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Last login: Sat Apr 11 12:38:35 on ttys007
Poetry Last login: Sat Apr 11 12:38:35 on ttys007
Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parents
Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parents
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix
docker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff
PHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.
PHP runtime: 8.3.30
Running analysis on 7 cores with 10 files per process.
Parallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!
Loaded config default from ".php-cs-fixer.dist.php".
5579/5579 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%
1) app/Services/Kiosk/AutomatedReports/AskJiminnyReportActivityService.php (trailing_comma_in_multiline)
---------- begin diff ----------
--- /home/jiminny/app/Services/Kiosk/AutomatedReports/AskJiminnyReportActivityService.php
+++ /home/jiminny/app/Services/Kiosk/AutomatedReports/AskJiminnyReportActivityService.php
@@ -51,7 +51,7 @@
array_merge($requestParams, [
'limit' => self::DEFAULT_TOP_ACTIVITIES_COUNT,
'page' => 1,
- 'sequence_number' => 1
+ 'sequence_number' => 1,
]),
$user->getTimezone()
);
----------- end diff -----------
Fixed 1 of 5579 files in 108.308 seconds, 67.00 MB memory used
What's next:
Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1
Learn more at [URL_WITH_CREDENTIALS] ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ git status
On branch JY-18909-automated-reports-ask-jiminny
Your branch is ahead of 'origin/JY-18909-automated-reports-ask-jiminny' by 2 commits.
(use "git push" to publish your local commits)
Changes not staged for commit:
(use "git add <file>..." to update what will be committed)
(use "git restore <file>..." to discard changes in working directory)
modified: .env.local
modified: app/Console/Commands/JiminnyDebugCommand.php
modified: app/Http/Controllers/API/ActivityController.php
modified: app/Http/Controllers/Webhook/ReportController.php
modified: app/Jobs/Team/SyncToIntercom.php
modified: app/Services/PlaybackService.php
modified: config/logging.php
modified: routes/web.php
Untracked files:
(use "git add <file>..." to include in what will be committed)
.env.nikilocal
.env.other
WEBHOOK_FILTERING_IMPLEMENTATION.md
app/Console/Commands/Crm/Hubspot/SimulateWebhooksCommand.php
app/Console/Commands/Reports/CreateMockAskJiminnyReportResultCommand.php
ids.txt
raw_sql_query.sql
tests/Unit/Policies/CanAccessAiReportsTest.php
no changes added to commit (use "git add" and/or "git commit -a")
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ git push
Enumerating objects: 63, done.
Counting objects: 100% (55/55), done.
Delta compression using up to 8 threads
Compressing objects: 100% (28/28), done.
Writing objects: 100% (29/29), 3.77 KiB | 3.77 MiB/s, done.
Total 29 (delta 22), reused 0 (delta 0), pack-reused 0
remote: Resolving deltas: 100% (22/22), completed with 20 local objects.
remote:
remote: GitHub found 24 vulnerabilities on jiminny/app's default branch (2 critical, 12 high, 9 moderate, 1 low). To find out more, visit:
remote: [URL_WITH_CREDENTIALS] ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix
docker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff
PHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.
PHP runtime: 8.3.30
Running analysis on 7 cores with 10 files per process.
Parallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!
Loaded config default from ".php-cs-fixer.dist.php".
5589/5589 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%
Fixed 0 of 5589 files in 39.991 seconds, 67.00 MB memory used
What's next:
Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1
Learn more at [URL_WITH_CREDENTIALS] ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix
docker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff
PHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.
PHP runtime: 8.3.30
Running analysis on 7 cores with 10 files per process.
Parallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!
Loaded config default from ".php-cs-fixer.dist.php".
5589/5589 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%
Fixed 0 of 5589 files in 52.171 seconds, 67.00 MB memory used
What's next:
Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1
Learn more at [URL_WITH_CREDENTIALS] ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $
DOCKER
Close Tab
DEV (docker)
Close Tab
APP (-zsh)
Close Tab
ec2-user@ip-10-30-93-249:~ (nc)
Close Tab
-zsh
Close Tab
-zsh
Close Tab
-zsh
Close Tab
✳ Unable to access screenpipe activity data (claude)
Close Tab
⌥⌘1
APP (-zsh)...
|
[{"role":"AXTextArea","text [{"role":"AXTextArea","text":"Last login: Sat Apr 11 12:38:35 on ttys007\n\nPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parents\n\nPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parents\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix\ndocker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff \nPHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.\nPHP runtime: 8.3.30\nRunning analysis on 7 cores with 10 files per process.\nParallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!\nLoaded config default from \".php-cs-fixer.dist.php\".\n 5579/5579 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%\n\n 1) app/Services/Kiosk/AutomatedReports/AskJiminnyReportActivityService.php (trailing_comma_in_multiline)\n ---------- begin diff ----------\n--- /home/jiminny/app/Services/Kiosk/AutomatedReports/AskJiminnyReportActivityService.php\n+++ /home/jiminny/app/Services/Kiosk/AutomatedReports/AskJiminnyReportActivityService.php\n@@ -51,7 +51,7 @@\n array_merge($requestParams, [\n 'limit' => self::DEFAULT_TOP_ACTIVITIES_COUNT,\n 'page' => 1,\n- 'sequence_number' => 1\n+ 'sequence_number' => 1,\n ]),\n $user->getTimezone()\n );\n\n ----------- end diff -----------\n\n\nFixed 1 of 5579 files in 108.308 seconds, 67.00 MB memory used\n\nWhat's next:\n Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1\n Learn more at https://docs.docker.com/go/debug-cli/\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ git pull\nremote: Enumerating objects: 185, done.\nremote: Counting objects: 100% (185/185), done.\nremote: Compressing objects: 100% (53/53), done.\nremote: Total 185 (delta 138), reused 176 (delta 130), pack-reused 0 (from 0)\nReceiving objects: 100% (185/185), 168.96 KiB | 1.05 MiB/s, done.\nResolving deltas: 100% (138/138), completed with 74 local objects.\nFrom github.com:jiminny/app\n * [new branch] JY-20255-quick-search-show-duration-and-call-conference-type -> origin/JY-20255-quick-search-show-duration-and-call-conference-type\n * [new branch] JY-20297-reposition-the-old-acs-feature -> origin/JY-20297-reposition-the-old-acs-feature\n * [new branch] JY-20541-stale-records-pr-1 -> origin/JY-20541-stale-records-pr-1\nMerge made by the 'ort' strategy.\n .env.production | 2 +-\n .env.production-eu | 2 +-\n .github/workflows/secfix.yml | 10 +\n app/Component/Activity/ActivityProcessingStateManager.php | 4 +\n app/Component/ActivityAnalytics/Service/ActivityAnalyticsService.php | 68 +++----\n app/Component/ActivityAnalytics/Service/LocalTriggerMatcherService.php | 40 ++--\n app/Component/ActivityAnalytics/Service/QuestionService.php | 137 +++++++------\n app/Component/ActivityAnalytics/Service/SentenceBreaker.php | 110 -----------\n app/Component/ActivityAnalytics/VO/TopicTriggerMatch.php | 4 +-\n app/Component/AiAutomation/Actions/UpdateCrmFieldAction.php | 14 +-\n app/Component/AiAutomation/Services/CrmFieldContentTrimmer.php | 92 +++++++++\n app/Component/Kiosk/Services/GetTranscriptionComparisonService.php | 61 +-----\n app/Component/LanguageDetection/Services/DetectLanguageService.php | 74 ++++++-\n app/Component/MediaPipeline/Handlers/LanguageDetectionPipeHandler.php | 37 ++++\n app/Component/MediaPipeline/Handlers/TranscriptionParagraphsPipeHandler.php | 45 +++++\n app/Component/MediaPipeline/MediaPipelineOrchestrator.php | 2 +\n app/Component/MediaPipeline/MediaPipelineServiceProvider.php | 8 +\n app/Component/ParagraphBreaker/DTOs/TranscriptParagraph.php | 46 +++++\n app/Component/ParagraphBreaker/Services/ParagraphBuilder.php | 29 ++-\n app/Component/ParagraphBreaker/Services/TranscriptionParagraphsService.php | 179 +++++++++++++++++\n app/Component/ParagraphBreaker/Services/UtteranceBuilder.php | 192 ++++++++----------\n app/Component/PlaybackPage/Snapshots/Listeners/GenerateSnapshotsListener.php | 6 +\n app/Component/Transcription/Formatter/TranscriptionFormatter.php | 11 +-\n app/Component/Transcription/Job/StartTranscriptDiarizationJob.php | 2 +-\n app/Component/Transcription/Job/TranscriptSegmentsCompileJob.php | 43 +++-\n app/Component/Transcription/Listener/TranscriptSegmentsCompileListener.php | 16 --\n app/Component/Transcription/Service/SearchService.php | 179 ++++++++++-------\n app/Component/Transcription/Service/TranscriptSegmentsService.php | 396 -------------------------------------\n app/Component/Transcription/Service/TranscriptionService.php | 142 ++------------\n app/Component/Transcription/Service/TranslationService.php | 59 ++----\n app/Component/Transcription/TranscriptionProcessor/AssemblyAI/AssemblyAI.php | 172 +---------------\n app/Component/Transcription/TranscriptionProcessor/AssemblyAI/Services/FetchTranscriptService.php | 48 +++++\n .../Transcription/TranscriptionProcessor/AssemblyAI/Services/SubmitAudioFileForLanguageDetectionService.php | 111 +++++++++++\n app/Component/Transcription/VO/TranscriptSegmentTransformer.php | 37 ----\n app/Console/Commands/Crm/Hubspot/RestoreDealAssociationsCommand.php | 344 ++++++++++++++++++++++++--------\n app/Console/Commands/EngagementStats/JiminnyEngagementStatsExplainCommand.php | 76 ++++---\n