review scope
Determine whether the request is suitable for a deal inspection.
Retrieve relevant calls, summaries, CRM context, stakeholders, and risks.
Apply a standard Jiminny review framework to assess deal health.
Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs.
Present findings in a consistent structure with recommended actions.
Structured deal inspection with health assessment, red flags, and recommendations
Manager Coaching Skill
Skill
Help managers review calls consistently and identify coaching moments
Analyse a rep’s call and surface strengths, gaps, and examples for coaching
Call ID
optional rep ID
optional coaching focus
Retrieve transcript, score, summary, and key moments from the call.
Evaluate the conversation against a coaching framework.
Identify what went well and where the rep could improve.
Suggest moments to clip as snippets.
Recommend coaching actions or playlist additions.
Coaching review with strengths, gaps, snippet suggestions, and recommended coaching actions
CSM / QBR Prep Skill
Skill
Help CS or AM teams prepare structured customer reviews from conversations
Generate an account review or QBR-style summary from recent customer interactions
Account ID, optional time period
Retrieve all recent calls, emails, and account-linked context.
Aggregate themes, requests, blockers, sentiment shifts, and opportunities.
Apply a repeatable account review structure.
Summarise outcomes, open issues, and suggested next steps.
Structured QBR/account review summary with themes, risks, opportunities, and actions
Snippet Builder Skill
Skill
Help users consistently capture and label useful moments from calls
Identify useful moments in a call and convert them into reusable snippets
Call ID, optional focus area such as pricing, objection, competitor, discovery
Retrieve transcript, recording timeline, and call insights.
Detect moments matching the requested focus area.
Propose snippet start/end times and titles.
Tag snippets by type or theme.
Optionally save them or add them to a playlist.
One or more proposed or saved snippets with title, timing, and tags
💡 Technical details
Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate.
Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users.
Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns.
Open Questions
Track unresolved issues or decisions that need to be made. Update as answers become available.
Question
Answer
Date Answered
Competitor research
Competitor
MCP Availability
Core Capabilities
Advanced Capabilities
Positioning
Key Strength
Key Weakness
Gong
✅ Mature MCP
List/search calls
Get transcripts
get Call metadata
Deal intelligence
Scorecards, analytics
CRM context
Enterprise data layer
Deep data + ecosystem integrations
Limited opinionated AI workflows
Avoma
✅ Mature MCP
list_meetings
get_meeting
get_transcript
key topics
Deal fields/Deal level analysis
Coaching/Scoring
CRM context
pushing insights into Notion / Confluence
AI-ready meeting intelligence
Strongest “outputs” layer
Mostly pull-based interactions
Fathom
✅ MCP
list/search meetings
get transcript
get summary/action items
Team/org context
Webhooks (event-based triggers)
get_weekly_recap
aggregated action items across meetings
creating webhooks directly from the MCP server
Lightweight meeting data access
Includes summaries + actions
Limited analytics depth
Grain
✅ Basic MCP
get meetings
download transcripts
Notes
Deals
Scorecards
Data access utility
Simple + accessible
Low sophistication, sometimes browser-based
Outreach
⚠️ MCP (workflow-focused)
Search sequences
Emails
Meetings (Kaia)
account / prospect / opportunity lookups
Workflow automation
Multi-system orchestration
Revenue workflow engine
End-to-end GTM workflows
Less depth in conversation data
Attention
⚠️ Emerging MCP
Meeting data
Transcripts
CRM-linked context
AI insights
Sales assistant workflows
create snippets
AI sales assistant + CRM
Strong CRM linkage
Less focus on external knowledge layer
Planhat -
Related content
Ask Jiminny Anything on Call level
Galya Dimitrova
Hubspot app
Galya Dimitrova
Product Vision & Strategy
Galya Dimitrova
Automated CRM Filling
Simona Georgieva (Deactivated)
Competitive analysis
Galya Dimitrova
AI agent ideas
Steliyan Georgiev
Comments
James Graham
9 Apr
A few areas around Security & Compliance that need more depth before this moves beyond draft:
Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate.
Team visibility enforcement should be its own HIGH requirement. The current note under access control ("ensure MCP only exposes data user has access to") is doing too much work as a single line. Every MCP tool response (especially search_calls and deal-level aggregations) must be filtered through the same visibility engine as the UI. Semantic search is the riskiest here; it could surface results across team boundaries if not properly scoped.
No data minimisation or scoping controls. We're exposing transcripts, CRM fields, contact details with job titles, deal context - significant PII and commercial data leaving the platform. Can customers configure which data types are exposed? Can transcripts be PII-stripped before they leave Jiminny? Given we position GDPR consent as a differentiator, we should be thinking about this.
GDPR downstream processing is unaddressed. Piping transcripts to OpenAI/Gemini introduces new data processors. Customers need to acknowledge responsibility for downstream processing during setup. We should also consider whether call participants' recording consent covers onward sharing to third-party AI - it almost certainly doesn't by default. This is one for me to pick up.
Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users.
Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns.
Audit logs should go deeper. Current spec (what data, which system, when) is a start. Add: identity behind the key, specific queries/filters used, and failed access attempts (especially cross-visibility-boundary attempts). Enterprise compliance teams will expect this.
Downgrade handling. Doc says Scale tier and above, but doesn't say what happens on downgrade. Keys should auto-deactivate.
Happy to help draft a dedicated Security & Compliance section i...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST [URL_WITH_CREDENTIALS] Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.
You said
Jiminny MCP Connector
Epic
Link to Epik in Jira
Document status
DRAFT
Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.
👤 Target user
Revenue teams using AI tools (Sales, CS, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
Build an AI Knowledge Connector (MCP-based) that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides structured insights (summaries, action items, scoring etc.)
Supports deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
Metric
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of MCP/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
User interaction and design
Simple setup flow:
Generate API key / connect MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus: usable by non-technical users
Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
Importance
Notes
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH
For each deal expose:
calls/meetings/emails
Identified risks
contacts related to the deal with their job title
linked account details
all CRM field information that we have in DI + fields used for CRM filling
example - get_deal_summary
request -
{
"tool": "get_deal_summary",
"arguments": {
"deal_name": "ACME"
}
}
responce:
{
"deal_name": "ACME",
"stage": "Negotiation",
"recent_activity": [
"Demo call on 2026-04-01",
"Pricing discussion on 2026-04-03"
],
"risks": [
"Pricing concerns",
"Security review pending"
], "next_steps": [
"Send follow-up to VP Sales",
"Share security documentation"
]
}
Ensure the AI can easily understand the schema of the data coming from Jiminny
HIGH
Models perform much better when tools have:
clear names
rich descriptions
predictable schemas
Provide clear, actionable error states for customers when MCP connections fail
HIGH
Error messages should guide agents toward solutions with specific suggestions and next steps.
Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”
Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”
Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”
Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”
Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”
Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”
No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”
Allow customers to securely control who and what data is accessible externally
HIGH
Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well.
Private meetings shouldn’t be exposed via MCP
HIGH
When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP.
Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows
HIGH
Ensure data is structured to be easily combined with:
CRM (accounts, deals)
external docs
Use consistent identifiers:
account IDs
deal IDs
MCP is available only for Scale tier and above
HIGH
Customers who are on Capture tier should be able to access any of their data through MCP.
This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working.
Provide visibility into how external AI tools access Jiminny data
HIGH
Audit logs:
what data was accessed by which external system when
identity - who made the request
specific queries/filters used
failed access attempts (especially cross-visibility-boundary attempts)
Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it.
example from Atlassian -
Provide customers with example prompts to get started quickly
HIGH
MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments.
See examples in table below.
Expose Jiminny as a Connector in Claude
medium
Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny
Provide customers with example workflows to get started quickly
medium
A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.
See examples in table below.
Allow customers to use predefined Jiminny skills for common workflows
medium
Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.
Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com
See examples in table below.
Jiminny UI widget for OpenAI
medium
The reason to have this is to make certain Jiminny use cases much easier than pure chat.
Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.
For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.
Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.
Allow customers to run long-running analyses asynchronously and retrieve progress/results
low
Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to:
start the job
let it run in the background
return a status like “in progress”
let the client come back for the result later
For example - User asks in Claude:
“Analyse all calls from this quarter and tell me the top 5 objections by segment.”
That might require:
searching lots of calls
pulling many summaries/transcripts
aggregating results
Instead of making Claude wait 45+ seconds, Jiminny could:
create an analysis job
return: job_id = 123, status = running
We need this in order to support:
large-scale analysis
multi-call aggregation across lots of data
playlist generation across many calls
heavy recap/report generation
Creating a snippet through the MCP
low
Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team.
example request:
{
"tool": "create_snippet",
"arguments": {
"call_id": "call_123",
"start_time": "00:18:24",
"end_time": "00:19:10",
"title": "Pricing objection"
}
}
example response:
{
"snippet_id": "snip_987",
"title": "Pricing objection",
"share_url": "https://app.jiminny.com/snippets/snip_987"
}
Ability to add recordings or snippets to a playlist
low
Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes
Allow AI tools to read reusable Jiminny resources
low
fathom
Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be:
current deal snapshot
account summary
weekly rep recap
approved talk tracks / objection handling guidance
curated playlists or snippet libraries
This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls.
Ability to write coaching notes in Jiminny through MCP
low
What we want to do for prompts/worflows and skills...