app/Console/Commands/Mailboxes/InboxSync.php | 9 +-\n app/Http/Controllers/Auth/SocialController.php | 2 +-\n app/Jobs/Mailbox/SyncInbox.php | 2 +-\n app/Listeners/Activities/Coaching/Intercom/CreateActivitySummarizedEvent.php | 6 +\n app/Listeners/Activities/Coaching/Intercom/CreateCommentedEvent.php | 6 +\n app/Listeners/Activities/Coaching/Intercom/CreateManagerCoachedEvent.php | 6 +\n app/Listeners/Activities/Coaching/Intercom/CreatePlayedEvent.php | 6 +\n app/Listeners/Activities/Conferences/Intercom/CreateHeldActivityEvent.php | 6 +\n app/Listeners/Activities/Crm/Summary/AbstractSummaryListener.php | 5 +-\n app/Listeners/Activities/Sms/Intercom/CreateSmsReceivedEvent.php | 6 +\n app/Models/Activity/TopicTrigger.php | 2 +-\n app/Models/Activity/Transcription.php | 29 +--\n app/Providers/EventServiceProvider.php | 1 -\n app/Providers/JiminnyServiceProvider.php | 23 ---\n app/Repositories/QuestionRepository.php | 32 +--\n app/Services/Activity/HubSpot/AuthenticatedHttpClient.php | 75 +------\n app/Services/Activity/HubSpot/RedirectUrlResolver.php | 91 +++++++++\n app/Services/Activity/HubSpot/Service.php | 17 +-\n app/Services/Activity/HubSpot/ZoomRecordingDetector.php | 27 +++\n app/Services/Activity/MeetingBotService.php | 5 +-\n app/Services/Calendar/GoogleCalendarService.php | 3 +\n app/Services/Calendar/OfficeCalendarService.php | 25 +--\n app/Services/Crm/Hubspot/SyncRelatedActivityManager.php | 9 +-\n composer.json | 1 -\n composer.lock | 51 +----\n front-end/src/components/DealInsights/Modals/AskAnythingModal.vue | 1 -\n front-end/src/components/Settings/Kiosk/ActivitiesSearch/ActivityTranscription.vue | 12 +-\n front-end/src/components/shared/Filters/InputText.vue | 2 +\n tests/Unit/Component/Activity/ActivityProcessingStateManagerTest.php | 17 +-\n tests/Unit/Component/ActivityAnalytics/Service/ActivityAnalyticsServiceTest.php | 137 ++++++++++++-\n tests/Unit/Component/ActivityAnalytics/Service/LocalTriggerMatcherServiceTest.php | 135 +++++++------\n tests/Unit/Component/ActivityAnalytics/Service/QuestionServiceTest.php | 610 ++++++++++++---------------------------------------------\n tests/Unit/Component/ActivityAnalytics/VO/TopicTriggerMatchTest.php | 15 ++\n tests/Unit/Component/AiAutomation/Actions/UpdateCrmFieldActionTest.php | 66 +++++++\n tests/Unit/Component/AiAutomation/Services/CrmFieldContentTrimmerTest.php | 282 ++++++++++++++++++++++++++\n tests/Unit/Component/ES/UpdateProcessManagerTest.php | 2 +-\n tests/Unit/Component/Kiosk/Services/GetTranscriptionComparisonServiceTest.php | 79 +++-----\n tests/Unit/Component/LanguageDetection/Services/DetectLanguageServiceTest.php | 132 +++++++++----\n tests/Unit/Component/MediaPipeline/Handlers/LanguageDetectionPipeHandlerTest.php | 97 +++++++++\n tests/Unit/Component/MediaPipeline/Handlers/TranscriptionParagraphsPipeHandlerTest.php | 100 ++++++++++\n tests/Unit/Component/MediaPipeline/Services/GetMediaPipelineStatesServiceTest.php | 6 +\n tests/Unit/Component/ParagraphBreaker/Services/ParagraphBuilderTest.php | 53 ++---\n tests/Unit/Component/ParagraphBreaker/Services/TranscriptionParagraphServiceTest.php | 547 +++++++++++++++++++++++++++++++++++++++++++++++++++\n tests/Unit/Component/ParagraphBreaker/Services/UtteranceBuilderTest.php | 73 ++++---\n tests/Unit/Component/Transcription/Formatter/TranscriptionFormatterTest.php | 8 +-\n tests/Unit/Component/Transcription/Job/TranscriptSegmentCompileJobTest.php | 101 ----------\n tests/Unit/Component/Transcription/Job/TranscriptSegmentsCompileJobTest.php | 220 +++++++++++++++++++++\n tests/Unit/Component/Transcription/Service/SearchServiceTest.php | 193 ++++++++++++++++++\n tests/Unit/Component/Transcription/Service/TranscriptSegmentsServiceTest.php | 465 -------------------------------------------\n tests/Unit/Component/Transcription/Service/TranscriptionServiceTest.php | 61 ++----\n tests/Unit/Component/Transcription/Service/TranslationServiceTest.php | 31 +--\n tests/Unit/Component/Transcription/TranscriptionProcessor/AssemblyAI/AssemblyAITest.php | 355 ++-------------------------------\n .../TranscriptionProcessor/AssemblyAI/Services/SubmitAudioFileForLanguageDetectionServiceTest.php | 220 +++++++++++++++++++++\n tests/Unit/Component/Transcription/VO/TranscriptSegmentTransformerTest.php | 68 -------\n tests/Unit/Jobs/Mailbox/SyncInboxTest.php | 2 +-\n tests/Unit/Services/Activity/HubSpot/AuthenticatedHttpClientTest.php | 207 ++------------------\n tests/Unit/Services/Activity/HubSpot/RedirectUrlResolverTest.php | 184 +++++++++++++++++\n tests/Unit/Services/Activity/HubSpot/ServiceTest.php | 205 ++++++++++++++++++-\n tests/Unit/Services/Activity/MeetingBotServiceOnSharingDataTest.php | 196 ++++++++++++++++++\n tests/Unit/Services/Crm/Hubspot/SyncRelatedActivityManagerTest.php | 105 ++++++++++\n 96 files changed, 4708 insertions(+), 3522 deletions(-)\n create mode 100644 .github/workflows/secfix.yml\n delete mode 100644 app/Component/ActivityAnalytics/Service/SentenceBreaker.php\n create mode 100644 app/Component/AiAutomation/Services/CrmFieldContentTrimmer.php\n create mode 100644 app/Component/MediaPipeline/Handlers/LanguageDetectionPipeHandler.php\n create mode 100644 app/Component/MediaPipeline/Handlers/TranscriptionParagraphsPipeHandler.php\n create mode 100644 app/Component/ParagraphBreaker/DTOs/TranscriptParagraph.php\n create mode 100644 app/Component/ParagraphBreaker/Services/TranscriptionParagraphsService.php\n delete mode 100644 app/Component/Transcription/Listener/TranscriptSegmentsCompileListener.php\n delete mode 100644 app/Component/Transcription/Service/TranscriptSegmentsService.php\n create mode 100644 app/Component/Transcription/TranscriptionProcessor/AssemblyAI/Services/FetchTranscriptService.php\n create mode 100644 app/Component/Transcription/TranscriptionProcessor/AssemblyAI/Services/SubmitAudioFileForLanguageDetectionService.php\n delete mode 100644 app/Component/Transcription/VO/TranscriptSegmentTransformer.php\n create mode 100644 app/Services/Activity/HubSpot/RedirectUrlResolver.php\n create mode 100644 app/Services/Activity/HubSpot/ZoomRecordingDetector.php\n create mode 100644 tests/Unit/Component/AiAutomation/Services/CrmFieldContentTrimmerTest.php\n create mode 100644 tests/Unit/Component/MediaPipeline/Handlers/LanguageDetectionPipeHandlerTest.php\n create mode 100644 tests/Unit/Component/MediaPipeline/Handlers/TranscriptionParagraphsPipeHandlerTest.php\n create mode 100644 tests/Unit/Component/ParagraphBreaker/Services/TranscriptionParagraphServiceTest.php\n delete mode 100644 tests/Unit/Component/Transcription/Job/TranscriptSegmentCompileJobTest.php\n create mode 100644 tests/Unit/Component/Transcription/Job/TranscriptSegmentsCompileJobTest.php\n delete mode 100644 tests/Unit/Component/Transcription/Service/TranscriptSegmentsServiceTest.php\n create mode 100644 tests/Unit/Component/Transcription/TranscriptionProcessor/AssemblyAI/Services/SubmitAudioFileForLanguageDetectionServiceTest.php\n delete mode 100644 tests/Unit/Component/Transcription/VO/TranscriptSegmentTransformerTest.php\n create mode 100644 tests/Unit/Services/Activity/HubSpot/RedirectUrlResolverTest.php\n create mode 100644 tests/Unit/Services/Activity/MeetingBotServiceOnSharingDataTest.php\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ git status\nOn branch JY-18909-automated-reports-ask-jiminny\nYour branch is ahead of 'origin/JY-18909-automated-reports-ask-jiminny' by 2 commits.\n (use \"git push\" to publish your local commits)\n\nChanges not staged for commit:\n (use \"git add <file>...\" to update what will be committed)\n (use \"git restore <file>...\" to discard changes in working directory)\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: .env.local\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: app/Console/Commands/JiminnyDebugCommand.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: app/Http/Controllers/API/ActivityController.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: app/Http/Controllers/Webhook/ReportController.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: app/Jobs/Team/SyncToIntercom.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: app/Services/PlaybackService.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: config/logging.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: routes/web.php\n\nUntracked files:\n (use \"git add <file>...\" to include in what will be committed)\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\t.env.nikilocal\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\t.env.other\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tWEBHOOK_FILTERING_IMPLEMENTATION.md\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tapp/Console/Commands/Crm/Hubspot/SimulateWebhooksCommand.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tapp/Console/Commands/Reports/CreateMockAskJiminnyReportResultCommand.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tids.txt\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\traw_sql_query.sql\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\ttests/Unit/Policies/CanAccessAiReportsTest.php\n\nno changes added to commit (use \"git add\" and/or \"git commit -a\")\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ git push\nEnumerating objects: 63, done.\nCounting objects: 100% (55/55), done.\nDelta compression using up to 8 threads\nCompressing objects: 100% (28/28), done.\nWriting objects: 100% (29/29), 3.77 KiB | 3.77 MiB/s, done.\nTotal 29 (delta 22), reused 0 (delta 0), pack-reused 0\nremote: Resolving deltas: 100% (22/22), completed with 20 local objects.