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Objective Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows. Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product. 👤 Target user Revenue teams using AI tools (Sales, CS, RevOps) Companies already experimenting with Claude / OpenAI Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc) 🤕 Pain point or problem Jiminny data is locked in the platform AI tools lack access to high-value conversation context Customers must manually export/copy transcripts No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people Peer pressure as almost all competitors have this 💥 Impact and benefits For customers Better AI outputs using real customer conversations Ability to build automated workflows across tools For Jiminny Increased product stickiness (embedded in workflows) Competitive parity with Gong / Avoma etc. 💡 Solution ideas Build an AI Knowledge Connector (MCP-based) that: Exposes Jiminny data (calls, transcripts, metadata) to AI tools Provides structured insights (summaries, action items, scoring etc.) Supports deal-level context Allows integration into customer-owned AI stacks (Claude + Zapier, etc.) Success metrics List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets). Goal Metric Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows % of customers connecting external AI tools Number of MCP/API calls per customer - and which ones are being used the most Decrease the manual copying of transcripts decrease by X % User interaction and design Simple setup flow: Generate API key / connect MCP Clear instructions for Claude / Zapier setup Provide: Example prompts for Claude Pre-built workflow templates for Zapier Clear documentation - with use cases 👉 Focus: usable by non-technical users Detailed Requirements List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification. Requirement Importance Notes Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI) HIGH Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack Allow customers to access call data through AI tools HIGH Expose Jiminny data: list calls (filter by date, user, account, deal) get call get transcript (speaker-separated) get call metadata search calls (keyword + semantic) Support filtering by: account / contact/ lead / deal jiminny user date range Allow customers to search and query calls using AI tools HIGH Support keyword and semantic search across conversations Provide structured insights so customers can generate better outputs HIGH For each call, expose: Summary Action items Key points Activity type Notes Call Score CRM information Allow customers to understand deal status across multiple calls/emails HIGH For each deal expose: calls/meetings/emails Identified risks contacts related to the deal with their job title linked account details all CRM field information that we have in DI + fields used for CRM filling example - get_deal_summary request - { \"tool\": \"get_deal_summary\", \"arguments\": { \"deal_name\": \"ACME\" } } responce: { \"deal_name\": \"ACME\", \"stage\": \"Negotiation\", \"recent_activity\": [ \"Demo call on 2026-04-01\", \"Pricing discussion on 2026-04-03\" ], \"risks\": [ \"Pricing concerns\", \"Security review pending\" ], \"next_steps\": [ \"Send follow-up to VP Sales\", \"Share security documentation\" ] } Ensure the AI can easily understand the schema of the data coming from Jiminny HIGH Models perform much better when tools have: clear names rich descriptions predictable schemas Provide clear, actionable error states for customers when MCP connections fail HIGH Error messages should guide agents toward solutions with specific suggestions and next steps. Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.” Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.” Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.” Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.” Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.” Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.” No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.” Allow customers to securely control who and what data is accessible externally HIGH Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well. Private meetings shouldn’t be exposed via MCP HIGH When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP. Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows HIGH Ensure data is structured to be easily combined with: CRM (accounts, deals) external docs Use consistent identifiers: account IDs deal IDs MCP is available only for Scale tier and above HIGH Customers who are on Capture tier should be able to access any of their data through MCP. This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working. Provide visibility into how external AI tools access Jiminny data HIGH Audit logs: what data was accessed by which external system when identity - who made the request specific queries/filters used failed access attempts (especially cross-visibility-boundary attempts) Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it. example from Atlassian - Provide customers with example prompts to get started quickly HIGH MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments. See examples in table below. Expose Jiminny as a Connector in Claude medium Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny Provide customers with example workflows to get started quickly medium A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome. See examples in table below. Allow customers to use predefined Jiminny skills for common workflows medium Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job. Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com See examples in table below. Jiminny UI widget for OpenAI medium The reason to have this is to make certain Jiminny use cases much easier than pure chat. Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it. For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface. Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section. Allow customers to run long-running analyses asynchronously and retrieve progress/results low Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to: start the job let it run in the background return a status like “in progress” let the client come back for the result later For example - User asks in Claude: “Analyse all calls from this quarter and tell me the top 5 objections by segment.” That might require: searching lots of calls pulling many summaries/transcripts aggregating results Instead of making Claude wait 45+ seconds, Jiminny could: create an analysis job return: job_id = 123, status = running We need this in order to support: large-scale analysis multi-call aggregation across lots of data playlist generation across many calls heavy recap/report generation Creating a snippet through the MCP low Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team. example request: { \"tool\": \"create_snippet\", \"arguments\": { \"call_id\": \"call_123\", \"start_time\": \"00:18:24\", \"end_time\": \"00:19:10\", \"title\": \"Pricing objection\" } } example response: { \"snippet_id\": \"snip_987\", \"title\": \"Pricing objection\", \"share_url\": \"https://app.jiminny.com/snippets/snip_987\" } Ability to add recordings or snippets to a playlist low Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes Allow AI tools to read reusable Jiminny resources low fathom Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be: current deal snapshot account summary weekly rep recap approved talk tracks / objection handling guidance curated playlists or snippet libraries This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls. Ability to write coaching notes in Jiminny through MCP low What we want to do for prompts/worflows and skills Capability Type User value / job to be done Example Jiminny use case Inputs Steps performed Output Draft follow-up email Prompt Help reps quickly create tailored follow-ups after calls Use the latest customer call to draft an email with recap and next steps Call ID optional tone optional recipient type Retrieve call summary and action items from Jiminny. Pull key points, commitments, and next steps. Apply the chosen tone and structure. Draft the email in a consistent format. Customer-ready follow-up email draft Summarise call Prompt Help users quickly extract the most important points from a conversation Generate a concise summary of one call for internal or external use Call ID optional summary style optional audience Retrieve transcript, metadata, and structured insights. Identify key topics, decisions, risks, and next steps. Format output for the chosen audience. Short or structured call summary with actions and risks Generate deal recap Prompt Help reps and managers get a fast overview of a deal Summarise the current state of a deal based on recent calls and linked CRM context Deal ID or account name optional date range Retrieve calls and meetings linked to the deal. Pull deal metadata and CRM-linked context. Identify recent changes, blockers, risks, and next steps. Summarise into a concise recap. Deal recap with status, blockers, risks, and recommended next steps Prepare weekly recap Prompt Help managers or reps review what changed over a time period Weekly recap of calls, themes, and actions for one rep, team, or account Rep ID team ID or account ID time period Retrieve relevant calls and summaries for the selected time period. Aggregate themes, action items, and changes. Highlight notable risks, opportunities, and follow-ups. Weekly recap with top themes, risks, opportunities, and actions Prepare deal review Workflow Help reps and managers review deal status using multiple sources of context Review a deal before pipeline review or forecast meeting Deal ID optional date range Retrieve all recent calls, emails, and meetings tied to the deal. Pull structured call summaries, score, action items, and key points. Pull linked CRM context such as stage, contacts, and account details. Aggregate open risks, unresolved objections, stakeholder gaps, and next steps. Format into a review-ready structure. Deal review document with current status, risks, stakeholders, blockers, and next steps Weekly team recap Workflow Help managers monitor activity and risk across a group of reps or accounts End-of-week team summary for sales leadership Team ID date range Retrieve calls and structured insights across the team. Group by rep, account, or deal. Identify recurring themes, major risks, strong calls, and missed follow-ups. Highlight calls or deals that need attention. Team-level recap with patterns, key risks, notable wins, and action areas Objection analysis across calls Workflow Help teams identify patterns across multiple conversations Analyse pricing or competitor objections across recent calls Account ID deal ID rep ID keyword/topic date range Search calls matching the topic or keyword. Retrieve summaries, transcripts, and snippets where relevant. Identify recurring objections and how they were handled. Assess whether objections were resolved or remain open. Summarise patterns and examples. Objection analysis with themes, frequency, examples, and suggested follow-up actions Build onboarding playlist Workflow Help enablement teams curate useful learning content faster Build a playlist of strong discovery, demo, or pricing calls for new hires Topic call type rep/team filter optional time period Search for calls matching the selected criteria. Rank or filter calls based on score, topics, or key moments. Identify useful snippets or full recordings. Add selected calls/snippets into a playlist structure. Generate short descriptions for each item. Curated onboarding playlist with selected calls/snippets and descriptions Deal Inspection Skill Skill Give users a consistent, opinionated way to assess a deal Claude reviews a deal using a repeatable Jiminny methodology Deal ID or account name optional review scope Determine whether the request is suitable for a deal inspection. Retrieve relevant calls, summaries, CRM context, stakeholders, and risks. Apply a standard Jiminny review framework to assess deal health. Look for missing next steps, unresolved objections, stakeholder gaps, and warning signs. Present findings in a consistent structure with recommended actions. Structured deal inspection with health assessment, red flags, and recommendations Manager Coaching Skill Skill Help managers review calls consistently and identify coaching moments Analyse a rep’s call and surface strengths, gaps, and examples for coaching Call ID optional rep ID optional coaching focus Retrieve transcript, score, summary, and key moments from the call. Evaluate the conversation against a coaching framework. Identify what went well and where the rep could improve. Suggest moments to clip as snippets. Recommend coaching actions or playlist additions. Coaching review with strengths, gaps, snippet suggestions, and recommended coaching actions CSM / QBR Prep Skill Skill Help CS or AM teams prepare structured customer reviews from conversations Generate an account review or QBR-style summary from recent customer interactions Account ID, optional time period Retrieve all recent calls, emails, and account-linked context. Aggregate themes, requests, blockers, sentiment shifts, and opportunities. Apply a repeatable account review structure. Summarise outcomes, open issues, and suggested next steps. Structured QBR/account review summary with themes, risks, opportunities, and actions Snippet Builder Skill Skill Help users consistently capture and label useful moments from calls Identify useful moments in a call and convert them into reusable snippets Call ID, optional focus area such as pricing, objection, competitor, discovery Retrieve transcript, recording timeline, and call insights. Detect moments matching the requested focus area. Propose snippet start/end times and titles. Tag snippets by type or theme. Optionally save them or add them to a playlist. One or more proposed or saved snippets with title, timing, and tags 💡 Technical details Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate. Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users. Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns. Open Questions Track unresolved issues or decisions that need to be made. Update as answers become available. Question Answer Date Answered Competitor research Competitor MCP Availability Core Capabilities Advanced Capabilities Positioning Key Strength Key Weakness Gong ✅ Mature MCP List/search calls Get transcripts get Call metadata Deal intelligence Scorecards, analytics CRM context Enterprise data layer Deep data + ecosystem integrations Limited opinionated AI workflows Avoma ✅ Mature MCP list_meetings get_meeting get_transcript key topics Deal fields/Deal level analysis Coaching/Scoring CRM context pushing insights into Notion / Confluence AI-ready meeting intelligence Strongest “outputs” layer Mostly pull-based interactions Fathom ✅ MCP list/search meetings get transcript get summary/action items Team/org context Webhooks (event-based triggers) get_weekly_recap aggregated action items across meetings creating webhooks directly from the MCP server Lightweight meeting data access Includes summaries + actions Limited analytics depth Grain ✅ Basic MCP get meetings download transcripts Notes Deals Scorecards Data access utility Simple + accessible Low sophistication, sometimes browser-based Outreach ⚠️ MCP (workflow-focused) Search sequences Emails Meetings (Kaia) account / prospect / opportunity lookups Workflow automation Multi-system orchestration Revenue workflow engine End-to-end GTM workflows Less depth in conversation data Attention ⚠️ Emerging MCP Meeting data Transcripts CRM-linked context AI insights Sales assistant workflows create snippets AI sales assistant + CRM Strong CRM linkage Less focus on external knowledge layer Planhat - Related content Ask Jiminny Anything on Call level Galya Dimitrova Hubspot app Galya Dimitrova Product Vision & Strategy Galya Dimitrova Automated CRM Filling Simona Georgieva (Deactivated) Competitive analysis Galya Dimitrova AI agent ideas Steliyan Georgiev Comments James Graham 9 Apr A few areas around Security & Compliance that need more depth before this moves beyond draft: Auth model needs specifying. Are API keys scoped per user, per team, or per Jiminny instance? If a key is generated by an admin, does it inherit their visibility level, or provide org-wide? Are permissions evaluated at query time or frozen at key creation? Needs to be query-time, otherwise permission changes won't propagate. Team visibility enforcement should be its own HIGH requirement. The current note under access control (\"ensure MCP only exposes data user has access to\") is doing too much work as a single line. Every MCP tool response (especially search_calls and deal-level aggregations) must be filtered through the same visibility engine as the UI. Semantic search is the riskiest here; it could surface results across team boundaries if not properly scoped. No data minimisation or scoping controls. We're exposing transcripts, CRM fields, contact details with job titles, deal context - significant PII and commercial data leaving the platform. Can customers configure which data types are exposed? Can transcripts be PII-stripped before they leave Jiminny? Given we position GDPR consent as a differentiator, we should be thinking about this. GDPR downstream processing is unaddressed. Piping transcripts to OpenAI/Gemini introduces new data processors. Customers need to acknowledge responsibility for downstream processing during setup. We should also consider whether call participants' recording consent covers onward sharing to third-party AI - it almost certainly doesn't by default. This is one for me to pick up. Key lifecycle management is missing. No mention of rotation, expiry, or revocation. A departed employee's active key is a data leak. Need: revocation, optional expiry, alerts on stale keys, and consider whether keys should be tied to service accounts rather than personal users. Rate limiting should be HIGH. A compromised key or misconfigured automation could bulk-export an entire customer's call history. Need per-key rate limits and anomaly detection on unusual access patterns. Audit logs should go deeper. Current spec (what data, which system, when) is a start. Add: identity behind the key, specific queries/filters used, and failed access attempts (especially cross-visibility-boundary attempts). Enterprise compliance teams will expect this. Downgrade handling. Doc says Scale tier and above, but doesn't say what happens on downgrade. Keys should auto-deactivate. Happy to help draft a dedicated Security & Compliance section if useful. Galya Dimitrova 15 Apr Thanks for the input @James Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.","depth":21,"bounds":{"left":0.41805187,"top":0.19313647,"width":0.051861703,"height":0.11173184},"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"You said","depth":23,"bounds":{"left":0.36053857,"top":0.19553073,"width":0.019946808,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny MCP Connector","depth":23,"bounds":{"left":0.41805187,"top":0.1963288,"width":0.03125,"height":0.038707104},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Epic","depth":23,"bounds":{"left":0.41805187,"top":0.30806065,"width":0.010305851,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Link to Epik in Jira","depth":23,"bounds":{"left":0.41805187,"top":0.33040702,"width":0.043218084,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Document status","depth":23,"bounds":{"left":0.41805187,"top":0.3527534,"width":0.04105718,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"DRAFT","depth":23,"bounds":{"left":0.41805187,"top":0.37509975,"width":0.016289894,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Objective","depth":23,"bounds":{"left":0.41805187,"top":0.39744613,"width":0.024268618,"height":0.016360734},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.","depth":23,"bounds":{"left":0.41805187,"top":0.4197925,"width":0.051529255,"height":0.17278531},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.","depth":23,"bounds":{"left":0.41805187,"top":0.59856343,"width":0.049700797,"height":0.10574621},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"👤 Target user","depth":23,"bounds":{"left":0.41805187,"top":0.75458896,"width":0.033909574,"height":0.016759777},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Revenue teams using AI tools (Sales, CS, RevOps)","depth":23,"bounds":{"left":0.41805187,"top":0.7773344,"width":0.051030584,"height":0.061053474},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Companies already experimenting with Claude / OpenAI","depth":23,"bounds":{"left":0.41805187,"top":0.8443735,"width":0.04637633,"height":0.061053474},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)","depth":23,"bounds":{"left":0.41805187,"top":0.9114126,"width":0.051363032,"height":0.0885874},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"🤕 Pain point or problem","depth":23,"bounds":{"left":0.41805187,"top":1.0,"width":0.03706782,"height":-0.0897845},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny data is locked in the platform","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"AI tools lack access to high-value conversation context","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Customers must manually export/copy transcripts","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"No easy way to combine calls with CRM + docs + other info - 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This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to securely control who and what data is accessible externally","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Private meetings shouldn’t be exposed via MCP","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ensure data is structured to be easily combined with:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"CRM (accounts, deals)","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"external docs","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Use consistent identifiers:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"account IDs","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"deal IDs","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"MCP is available only for Scale tier and above","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Customers who are on Capture tier should be able to access any of their data through MCP.