\nremote: \nremote: GitHub found 24 vulnerabilities on jiminny/app's default branch (2 critical, 12 high, 9 moderate, 1 low). To find out more, visit:\nremote: https://github.com/jiminny/app/security/dependabot\nremote: \nTo github.com:jiminny/app.git\n e5eb1c8958..42e3e90859 JY-18909-automated-reports-ask-jiminny -> JY-18909-automated-reports-ask-jiminny\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix\ndocker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff \nPHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.\nPHP runtime: 8.3.30\nRunning analysis on 7 cores with 10 files per process.\nParallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!\nLoaded config default from \".php-cs-fixer.dist.php\".\n 5589/5589 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%\n\n\nFixed 0 of 5589 files in 39.991 seconds, 67.00 MB memory used\n\nWhat's next:\n Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1\n Learn more at https://docs.docker.com/go/debug-cli/\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix\ndocker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff \nPHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.\nPHP runtime: 8.3.30\nRunning analysis on 7 cores with 10 files per process.\nParallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!\nLoaded config default from \".php-cs-fixer.dist.php\".\n 5589/5589 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%\n\n\nFixed 0 of 5589 files in 39.687 seconds, 67.00 MB memory used\n\nWhat's next:\n Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1\n Learn more at https://docs.docker.com/go/debug-cli/\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix\ndocker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff \nPHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.\nPHP runtime: 8.3.30\nRunning analysis on 7 cores with 10 files per process.\nParallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!\nLoaded config default from \".php-cs-fixer.dist.php\".\n 5589/5589 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%\n\n\nFixed 0 of 5589 files in 52.171 seconds, 67.00 MB memory used\n\nWhat's next:\n Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1\n Learn more at https://docs.docker.com/go/debug-cli/\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix\ndocker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff \nPHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.\nPHP runtime: 8.3.30\nRunning analysis on 7 cores with 10 files per process.\nParallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!\nLoaded config default from \".php-cs-fixer.dist.php\".\n 5589/5589 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%\n\n\nFixed 0 of 5589 files in 49.458 seconds, 67.00 MB memory used\n\nWhat's next:\n Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1\n Learn more at https://docs.docker.com/go/debug-cli/\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $","depth":4,"value":"Last login: Sat Apr 11 12:38:35 on ttys007\n\nPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parents\n\nPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parents\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix\ndocker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff \nPHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.\nPHP runtime: 8.3.30\nRunning analysis on 7 cores with 10 files per process.\nParallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!\nLoaded config default from \".php-cs-fixer.dist.php\".\n 5579/5579 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%\n\n 1) app/Services/Kiosk/AutomatedReports/AskJiminnyReportActivityService.php (trailing_comma_in_multiline)\n ---------- begin diff ----------\n--- /home/jiminny/app/Services/Kiosk/AutomatedReports/AskJiminnyReportActivityService.php\n+++ /home/jiminny/app/Services/Kiosk/AutomatedReports/AskJiminnyReportActivityService.php\n@@ -51,7 +51,7 @@\n array_merge($requestParams, [\n 'limit' => self::DEFAULT_TOP_ACTIVITIES_COUNT,\n 'page' => 1,\n- 'sequence_number' => 1\n+ 'sequence_number' => 1,\n ]),\n $user->getTimezone()\n );\n\n ----------- end diff -----------\n\n\nFixed 1 of 5579 files in 108.308 seconds, 67.00 MB memory used\n\nWhat's next:\n Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1\n Learn more at https://docs.docker.com/go/debug-cli/\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ git pull\nremote: Enumerating objects: 185, done.\nremote: Counting objects: 100% (185/185), done.\nremote: Compressing objects: 100% (53/53), done.\nremote: Total 185 (delta 138), reused 176 (delta 130), pack-reused 0 (from 0)\nReceiving objects: 100% (185/185), 168.96 KiB | 1.05 MiB/s, done.\nResolving deltas: 100% (138/138), completed with 74 local objects.\nFrom github.com:jiminny/app\n * [new branch] JY-20255-quick-search-show-duration-and-call-conference-type -> origin/JY-20255-quick-search-show-duration-and-call-conference-type\n * [new branch] JY-20297-reposition-the-old-acs-feature -> origin/JY-20297-reposition-the-old-acs-feature\n * [new branch] JY-20541-stale-records-pr-1 -> origin/JY-20541-stale-records-pr-1\nMerge made by the 'ort' strategy.\n .env.production | 2 +-\n .env.production-eu | 2 +-\n .github/workflows/secfix.yml | 10 +\n app/Component/Activity/ActivityProcessingStateManager.php | 4 +\n app/Component/ActivityAnalytics/Service/ActivityAnalyticsService.php | 68 +++----\n app/Component/ActivityAnalytics/Service/LocalTriggerMatcherService.php | 40 ++--\n app/Component/ActivityAnalytics/Service/QuestionService.php | 137 +++++++------\n app/Component/ActivityAnalytics/Service/SentenceBreaker.php | 110 -----------\n app/Component/ActivityAnalytics/VO/TopicTriggerMatch.php | 4 +-\n app/Component/AiAutomation/Actions/UpdateCrmFieldAction.php | 14 +-\n app/Component/AiAutomation/Services/CrmFieldContentTrimmer.php | 92 +++++++++\n app/Component/Kiosk/Services/GetTranscriptionComparisonService.php | 61 +-----\n app/Component/LanguageDetection/Services/DetectLanguageService.php | 74 ++++++-\n app/Component/MediaPipeline/Handlers/LanguageDetectionPipeHandler.php | 37 ++++\n app/Component/MediaPipeline/Handlers/TranscriptionParagraphsPipeHandler.php | 45 +++++\n app/Component/MediaPipeline/MediaPipelineOrchestrator.php | 2 +\n app/Component/MediaPipeline/MediaPipelineServiceProvider.php | 8 +\n app/Component/ParagraphBreaker/DTOs/TranscriptParagraph.php | 46 +++++\n app/Component/ParagraphBreaker/Services/ParagraphBuilder.php | 29 ++-\n app/Component/ParagraphBreaker/Services/TranscriptionParagraphsService.php | 179 +++++++++++++++++\n app/Component/ParagraphBreaker/Services/UtteranceBuilder.php | 192 ++++++++----------\n app/Component/PlaybackPage/Snapshots/Listeners/GenerateSnapshotsListener.php | 6 +\n app/Component/Transcription/Formatter/TranscriptionFormatter.php | 11 +-\n app/Component/Transcription/Job/StartTranscriptDiarizationJob.php | 2 +-\n app/Component/Transcription/Job/TranscriptSegmentsCompileJob.php | 43 +++-\n app/Component/Transcription/Listener/TranscriptSegmentsCompileListener.php | 16 --\n app/Component/Transcription/Service/SearchService.php | 179 ++++++++++-------\n app/Component/Transcription/Service/TranscriptSegmentsService.php | 396 -------------------------------------\n app/Component/Transcription/Service/TranscriptionService.php | 142 ++------------\n app/Component/Transcription/Service/TranslationService.php | 59 ++----\n app/Component/Transcription/TranscriptionProcessor/AssemblyAI/AssemblyAI.php | 172 +---------------\n app/Component/Transcription/TranscriptionProcessor/AssemblyAI/Services/FetchTranscriptService.php | 48 +++++\n .../Transcription/TranscriptionProcessor/AssemblyAI/Services/SubmitAudioFileForLanguageDetectionService.php | 111 +++++++++++\n app/Component/Transcription/VO/TranscriptSegmentTransformer.php | 37 ----\n app/Console/Commands/Crm/Hubspot/RestoreDealAssociationsCommand.php | 344 ++++++++++++++++++++++++--------\n app/Console/Commands/EngagementStats/JiminnyEngagementStatsExplainCommand.php | 76 ++++---\n app/Console/Commands/Mailboxes/InboxSync.php | 9 +-\n app/Http/Controllers/Auth/SocialController.php | 2 +-\n app/Jobs/Mailbox/SyncInbox.php | 2 +-\n app/Listeners/Activities/Coaching/Intercom/CreateActivitySummarizedEvent.php | 6 +\n app/Listeners/Activities/Coaching/Intercom/CreateCommentedEvent.php | 6 +\n app/Listeners/Activities/Coaching/Intercom/CreateManagerCoachedEvent.php | 6 +\n app/Listeners/Activities/Coaching/Intercom/CreatePlayedEvent.php | 6 +\n app/Listeners/Activities/Conferences/Intercom/CreateHeldActivityEvent.php | 6 +\n app/Listeners/Activities/Crm/Summary/AbstractSummaryListener.php | 5 +-\n app/Listeners/Activities/Sms/Intercom/CreateSmsReceivedEvent.php | 6 +\n app/Models/Activity/TopicTrigger.php | 2 +-\n app/Models/Activity/Transcription.php | 29 +--\n app/Providers/EventServiceProvider.php | 1 -\n app/Providers/JiminnyServiceProvider.php | 23 ---\n app/Repositories/QuestionRepository.php | 32 +--\n app/Services/Activity/HubSpot/AuthenticatedHttpClient.php | 75 +------\n app/Services/Activity/HubSpot/RedirectUrlResolver.php | 91 +++++++++\n app/Services/Activity/HubSpot/Service.php | 17 +-\n app/Services/Activity/HubSpot/ZoomRecordingDetector.php | 27 +++\n app/Services/Activity/MeetingBotService.php | 5 +-\n app/Services/Calendar/GoogleCalendarService.php | 3 +\n app/Services/Calendar/OfficeCalendarService.php | 25 +--\n app/Services/Crm/Hubspot/SyncRelatedActivityManager.php | 9 +-\n composer.json | 1 -\n composer.lock | 51 +----\n front-end/src/components/DealInsights/Modals/AskAnythingModal.vue | 1 -\n front-end/src/components/Settings/Kiosk/ActivitiesSearch/ActivityTranscription.vue | 12 +-\n front-end/src/components/shared/Filters/InputText.vue | 2 +\n tests/Unit/Component/Activity/ActivityProcessingStateManagerTest.php | 17 +-\n tests/Unit/Component/ActivityAnalytics/Service/ActivityAnalyticsServiceTest.php | 137 ++++++++++++-\n tests/Unit/Component/ActivityAnalytics/Service/LocalTriggerMatcherServiceTest.php | 135 +++++++------\n tests/Unit/Component/ActivityAnalytics/Service/QuestionServiceTest.php | 610 ++++++++++++---------------------------------------------\n tests/Unit/Component/ActivityAnalytics/VO/TopicTriggerMatchTest.php | 15 ++\n tests/Unit/Component/AiAutomation/Actions/UpdateCrmFieldActionTest.php | 66 +++++++\n tests/Unit/Component/AiAutomation/Services/CrmFieldContentTrimmerTest.php | 282 ++++++++++++++++++++++++++\n tests/Unit/Component/ES/UpdateProcessManagerTest.php | 2 +-\n tests/Unit/Component/Kiosk/Services/GetTranscriptionComparisonServiceTest.php | 79 +++-----\n tests/Unit/Component/LanguageDetection/Services/DetectLanguageServiceTest.php | 132 +++++++++----\n tests/Unit/Component/MediaPipeline/Handlers/LanguageDetectionPipeHandlerTest.php | 97 +++++++++\n tests/Unit/Component/MediaPipeline/Handlers/TranscriptionParagraphsPipeHandlerTest.php | 100 ++++++++++\n tests/Unit/Component/MediaPipeline/Services/GetMediaPipelineStatesServiceTest.php | 6 +\n tests/Unit/Component/ParagraphBreaker/Services/ParagraphBuilderTest.php | 53 ++---\n tests/Unit/Component/ParagraphBreaker/Services/TranscriptionParagraphServiceTest.php | 547 +++++++++++++++++++++++++++++++++++++++++++++++++++\n tests/Unit/Component/ParagraphBreaker/Services/UtteranceBuilderTest.php | 73 ++++---\n tests/Unit/Component/Transcription/Formatter/TranscriptionFormatterTest.php | 8 +-\n tests/Unit/Component/Transcription/Job/TranscriptSegmentCompileJobTest.php | 101 ----------\n tests/Unit/Component/Transcription/Job/TranscriptSegmentsCompileJobTest.php | 220 +++++++++++++++++++++\n tests/Unit/Component/Transcription/Service/SearchServiceTest.php | 193 ++++++++++++++++++\n tests/Unit/Component/Transcription/Service/TranscriptSegmentsServiceTest.php | 465 -------------------------------------------\n tests/Unit/Component/Transcription/Service/TranscriptionServiceTest.php | 61 ++----\n tests/Unit/Component/Transcription/Service/TranslationServiceTest.php | 31 +--\n tests/Unit/Component/Transcription/TranscriptionProcessor/AssemblyAI/AssemblyAITest.php | 355 ++-------------------------------\n .../TranscriptionProcessor/AssemblyAI/Services/SubmitAudioFileForLanguageDetectionServiceTest.php | 220 +++++++++++++++++++++\n tests/Unit/Component/Transcription/VO/TranscriptSegmentTransformerTest.php | 68 -------\n tests/Unit/Jobs/Mailbox/SyncInboxTest.php | 2 +-\n tests/Unit/Services/Activity/HubSpot/AuthenticatedHttpClientTest.php | 207 ++------------------\n tests/Unit/Services/Activity/HubSpot/RedirectUrlResolverTest.php | 184 +++++++++++++++++\n tests/Unit/Services/Activity/HubSpot/ServiceTest.php | 205 ++++++++++++++++++-\n tests/Unit/Services/Activity/MeetingBotServiceOnSharingDataTest.php | 196 ++++++++++++++++++\n tests/Unit/Services/Crm/Hubspot/SyncRelatedActivityManagerTest.php | 105 ++++++++++\n 96 files changed, 4708 insertions(+), 3522 deletions(-)\n create mode 100644 .github/workflows/secfix.yml\n delete mode 100644 app/Component/ActivityAnalytics/Service/SentenceBreaker.php\n create mode 100644 app/Component/AiAutomation/Services/CrmFieldContentTrimmer.php\n create mode 100644 app/Component/MediaPipeline/Handlers/LanguageDetectionPipeHandler.php\n create mode 100644 app/Component/MediaPipeline/Handlers/TranscriptionParagraphsPipeHandler.php\n create mode 100644 app/Component/ParagraphBreaker/DTOs/TranscriptParagraph.php\n create mode 100644 app/Component/ParagraphBreaker/Services/TranscriptionParagraphsService.php\n delete mode 100644 app/Component/Transcription/Listener/TranscriptSegmentsCompileListener.php\n delete mode 100644 app/Component/Transcription/Service/TranscriptSegmentsService.php\n create mode 100644 app/Component/Transcription/TranscriptionProcessor/AssemblyAI/Services/FetchTranscriptService.php\n create mode 100644 app/Component/Transcription/TranscriptionProcessor/AssemblyAI/Services/SubmitAudioFileForLanguageDetectionService.php\n delete mode 100644 app/Component/Transcription/VO/TranscriptSegmentTransformer.php\n create mode 100644 app/Services/Activity/HubSpot/RedirectUrlResolver.php\n create mode 100644 app/Services/Activity/HubSpot/ZoomRecordingDetector.php\n create mode 100644 tests/Unit/Component/AiAutomation/Services/CrmFieldContentTrimmerTest.php\n create mode 100644 tests/Unit/Component/MediaPipeline/Handlers/LanguageDetectionPipeHandlerTest.php\n create mode 100644 tests/Unit/Component/MediaPipeline/Handlers/TranscriptionParagraphsPipeHandlerTest.php\n create mode 100644 tests/Unit/Component/ParagraphBreaker/Services/TranscriptionParagraphServiceTest.php\n delete mode 100644 tests/Unit/Component/Transcription/Job/TranscriptSegmentCompileJobTest.php\n create mode 100644 tests/Unit/Component/Transcription/Job/TranscriptSegmentsCompileJobTest.php\n delete mode 100644 tests/Unit/Component/Transcription/Service/TranscriptSegmentsServiceTest.php\n create mode 100644 tests/Unit/Component/Transcription/TranscriptionProcessor/AssemblyAI/Services/SubmitAudioFileForLanguageDetectionServiceTest.php\n delete mode 100644 tests/Unit/Component/Transcription/VO/TranscriptSegmentTransformerTest.php\n create mode 100644 tests/Unit/Services/Activity/HubSpot/RedirectUrlResolverTest.php\n create mode 100644 tests/Unit/Services/Activity/MeetingBotServiceOnSharingDataTest.php\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ git status\nOn branch JY-18909-automated-reports-ask-jiminny\nYour branch is ahead of 'origin/JY-18909-automated-reports-ask-jiminny' by 2 commits.\n (use \"git push\" to publish your local commits)\n\nChanges not staged for commit:\n (use \"git add <file>...\" to update what will be committed)\n (use \"git restore <file>...\" to discard changes in working directory)\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: .env.local\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: app/Console/Commands/JiminnyDebugCommand.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: app/Http/Controllers/API/ActivityController.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: app/Http/Controllers/Webhook/ReportController.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: app/Jobs/Team/SyncToIntercom.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: app/Services/PlaybackService.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: config/logging.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: routes/web.php\n\nUntracked files:\n (use \"git add <file>...\" to include in what will be committed)\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\t.env.nikilocal\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\t.env.other\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tWEBHOOK_FILTERING_IMPLEMENTATION.md\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tapp/Console/Commands/Crm/Hubspot/SimulateWebhooksCommand.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tapp/Console/Commands/Reports/CreateMockAskJiminnyReportResultCommand.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tids.txt\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\traw_sql_query.sql\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\ttests/Unit/Policies/CanAccessAiReportsTest.php\n\nno changes added to commit (use \"git add\" and/or \"git commit -a\")\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ git push\nEnumerating objects: 63, done.\nCounting objects: 100% (55/55), done.\nDelta compression using up to 8 threads\nCompressing objects: 100% (28/28), done.\nWriting objects: 100% (29/29), 3.77 KiB | 3.77 MiB/s, done.\nTotal 29 (delta 22), reused 0 (delta 0), pack-reused 0\nremote: Resolving deltas: 100% (22/22), completed with 20 local objects.\nremote: \nremote: GitHub found 24 vulnerabilities on jiminny/app's default branch (2 critical, 12 high, 9 moderate, 1 low). To find out more, visit:\nremote: https://github.com/jiminny/app/security/dependabot\nremote: \nTo github.com:jiminny/app.git\n e5eb1c8958..42e3e90859 JY-18909-automated-reports-ask-jiminny -> JY-18909-automated-reports-ask-jiminny\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix\ndocker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff \nPHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.\nPHP runtime: 8.3.30\nRunning analysis on 7 cores with 10 files per process.\nParallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!\nLoaded config default from \".php-cs-fixer.dist.php\".\n 5589/5589 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%\n\n\nFixed 0 of 5589 files in 39.991 seconds, 67.00 MB memory used\n\nWhat's next:\n Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1\n Learn more at https://docs.docker.com/go/debug-cli/\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix\ndocker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff \nPHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.\nPHP runtime: 8.3.30\nRunning analysis on 7 cores with 10 files per process.\nParallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!\nLoaded config default from \".php-cs-fixer.dist.php\".\n 5589/5589 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%\n\n\nFixed 0 of 5589 files in 39.687 seconds, 67.00 MB memory used\n\nWhat's next:\n Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1\n Learn more at https://docs.docker.com/go/debug-cli/\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix\ndocker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff \nPHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.\nPHP runtime: 8.3.30\nRunning analysis on 7 cores with 10 files per process.\nParallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!\nLoaded config default from \".php-cs-fixer.dist.php\".\n 5589/5589 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%\n\n\nFixed 0 of 5589 files in 52.171 seconds, 67.00 MB memory used\n\nWhat's next:\n Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1\n Learn more at https://docs.docker.com/go/debug-cli/\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix\ndocker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff \nPHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.\nPHP runtime: 8.3.30\nRunning analysis on 7 cores with 10 files per process.\nParallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!\nLoaded config default from \".php-cs-fixer.dist.php\".\n 5589/5589 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%\n\n\nFixed 0 of 5589 files in 49.458 seconds, 67.00 MB memory used\n\nWhat's next:\n Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1\n Learn more at https://docs.docker.com/go/debug-cli/\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $","is_focused":true},{"role":"AXRadioButton","text":"DOCKER","depth":2,"bounds":{"left":0.0,"top":0.05888889,"width":0.12291667,"height":0.026666667},"role_description":"radio button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Close Tab","depth":3,"bounds":{"left":0.004166667,"top":0.06333333,"width":0.011111111,"height":0.017777778},"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXRadioButton","text":"DEV (docker)","depth":2,"bounds":{"left":0.12291667,"top":0.05888889,"width":0.12291667,"height":0.026666667},"role_description":"radio button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Close Tab","depth":3,"bounds":{"left":0.12708333,"top":0.06333333,"width":0.011111111,"height":0.017777778},"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXRadioButton","text":"APP (-zsh)","depth":2,"bounds":{"left":0.24583334,"top":0.05888889,"width":0.12291667,"height":0.026666667},"role_description":"radio button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Close Tab","depth":3,"bounds":{"left":0.25,"top":0.06333333,"width":0.011111111,"height":0.017777778},"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXRadioButton","text":"ec2-user@ip-10-30-93-249:~ (nc)","depth":2,"bounds":{"left":0.36875,"top":0.05888889,"width":0.12291667,"height":0.026666667},"role_description":"radio button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Close Tab","depth":3,"bounds":{"left":0.37291667,"top":0.06333333,"width":0.011111111,"height":0.017777778},"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXRadioButton","text":"-zsh","depth":2,"bounds":{"left":0.49166667,"top":0.05888889,"width":0.12291667,"height":0.026666667},"role_description":"radio button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Close Tab","depth":3,"bounds":{"left":0.49583334,"top":0.06333333,"width":0.011111111,"height":0.017777778},"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXRadioButton","text":"-zsh","depth":2,"bounds":{"left":0.6145833,"top":0.05888889,"width":0.12291667,"height":0.026666667},"role_description":"radio button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Close Tab","depth":3,"bounds":{"left":0.61875,"top":0.06333333,"width":0.011111111,"height":0.017777778},"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXRadioButton","text":"-zsh","depth":2,"bounds":{"left":0.7375,"top":0.05888889,"width":0.12291667,"height":0.026666667},"role_description":"radio button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Close Tab","depth":3,"bounds":{"left":0.7416667,"top":0.06333333,"width":0.011111111,"height":0.017777778},"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXRadioButton","text":"✳ Unable to access screenpipe activity data (claude)","depth":2,"bounds":{"left":0.86041665,"top":0.05888889,"width":0.12291667,"height":0.026666667},"role_description":"radio button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Close Tab","depth":3,"bounds":{"left":0.8645833,"top":0.06333333,"width":0.011111111,"height":0.017777778},"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"⌥⌘1","depth":1,"bounds":{"left":0.9548611,"top":0.032222223,"width":0.03888889,"height":0.018888889},"automation_id":"_NS:8","role_description":"text"},{"role":"AXStaticText","text":"APP (-zsh)","depth":1,"bounds":{"left":0.47430557,"top":0.033333335,"width":0.05138889,"height":0.017777778},"role_description":"text"}]...
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7426414321496521432
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visual_change
|
accessibility
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NULL
|
Last login: Sat Apr 11 12:38:35 on ttys007
Poetry Last login: Sat Apr 11 12:38:35 on ttys007
Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parents
Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parents
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix
docker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff
PHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.
PHP runtime: 8.3.30
Running analysis on 7 cores with 10 files per process.
Parallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!
Loaded config default from ".php-cs-fixer.dist.php".
5579/5579 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%
1) app/Services/Kiosk/AutomatedReports/AskJiminnyReportActivityService.php (trailing_comma_in_multiline)
---------- begin diff ----------
--- /home/jiminny/app/Services/Kiosk/AutomatedReports/AskJiminnyReportActivityService.php
+++ /home/jiminny/app/Services/Kiosk/AutomatedReports/AskJiminnyReportActivityService.php
@@ -51,7 +51,7 @@
array_merge($requestParams, [
'limit' => self::DEFAULT_TOP_ACTIVITIES_COUNT,
'page' => 1,
- 'sequence_number' => 1
+ 'sequence_number' => 1,
]),
$user->getTimezone()
);
----------- end diff -----------
Fixed 1 of 5579 files in 108.308 seconds, 67.00 MB memory used
What's next:
Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1
Learn more at [URL_WITH_CREDENTIALS] ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ git status
On branch JY-18909-automated-reports-ask-jiminny
Your branch is ahead of 'origin/JY-18909-automated-reports-ask-jiminny' by 2 commits.
(use "git push" to publish your local commits)
Changes not staged for commit:
(use "git add <file>..." to update what will be committed)
(use "git restore <file>..." to discard changes in working directory)
modified: .env.local
modified: app/Console/Commands/JiminnyDebugCommand.php
modified: app/Http/Controllers/API/ActivityController.php
modified: app/Http/Controllers/Webhook/ReportController.php
modified: app/Jobs/Team/SyncToIntercom.php
modified: app/Services/PlaybackService.php
modified: config/logging.php
modified: routes/web.php
Untracked files:
(use "git add <file>..." to include in what will be committed)
.env.nikilocal
.env.other
WEBHOOK_FILTERING_IMPLEMENTATION.md
app/Console/Commands/Crm/Hubspot/SimulateWebhooksCommand.php
app/Console/Commands/Reports/CreateMockAskJiminnyReportResultCommand.php
ids.txt
raw_sql_query.sql
tests/Unit/Policies/CanAccessAiReportsTest.php
no changes added to commit (use "git add" and/or "git commit -a")
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ git push
Enumerating objects: 63, done.
Counting objects: 100% (55/55), done.
Delta compression using up to 8 threads
Compressing objects: 100% (28/28), done.
Writing objects: 100% (29/29), 3.77 KiB | 3.77 MiB/s, done.
Total 29 (delta 22), reused 0 (delta 0), pack-reused 0
remote: Resolving deltas: 100% (22/22), completed with 20 local objects.
remote:
remote: GitHub found 24 vulnerabilities on jiminny/app's default branch (2 critical, 12 high, 9 moderate, 1 low). To find out more, visit:
remote: [URL_WITH_CREDENTIALS] ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix
docker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff
PHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.
PHP runtime: 8.3.30
Running analysis on 7 cores with 10 files per process.
Parallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!
Loaded config default from ".php-cs-fixer.dist.php".
5589/5589 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%
Fixed 0 of 5589 files in 39.991 seconds, 67.00 MB memory used
What's next:
Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1
Learn more at [URL_WITH_CREDENTIALS] ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix
docker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff
PHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.
PHP runtime: 8.3.30
Running analysis on 7 cores with 10 files per process.
Parallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!
Loaded config default from ".php-cs-fixer.dist.php".
5589/5589 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%
Fixed 0 of 5589 files in 52.171 seconds, 67.00 MB memory used
What's next:
Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1
Learn more at [URL_WITH_CREDENTIALS] ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $
DOCKER
Close Tab
DEV (docker)
Close Tab
APP (-zsh)
Close Tab
ec2-user@ip-10-30-93-249:~ (nc)
Close Tab
-zsh
Close Tab
-zsh
Close Tab
-zsh
Close Tab
✳ Unable to access screenpipe activity data (claude)
Close Tab
⌥⌘1
APP (-zsh)...
|
NULL
|
|
13237
|
289
|
44
|
2026-04-14T12:15:37.732064+00:00
|
/Users/lukas/.screenpipe/data/data/2026-04-14/1776 /Users/lukas/.screenpipe/data/data/2026-04-14/1776168937732_m2.jpg...
|
iTerm2
|
APP (-zsh)
|
True
|
NULL
|
monitor_2
|
NULL
|
NULL
|
NULL
|
NULL
|
Last login: Sat Apr 11 12:38:35 on ttys007
Poetry Last login: Sat Apr 11 12:38:35 on ttys007
Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parents
Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parents
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix
docker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff
PHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.