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide visibility into how external AI tools access Jiminny data","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Audit logs:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"what data was accessed by which external system when","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"identity - who made the request","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"specific queries/filters used","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"failed access attempts (especially cross-visibility-boundary attempts)","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"example from Atlassian -","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide customers with example prompts to get started quickly","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HIGH","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"See examples in table below.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Expose Jiminny as a Connector in Claude","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide customers with example workflows to get started quickly","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"See examples in table below.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to use predefined Jiminny skills for common workflows","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"See examples in table below.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Jiminny UI widget for OpenAI","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The reason to have this is to make certain Jiminny use cases much easier than pure chat.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to run long-running analyses asynchronously and retrieve progress/results","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"start the job","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"let it run in the background","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"return a status like “in progress”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"let the client come back for the result later","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"For example - User asks in Claude:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"“Analyse all calls from this quarter and tell me the top 5 objections by segment.”","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"That might require:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"searching lots of calls","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"pulling many summaries/transcripts","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"aggregating results","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Instead of making Claude wait 45+ seconds, Jiminny could:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"create an analysis job","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"return: job_id = 123, status = running","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"We need this in order to support:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"large-scale analysis","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"multi-call aggregation across lots of data","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"playlist generation across many calls","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"heavy recap/report generation","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Creating a snippet through the MCP","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"example request:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"{","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"tool\": \"create_snippet\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"arguments\": {","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"call_id\": \"call_123\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"start_time\": \"00:18:24\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"end_time\": \"00:19:10\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"title\": \"Pricing objection\"","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"example response:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"{","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"snippet_id\": \"snip_987\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"title\": \"Pricing objection\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"share_url\": \"https://app.jiminny.com/snippets/snip_987\"","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ability to add recordings or snippets to a playlist","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow AI tools to read reusable Jiminny resources","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"fathom","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"current deal snapshot","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"account summary","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"weekly rep recap","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"approved talk tracks / objection handling guidance","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"curated playlists or snippet libraries","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ability to write coaching notes in Jiminny through MCP","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"low","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"What we want to do for prompts/worflows and skills","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"}]...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST [URL_WITH_CREDENTIALS] Graham . I made the product related changes in the table and for the rest I left them as a note in the Technical section for the team to look at when they start looking at this. I wonder about your points related to GDPR. Currently multiple customers are using the Customer API/zapier to feed this data to AI and BI tools. They are also manually exporting transcripts and other information from the platform and feeding it to AI tools. I don’t get how MCP is so much different that all of these and why it will introduce new data processors (which are already being utilised) and would require any changes to the consent provided by participants.
You said
Jiminny MCP Connector
Epic
Link to Epik in Jira
Document status
DRAFT
Objective
Enable customers to connect Jiminny data to external AI tools (Claude, OpenAI, Gemini) so it can be used as part of their broader knowledge base and workflows.
Position Jiminny as a data layer for AI-driven revenue workflows, not just a standalone product.