PHP runtime: 8.3.30
Running analysis on 7 cores with 10 files per process.
Parallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!
Loaded config default from ".php-cs-fixer.dist.php".
5579/5579 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%
1) app/Services/Kiosk/AutomatedReports/AskJiminnyReportActivityService.php (trailing_comma_in_multiline)
---------- begin diff ----------
--- /home/jiminny/app/Services/Kiosk/AutomatedReports/AskJiminnyReportActivityService.php
+++ /home/jiminny/app/Services/Kiosk/AutomatedReports/AskJiminnyReportActivityService.php
@@ -51,7 +51,7 @@
array_merge($requestParams, [
'limit' => self::DEFAULT_TOP_ACTIVITIES_COUNT,
'page' => 1,
- 'sequence_number' => 1
+ 'sequence_number' => 1,
]),
$user->getTimezone()
);
----------- end diff -----------
Fixed 1 of 5579 files in 108.308 seconds, 67.00 MB memory used
What's next:
Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1
Learn more at [URL_WITH_CREDENTIALS] ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ git status
On branch JY-18909-automated-reports-ask-jiminny
Your branch is ahead of 'origin/JY-18909-automated-reports-ask-jiminny' by 2 commits.
(use "git push" to publish your local commits)
Changes not staged for commit:
(use "git add <file>..." to update what will be committed)
(use "git restore <file>..." to discard changes in working directory)
modified: .env.local
modified: app/Console/Commands/JiminnyDebugCommand.php
modified: app/Http/Controllers/API/ActivityController.php
modified: app/Http/Controllers/Webhook/ReportController.php
modified: app/Jobs/Team/SyncToIntercom.php
modified: app/Services/PlaybackService.php
modified: config/logging.php
modified: routes/web.php
Untracked files:
(use "git add <file>..." to include in what will be committed)
.env.nikilocal
.env.other
WEBHOOK_FILTERING_IMPLEMENTATION.md
app/Console/Commands/Crm/Hubspot/SimulateWebhooksCommand.php
app/Console/Commands/Reports/CreateMockAskJiminnyReportResultCommand.php
ids.txt
raw_sql_query.sql
tests/Unit/Policies/CanAccessAiReportsTest.php
no changes added to commit (use "git add" and/or "git commit -a")
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ git push
Enumerating objects: 63, done.
Counting objects: 100% (55/55), done.
Delta compression using up to 8 threads
Compressing objects: 100% (28/28), done.
Writing objects: 100% (29/29), 3.77 KiB | 3.77 MiB/s, done.
Total 29 (delta 22), reused 0 (delta 0), pack-reused 0
remote: Resolving deltas: 100% (22/22), completed with 20 local objects.
remote:
remote: GitHub found 24 vulnerabilities on jiminny/app's default branch (2 critical, 12 high, 9 moderate, 1 low). To find out more, visit:
remote: [URL_WITH_CREDENTIALS] ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix
docker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff
PHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.
PHP runtime: 8.3.30
Running analysis on 7 cores with 10 files per process.
Parallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!
Loaded config default from ".php-cs-fixer.dist.php".
5589/5589 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%
Fixed 0 of 5589 files in 39.991 seconds, 67.00 MB memory used
What's next:
Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1
Learn more at [URL_WITH_CREDENTIALS] ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix
docker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff
PHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.
PHP runtime: 8.3.30
Running analysis on 7 cores with 10 files per process.
Parallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!
Loaded config default from ".php-cs-fixer.dist.php".
5589/5589 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%
Fixed 0 of 5589 files in 52.171 seconds, 67.00 MB memory used
What's next:
Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1
Learn more at [URL_WITH_CREDENTIALS] ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $
DOCKER
Close Tab
DEV (docker)
Close Tab
APP (-zsh)
Close Tab
ec2-user@ip-10-30-93-249:~ (nc)
Close Tab
-zsh
Close Tab
-zsh
Close Tab
-zsh
Close Tab
✳ Unable to access screenpipe activity data (claude)
Close Tab
⌥⌘1
APP (-zsh)...
|
[{"role":"AXTextArea","text [{"role":"AXTextArea","text":"Last login: Sat Apr 11 12:38:35 on ttys007\n\nPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parents\n\nPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parents\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix\ndocker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff \nPHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.\nPHP runtime: 8.3.30\nRunning analysis on 7 cores with 10 files per process.\nParallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!\nLoaded config default from \".php-cs-fixer.dist.php\".\n 5579/5579 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%\n\n 1) app/Services/Kiosk/AutomatedReports/AskJiminnyReportActivityService.php (trailing_comma_in_multiline)\n ---------- begin diff ----------\n--- /home/jiminny/app/Services/Kiosk/AutomatedReports/AskJiminnyReportActivityService.php\n+++ /home/jiminny/app/Services/Kiosk/AutomatedReports/AskJiminnyReportActivityService.php\n@@ -51,7 +51,7 @@\n array_merge($requestParams, [\n 'limit' => self::DEFAULT_TOP_ACTIVITIES_COUNT,\n 'page' => 1,\n- 'sequence_number' => 1\n+ 'sequence_number' => 1,\n ]),\n $user->getTimezone()\n );\n\n ----------- end diff -----------\n\n\nFixed 1 of 5579 files in 108.308 seconds, 67.00 MB memory used\n\nWhat's next:\n Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1\n Learn more at https://docs.docker.com/go/debug-cli/\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ git pull\nremote: Enumerating objects: 185, done.\nremote: Counting objects: 100% (185/185), done.\nremote: Compressing objects: 100% (53/53), done.\nremote: Total 185 (delta 138), reused 176 (delta 130), pack-reused 0 (from 0)\nReceiving objects: 100% (185/185), 168.96 KiB | 1.05 MiB/s, done.\nResolving deltas: 100% (138/138), completed with 74 local objects.\nFrom github.com:jiminny/app\n * [new branch] JY-20255-quick-search-show-duration-and-call-conference-type -> origin/JY-20255-quick-search-show-duration-and-call-conference-type\n * [new branch] JY-20297-reposition-the-old-acs-feature -> origin/JY-20297-reposition-the-old-acs-feature\n * [new branch] JY-20541-stale-records-pr-1 -> origin/JY-20541-stale-records-pr-1\nMerge made by the 'ort' strategy.\n .env.production | 2 +-\n .env.production-eu | 2 +-\n .github/workflows/secfix.yml | 10 +\n app/Component/Activity/ActivityProcessingStateManager.php | 4 +\n app/Component/ActivityAnalytics/Service/ActivityAnalyticsService.php | 68 +++----\n app/Component/ActivityAnalytics/Service/LocalTriggerMatcherService.php | 40 ++--\n app/Component/ActivityAnalytics/Service/QuestionService.php | 137 +++++++------\n app/Component/ActivityAnalytics/Service/SentenceBreaker.php | 110 -----------\n app/Component/ActivityAnalytics/VO/TopicTriggerMatch.php | 4 +-\n app/Component/AiAutomation/Actions/UpdateCrmFieldAction.php | 14 +-\n app/Component/AiAutomation/Services/CrmFieldContentTrimmer.php | 92 +++++++++\n app/Component/Kiosk/Services/GetTranscriptionComparisonService.php | 61 +-----\n app/Component/LanguageDetection/Services/DetectLanguageService.php | 74 ++++++-\n app/Component/MediaPipeline/Handlers/LanguageDetectionPipeHandler.php | 37 ++++\n app/Component/MediaPipeline/Handlers/TranscriptionParagraphsPipeHandler.php | 45 +++++\n app/Component/MediaPipeline/MediaPipelineOrchestrator.php | 2 +\n app/Component/MediaPipeline/MediaPipelineServiceProvider.php | 8 +\n app/Component/ParagraphBreaker/DTOs/TranscriptParagraph.php | 46 +++++\n app/Component/ParagraphBreaker/Services/ParagraphBuilder.php | 29 ++-\n app/Component/ParagraphBreaker/Services/TranscriptionParagraphsService.php | 179 +++++++++++++++++\n app/Component/ParagraphBreaker/Services/UtteranceBuilder.php | 192 ++++++++----------\n app/Component/PlaybackPage/Snapshots/Listeners/GenerateSnapshotsListener.php | 6 +\n app/Component/Transcription/Formatter/TranscriptionFormatter.php | 11 +-\n app/Component/Transcription/Job/StartTranscriptDiarizationJob.php | 2 +-\n app/Component/Transcription/Job/TranscriptSegmentsCompileJob.php | 43 +++-\n app/Component/Transcription/Listener/TranscriptSegmentsCompileListener.php | 16 --\n app/Component/Transcription/Service/SearchService.php | 179 ++++++++++-------\n app/Component/Transcription/Service/TranscriptSegmentsService.php | 396 -------------------------------------\n app/Component/Transcription/Service/TranscriptionService.php | 142 ++------------\n app/Component/Transcription/Service/TranslationService.php | 59 ++----\n app/Component/Transcription/TranscriptionProcessor/AssemblyAI/AssemblyAI.php | 172 +---------------\n app/Component/Transcription/TranscriptionProcessor/AssemblyAI/Services/FetchTranscriptService.php | 48 +++++\n .../Transcription/TranscriptionProcessor/AssemblyAI/Services/SubmitAudioFileForLanguageDetectionService.php | 111 +++++++++++\n app/Component/Transcription/VO/TranscriptSegmentTransformer.php | 37 ----\n app/Console/Commands/Crm/Hubspot/RestoreDealAssociationsCommand.php | 344 ++++++++++++++++++++++++--------\n app/Console/Commands/EngagementStats/JiminnyEngagementStatsExplainCommand.php | 76 ++++---\n app/Console/Commands/Mailboxes/InboxSync.php | 9 +-\n app/Http/Controllers/Auth/SocialController.php | 2 +-\n app/Jobs/Mailbox/SyncInbox.php | 2 +-\n app/Listeners/Activities/Coaching/Intercom/CreateActivitySummarizedEvent.php | 6 +\n app/Listeners/Activities/Coaching/Intercom/CreateCommentedEvent.php | 6 +\n app/Listeners/Activities/Coaching/Intercom/CreateManagerCoachedEvent.php | 6 +\n app/Listeners/Activities/Coaching/Intercom/CreatePlayedEvent.php | 6 +\n app/Listeners/Activities/Conferences/Intercom/CreateHeldActivityEvent.php | 6 +\n app/Listeners/Activities/Crm/Summary/AbstractSummaryListener.php | 5 +-\n app/Listeners/Activities/Sms/Intercom/CreateSmsReceivedEvent.php | 6 +\n app/Models/Activity/TopicTrigger.php | 2 +-\n app/Models/Activity/Transcription.php | 29 +--\n app/Providers/EventServiceProvider.php | 1 -\n app/Providers/JiminnyServiceProvider.php | 23 ---\n app/Repositories/QuestionRepository.php | 32 +--\n app/Services/Activity/HubSpot/AuthenticatedHttpClient.php | 75 +------\n app/Services/Activity/HubSpot/RedirectUrlResolver.php | 91 +++++++++\n app/Services/Activity/HubSpot/Service.php | 17 +-\n app/Services/Activity/HubSpot/ZoomRecordingDetector.php | 27 +++\n app/Services/Activity/MeetingBotService.php | 5 +-\n app/Services/Calendar/GoogleCalendarService.php | 3 +\n app/Services/Calendar/OfficeCalendarService.php | 25 +--\n app/Services/Crm/Hubspot/SyncRelatedActivityManager.php | 9 +-\n composer.json | 1 -\n composer.lock | 51 +----\n front-end/src/components/DealInsights/Modals/AskAnythingModal.vue | 1 -\n front-end/src/components/Settings/Kiosk/ActivitiesSearch/ActivityTranscription.vue | 12 +-\n front-end/src/components/shared/Filters/InputText.vue | 2 +\n tests/Unit/Component/Activity/ActivityProcessingStateManagerTest.php | 17 +-\n tests/Unit/Component/ActivityAnalytics/Service/ActivityAnalyticsServiceTest.php | 137 ++++++++++++-\n tests/Unit/Component/ActivityAnalytics/Service/LocalTriggerMatcherServiceTest.php | 135 +++++++------\n tests/Unit/Component/ActivityAnalytics/Service/QuestionServiceTest.php | 610 ++++++++++++---------------------------------------------\n tests/Unit/Component/ActivityAnalytics/VO/TopicTriggerMatchTest.php | 15 ++\n tests/Unit/Component/AiAutomation/Actions/UpdateCrmFieldActionTest.php | 66 +++++++\n tests/Unit/Component/AiAutomation/Services/CrmFieldContentTrimmerTest.php | 282 ++++++++++++++++++++++++++\n tests/Unit/Component/ES/UpdateProcessManagerTest.php | 2 +-\n tests/Unit/Component/Kiosk/Services/GetTranscriptionComparisonServiceTest.php | 79 +++-----\n tests/Unit/Component/LanguageDetection/Services/DetectLanguageServiceTest.php | 132 +++++++++----\n tests/Unit/Component/MediaPipeline/Handlers/LanguageDetectionPipeHandlerTest.php | 97 +++++++++\n tests/Unit/Component/MediaPipeline/Handlers/TranscriptionParagraphsPipeHandlerTest.php | 100 ++++++++++\n tests/Unit/Component/MediaPipeline/Services/GetMediaPipelineStatesServiceTest.php | 6 +\n tests/Unit/Component/ParagraphBreaker/Services/ParagraphBuilderTest.php | 53 ++---\n tests/Unit/Component/ParagraphBreaker/Services/TranscriptionParagraphServiceTest.php | 547 +++++++++++++++++++++++++++++++++++++++++++++++++++\n tests/Unit/Component/ParagraphBreaker/Services/UtteranceBuilderTest.php | 73 ++++---\n tests/Unit/Component/Transcription/Formatter/TranscriptionFormatterTest.php | 8 +-\n tests/Unit/Component/Transcription/Job/TranscriptSegmentCompileJobTest.php | 101 ----------\n tests/Unit/Component/Transcription/Job/TranscriptSegmentsCompileJobTest.php | 220 +++++++++++++++++++++\n tests/Unit/Component/Transcription/Service/SearchServiceTest.php | 193 ++++++++++++++++++\n tests/Unit/Component/Transcription/Service/TranscriptSegmentsServiceTest.php | 465 -------------------------------------------\n tests/Unit/Component/Transcription/Service/TranscriptionServiceTest.php | 61 ++----\n tests/Unit/Component/Transcription/Service/TranslationServiceTest.php | 31 +--\n tests/Unit/Component/Transcription/TranscriptionProcessor/AssemblyAI/AssemblyAITest.php | 355 ++-------------------------------\n .../TranscriptionProcessor/AssemblyAI/Services/SubmitAudioFileForLanguageDetectionServiceTest.php | 220 +++++++++++++++++++++\n tests/Unit/Component/Transcription/VO/TranscriptSegmentTransformerTest.php | 68 -------\n tests/Unit/Jobs/Mailbox/SyncInboxTest.php | 2 +-\n tests/Unit/Services/Activity/HubSpot/AuthenticatedHttpClientTest.php | 207 ++------------------\n tests/Unit/Services/Activity/HubSpot/RedirectUrlResolverTest.php | 184 +++++++++++++++++\n tests/Unit/Services/Activity/HubSpot/ServiceTest.php | 205 ++++++++++++++++++-\n tests/Unit/Services/Activity/MeetingBotServiceOnSharingDataTest.php | 196 ++++++++++++++++++\n tests/Unit/Services/Crm/Hubspot/SyncRelatedActivityManagerTest.php | 105 ++++++++++\n 96 files changed, 4708 insertions(+), 3522 deletions(-)\n create mode 100644 .github/workflows/secfix.yml\n delete mode 100644 app/Component/ActivityAnalytics/Service/SentenceBreaker.php\n create mode 100644 app/Component/AiAutomation/Services/CrmFieldContentTrimmer.php\n create mode 100644 app/Component/MediaPipeline/Handlers/LanguageDetectionPipeHandler.php\n create mode 100644 app/Component/MediaPipeline/Handlers/TranscriptionParagraphsPipeHandler.php\n create mode 100644 app/Component/ParagraphBreaker/DTOs/TranscriptParagraph.php\n create mode 100644 app/Component/ParagraphBreaker/Services/TranscriptionParagraphsService.php\n delete mode 100644 app/Component/Transcription/Listener/TranscriptSegmentsCompileListener.php\n delete mode 100644 app/Component/Transcription/Service/TranscriptSegmentsService.php\n create mode 100644 app/Component/Transcription/TranscriptionProcessor/AssemblyAI/Services/FetchTranscriptService.php\n create mode 100644 app/Component/Transcription/TranscriptionProcessor/AssemblyAI/Services/SubmitAudioFileForLanguageDetectionService.php\n delete mode 100644 app/Component/Transcription/VO/TranscriptSegmentTransformer.php\n create mode 100644 app/Services/Activity/HubSpot/RedirectUrlResolver.php\n create mode 100644 app/Services/Activity/HubSpot/ZoomRecordingDetector.php\n create mode 100644 tests/Unit/Component/AiAutomation/Services/CrmFieldContentTrimmerTest.php\n create mode 100644 tests/Unit/Component/MediaPipeline/Handlers/LanguageDetectionPipeHandlerTest.php\n create mode 100644 tests/Unit/Component/MediaPipeline/Handlers/TranscriptionParagraphsPipeHandlerTest.php\n create mode 100644 tests/Unit/Component/ParagraphBreaker/Services/TranscriptionParagraphServiceTest.php\n delete mode 100644 tests/Unit/Component/Transcription/Job/TranscriptSegmentCompileJobTest.php\n create mode 100644 tests/Unit/Component/Transcription/Job/TranscriptSegmentsCompileJobTest.php\n delete mode 100644 tests/Unit/Component/Transcription/Service/TranscriptSegmentsServiceTest.php\n create mode 100644 tests/Unit/Component/Transcription/TranscriptionProcessor/AssemblyAI/Services/SubmitAudioFileForLanguageDetectionServiceTest.php\n delete mode 100644 tests/Unit/Component/Transcription/VO/TranscriptSegmentTransformerTest.php\n create mode 100644 tests/Unit/Services/Activity/HubSpot/RedirectUrlResolverTest.php\n create mode 100644 tests/Unit/Services/Activity/MeetingBotServiceOnSharingDataTest.php\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ git status\nOn branch JY-18909-automated-reports-ask-jiminny\nYour branch is ahead of 'origin/JY-18909-automated-reports-ask-jiminny' by 2 commits.\n (use \"git push\" to publish your local commits)\n\nChanges not staged for commit:\n (use \"git add <file>...\" to update what will be committed)\n (use \"git restore <file>...\" to discard changes in working directory)\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: .env.local\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: app/Console/Commands/JiminnyDebugCommand.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: app/Http/Controllers/API/ActivityController.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: app/Http/Controllers/Webhook/ReportController.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: app/Jobs/Team/SyncToIntercom.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: app/Services/PlaybackService.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: config/logging.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: routes/web.php\n\nUntracked files:\n (use \"git add <file>...\" to include in what will be committed)\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\t.env.nikilocal\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\t.env.other\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tWEBHOOK_FILTERING_IMPLEMENTATION.md\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tapp/Console/Commands/Crm/Hubspot/SimulateWebhooksCommand.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tapp/Console/Commands/Reports/CreateMockAskJiminnyReportResultCommand.