👤 Target user
Revenue teams using AI tools (Sales, CS, RevOps)
Companies already experimenting with Claude / OpenAI
Mid-market & Enterprise customers with multiple data sources (CRM, docs, CI tools, Support tools etc)
🤕 Pain point or problem
Jiminny data is locked in the platform
AI tools lack access to high-value conversation context
Customers must manually export/copy transcripts
No easy way to combine calls with CRM + docs + other info - using the Jiminny API is not suitable for non technical sales people
Peer pressure as almost all competitors have this
💥 Impact and benefits
For customers
Better AI outputs using real customer conversations
Ability to build automated workflows across tools
For Jiminny
Increased product stickiness (embedded in workflows)
Competitive parity with Gong / Avoma etc.
💡 Solution ideas
Build an AI Knowledge Connector (MCP-based) that:
Exposes Jiminny data (calls, transcripts, metadata) to AI tools
Provides structured insights (summaries, action items, scoring etc.)
Supports deal-level context
Allows integration into customer-owned AI stacks (Claude + Zapier, etc.)
Success metrics
List the key goals and how you will measure success. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).
Goal
Metric
Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows
% of customers connecting external AI tools
Number of MCP/API calls per customer - and which ones are being used the most
Decrease the manual copying of transcripts
decrease by X %
User interaction and design
Simple setup flow:
Generate API key / connect MCP
Clear instructions for Claude / Zapier setup
Provide:
Example prompts for Claude
Pre-built workflow templates for Zapier
Clear documentation - with use cases
👉 Focus: usable by non-technical users
Detailed Requirements
List the functional and non-functional requirements. Prioritize them (e.g., High, Medium, Low) and add notes for clarification.
Requirement
Importance
Notes
Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)
HIGH
Provide MCP endpoint so customers can easily integrate Jiminny into their AI stack
Allow customers to access call data through AI tools
HIGH
Expose Jiminny data:
list calls (filter by date, user, account, deal)
get call
get transcript (speaker-separated)
get call metadata
search calls (keyword + semantic)
Support filtering by:
account / contact/ lead / deal
jiminny user
date range
Allow customers to search and query calls using AI tools
HIGH
Support keyword and semantic search across conversations
Provide structured insights so customers can generate better outputs
HIGH
For each call, expose:
Summary
Action items
Key points
Activity type
Notes
Call Score
CRM information
Allow customers to understand deal status across multiple calls/emails
HIGH
For each deal expose:
calls/meetings/emails
Identified risks
contacts related to the deal with their job title
linked account details
all CRM field information that we have in DI + fields used for CRM filling
example - get_deal_summary
request -
{
"tool": "get_deal_summary",
"arguments": {
"deal_name": "ACME"
}
}
responce:
{
"deal_name": "ACME",
"stage": "Negotiation",
"recent_activity": [
"Demo call on 2026-04-01",
"Pricing discussion on 2026-04-03"
],
"risks": [
"Pricing concerns",
"Security review pending"
], "next_steps": [
"Send follow-up to VP Sales",
"Share security documentation"
]
}
Ensure the AI can easily understand the schema of the data coming from Jiminny
HIGH
Models perform much better when tools have:
clear names
rich descriptions
predictable schemas
Provide clear, actionable error states for customers when MCP connections fail
HIGH
Error messages should guide agents toward solutions with specific suggestions and next steps.
Expired or revoked key — “Error: Authentication failed. The API key used to access Jiminny has been revoked or has expired. Please ask your Jiminny admin to generate a new API key in Settings > Organization > General and reconnect the MCP server.”
Rate limit hit — “Error: Rate limit exceeded. Too many requests have been made to the Jiminny MCP server in a short period. Please wait 60 seconds before retrying.”
Permissions mismatch — if a user tries to access data outside their team visibility settings - “Error: Access denied. The requested call (call_id: [X]) is outside your team's visibility settings in Jiminny. Only calls within your permitted scope are accessible.”
Trying to access private meeting - “Error: Access denied. This meeting has been marked as private by the host and cannot be accessed through the MCP connector. Try requesting a different call or ask the meeting host to change the visibility settings in Jiminny.”
Downgraded/Capture account — if a customer's plan drops below Scale tier or the Customer is on Capture in the first place - “Error: MCP access unavailable. The Jiminny MCP connector is available on the Scale plan and above. Your account does not currently have an active Scale subscription.”
Jiminny-side outage — “Error:Jiminny is temporarily unavailable (status 503). This is not a data or permissions issue — please retry in a few minutes.”
No data found - “Error:No calls found matching your criteria. Try adjusting your filters — for example, widening the date range, removing the account filter, or checking the rep name is spelled correctly in Jiminny.”
Allow customers to securely control who and what data is accessible externally
HIGH
Ensure MCP only exposes data user has access to - based on team visibility settings. This means that if a user is not allow to access calls/deals in Jiminny UI then they shouldn’t be able to access those through the MCP as well.
Private meetings shouldn’t be exposed via MCP
HIGH
When a meetings is set to Private then the information about it shouldn’t be exposed through the MCP.
Ensure Jiminny data can be combined with other sources (e.g. CRM, docs) in AI workflows
HIGH
Ensure data is structured to be easily combined with:
CRM (accounts, deals)
external docs
Use consistent identifiers:
account IDs
deal IDs
MCP is available only for Scale tier and above
HIGH
Customers who are on Capture tier should be able to access any of their data through MCP.
This should also cover the scenario when a customer has been on Scale but then downgrades to Capture - their MCP access should stop working.
Provide visibility into how external AI tools access Jiminny data
HIGH
Audit logs:
what data was accessed by which external system when
identity - who made the request
specific queries/filters used
failed access attempts (especially cross-visibility-boundary attempts)
Admins should be able to access the logs from the org settings.We can start with a csv export before building a specific page for it.
example from Atlassian -
Provide customers with example prompts to get started quickly
HIGH
MCP prompts are explicitly defined as prompt templates that clients can discover and call with arguments.
See examples in table below.
Expose Jiminny as a Connector in Claude
medium
Competitors like Fathom are listed as Connectors in Claude. This will make it easier for customers to find us and to connect Jiminny
Provide customers with example workflows to get started quickly
medium
A workflow is usually multiple steps, maybe multiple tools, still focused on one outcome.
See examples in table below.
Allow customers to use predefined Jiminny skills for common workflows
medium
Anthropic describes Skills as folders that include instructions, scripts, and resources that Claude can load when relevant, and as a way to teach Claude repeatable workflows, preferences, and domain expertise once instead of re-explaining them every time. Skills - a reusable package of behavior that can include prompts, instructions, resources, and sometimes scripts/tools for a broader job.
Claude skills - https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf?hsLang=en&utm_source=chatgpt.com
See examples in table below.
Jiminny UI widget for OpenAI
medium
The reason to have this is to make certain Jiminny use cases much easier than pure chat.
Instead of ChatGPT only replying with text like “Here are the last 5 ACME calls,” it could also show a visual Jiminny card or mini-app inside the chat. That widget could list calls, show a deal summary, let the user filter by rep/date/account, open a transcript section, or save a snippet—while the conversation continues in normal chat alongside it.
For Jiminny, the best widget use cases are the ones where users need to browse or compare rather than ask one-off questions. A strong example is deal review: the user asks ChatGPT, “What is the status of the ACME deal?” ChatGPT calls your Jiminny MCP tools, and the widget shows a compact deal view with recent calls, risks, next steps, stakeholders, and links to transcripts/snippets. The chat can still summarize the answer in words, but the widget gives a clearer, scannable interface.
Another good Jiminny widget use case is call search and exploration. A text answer can say “I found 12 calls mentioning pricing objections,” but a widget could show a sortable list with date, rep, account, score, and objection count, then let the user click into one call to see the exact excerpt or jump to a transcript section.
Allow customers to run long-running analyses asynchronously and retrieve progress/results
low
Some requests/questions require analysis of Hugh volumes of data. For those, it may be better to:
start the job
let it run in the background
return a status like “in progress”
let the client come back for the result later
For example - User asks in Claude:
“Analyse all calls from this quarter and tell me the top 5 objections by segment.”
That might require:
searching lots of calls
pulling many summaries/transcripts
aggregating results
Instead of making Claude wait 45+ seconds, Jiminny could:
create an analysis job
return: job_id = 123, status = running
We need this in order to support:
large-scale analysis
multi-call aggregation across lots of data
playlist generation across many calls
heavy recap/report generation
Creating a snippet through the MCP
low
Create a shareable snippet from a call recording by specifying a time range. Useful for highlighting key moments — objection handling, pricing discussions, competitive mentions — and sharing them with your team.
example request:
{
"tool": "create_snippet",
"arguments": {
"call_id": "call_123",
"start_time": "00:18:24",
"end_time": "00:19:10",
"title": "Pricing objection"
}
}
example response:
{
"snippet_id": "snip_987",
"title": "Pricing objection",
"share_url": "https://app.jiminny.com/snippets/snip_987"
}
Ability to add recordings or snippets to a playlist
low
Allow customers to create libraries of calls automatically. They can use those for onboarding or other purposes
Allow AI tools to read reusable Jiminny resources
low
fathom
Resources are useful for data that should be read consistently rather than recomputed every time. In Jiminny terms, that could be:
current deal snapshot
account summary
weekly rep recap
approved talk tracks / objection handling guidance
curated playlists or snippet libraries
This is useful because an AI can consume these directly as context, instead of repeatedly stitching together many tool calls.
Ability to write coaching notes in Jiminny through MCP
low
What we want to do for prompts/worflows and skills...
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