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tids.txt\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\traw_sql_query.sql\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\ttests/Unit/Policies/CanAccessAiReportsTest.php\n\nno changes added to commit (use \"git add\" and/or \"git commit -a\")\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ git push\nEnumerating objects: 63, done.\nCounting objects: 100% (55/55), done.\nDelta compression using up to 8 threads\nCompressing objects: 100% (28/28), done.\nWriting objects: 100% (29/29), 3.77 KiB | 3.77 MiB/s, done.\nTotal 29 (delta 22), reused 0 (delta 0), pack-reused 0\nremote: Resolving deltas: 100% (22/22), completed with 20 local objects.\nremote: \nremote: GitHub found 24 vulnerabilities on jiminny/app's default branch (2 critical, 12 high, 9 moderate, 1 low). To find out more, visit:\nremote: https://github.com/jiminny/app/security/dependabot\nremote: \nTo github.com:jiminny/app.git\n e5eb1c8958..42e3e90859 JY-18909-automated-reports-ask-jiminny -> JY-18909-automated-reports-ask-jiminny\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix\ndocker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff \nPHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.\nPHP runtime: 8.3.30\nRunning analysis on 7 cores with 10 files per process.\nParallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!\nLoaded config default from \".php-cs-fixer.dist.php\".\n 5589/5589 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%\n\n\nFixed 0 of 5589 files in 39.991 seconds, 67.00 MB memory used\n\nWhat's next:\n Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1\n Learn more at https://docs.docker.com/go/debug-cli/\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix\ndocker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff \nPHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.\nPHP runtime: 8.3.30\nRunning analysis on 7 cores with 10 files per process.\nParallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!\nLoaded config default from \".php-cs-fixer.dist.php\".\n 5589/5589 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%\n\n\nFixed 0 of 5589 files in 39.687 seconds, 67.00 MB memory used\n\nWhat's next:\n Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1\n Learn more at https://docs.docker.com/go/debug-cli/\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix\ndocker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff \nPHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.\nPHP runtime: 8.3.30\nRunning analysis on 7 cores with 10 files per process.\nParallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!\nLoaded config default from \".php-cs-fixer.dist.php\".\n 5589/5589 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%\n\n\nFixed 0 of 5589 files in 52.171 seconds, 67.00 MB memory used\n\nWhat's next:\n Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1\n Learn more at https://docs.docker.com/go/debug-cli/\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix\ndocker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff \nPHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.\nPHP runtime: 8.3.30\nRunning analysis on 7 cores with 10 files per process.\nParallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!\nLoaded config default from \".php-cs-fixer.dist.php\".\n 5589/5589 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%\n\n\nFixed 0 of 5589 files in 49.458 seconds, 67.00 MB memory used\n\nWhat's next:\n Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1\n Learn more at https://docs.docker.com/go/debug-cli/\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $","depth":4,"value":"Last login: Sat Apr 11 12:38:35 on ttys007\n\nPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parents\n\nPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parents\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix\ndocker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff \nPHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.\nPHP runtime: 8.3.30\nRunning analysis on 7 cores with 10 files per process.\nParallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!\nLoaded config default from \".php-cs-fixer.dist.php\".\n 5579/5579 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%\n\n 1) app/Services/Kiosk/AutomatedReports/AskJiminnyReportActivityService.php (trailing_comma_in_multiline)\n ---------- begin diff ----------\n--- /home/jiminny/app/Services/Kiosk/AutomatedReports/AskJiminnyReportActivityService.php\n+++ /home/jiminny/app/Services/Kiosk/AutomatedReports/AskJiminnyReportActivityService.php\n@@ -51,7 +51,7 @@\n array_merge($requestParams, [\n 'limit' => self::DEFAULT_TOP_ACTIVITIES_COUNT,\n 'page' => 1,\n- 'sequence_number' => 1\n+ 'sequence_number' => 1,\n ]),\n $user->getTimezone()\n );\n\n ----------- end diff -----------\n\n\nFixed 1 of 5579 files in 108.308 seconds, 67.00 MB memory used\n\nWhat's next:\n Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1\n Learn more at https://docs.docker.com/go/debug-cli/\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ git pull\nremote: Enumerating objects: 185, done.\nremote: Counting objects: 100% (185/185), done.\nremote: Compressing objects: 100% (53/53), done.\nremote: Total 185 (delta 138), reused 176 (delta 130), pack-reused 0 (from 0)\nReceiving objects: 100% (185/185), 168.96 KiB | 1.05 MiB/s, done.\nResolving deltas: 100% (138/138), completed with 74 local objects.\nFrom github.com:jiminny/app\n * [new branch] JY-20255-quick-search-show-duration-and-call-conference-type -> origin/JY-20255-quick-search-show-duration-and-call-conference-type\n * [new branch] JY-20297-reposition-the-old-acs-feature -> origin/JY-20297-reposition-the-old-acs-feature\n * [new branch] JY-20541-stale-records-pr-1 -> origin/JY-20541-stale-records-pr-1\nMerge made by the 'ort' strategy.\n .env.production | 2 +-\n .env.production-eu | 2 +-\n .github/workflows/secfix.yml | 10 +\n app/Component/Activity/ActivityProcessingStateManager.php | 4 +\n app/Component/ActivityAnalytics/Service/ActivityAnalyticsService.php | 68 +++----\n app/Component/ActivityAnalytics/Service/LocalTriggerMatcherService.php | 40 ++--\n app/Component/ActivityAnalytics/Service/QuestionService.php | 137 +++++++------\n app/Component/ActivityAnalytics/Service/SentenceBreaker.php | 110 -----------\n app/Component/ActivityAnalytics/VO/TopicTriggerMatch.php | 4 +-\n app/Component/AiAutomation/Actions/UpdateCrmFieldAction.php | 14 +-\n app/Component/AiAutomation/Services/CrmFieldContentTrimmer.php | 92 +++++++++\n app/Component/Kiosk/Services/GetTranscriptionComparisonService.php | 61 +-----\n app/Component/LanguageDetection/Services/DetectLanguageService.php | 74 ++++++-\n app/Component/MediaPipeline/Handlers/LanguageDetectionPipeHandler.php | 37 ++++\n app/Component/MediaPipeline/Handlers/TranscriptionParagraphsPipeHandler.php | 45 +++++\n app/Component/MediaPipeline/MediaPipelineOrchestrator.php | 2 +\n app/Component/MediaPipeline/MediaPipelineServiceProvider.php | 8 +\n app/Component/ParagraphBreaker/DTOs/TranscriptParagraph.php | 46 +++++\n app/Component/ParagraphBreaker/Services/ParagraphBuilder.php | 29 ++-\n app/Component/ParagraphBreaker/Services/TranscriptionParagraphsService.php | 179 +++++++++++++++++\n app/Component/ParagraphBreaker/Services/UtteranceBuilder.php | 192 ++++++++----------\n app/Component/PlaybackPage/Snapshots/Listeners/GenerateSnapshotsListener.php | 6 +\n app/Component/Transcription/Formatter/TranscriptionFormatter.php | 11 +-\n app/Component/Transcription/Job/StartTranscriptDiarizationJob.php | 2 +-\n app/Component/Transcription/Job/TranscriptSegmentsCompileJob.php | 43 +++-\n app/Component/Transcription/Listener/TranscriptSegmentsCompileListener.php | 16 --\n app/Component/Transcription/Service/SearchService.php | 179 ++++++++++-------\n app/Component/Transcription/Service/TranscriptSegmentsService.php | 396 -------------------------------------\n app/Component/Transcription/Service/TranscriptionService.php | 142 ++------------\n app/Component/Transcription/Service/TranslationService.php | 59 ++----\n app/Component/Transcription/TranscriptionProcessor/AssemblyAI/AssemblyAI.php | 172 +---------------\n app/Component/Transcription/TranscriptionProcessor/AssemblyAI/Services/FetchTranscriptService.php | 48 +++++\n .../Transcription/TranscriptionProcessor/AssemblyAI/Services/SubmitAudioFileForLanguageDetectionService.php | 111 +++++++++++\n app/Component/Transcription/VO/TranscriptSegmentTransformer.php | 37 ----\n app/Console/Commands/Crm/Hubspot/RestoreDealAssociationsCommand.php | 344 ++++++++++++++++++++++++--------\n app/Console/Commands/EngagementStats/JiminnyEngagementStatsExplainCommand.php | 76 ++++---\n app/Console/Commands/Mailboxes/InboxSync.php | 9 +-\n app/Http/Controllers/Auth/SocialController.php | 2 +-\n app/Jobs/Mailbox/SyncInbox.php | 2 +-\n app/Listeners/Activities/Coaching/Intercom/CreateActivitySummarizedEvent.php | 6 +\n app/Listeners/Activities/Coaching/Intercom/CreateCommentedEvent.php | 6 +\n app/Listeners/Activities/Coaching/Intercom/CreateManagerCoachedEvent.php | 6 +\n app/Listeners/Activities/Coaching/Intercom/CreatePlayedEvent.php | 6 +\n app/Listeners/Activities/Conferences/Intercom/CreateHeldActivityEvent.php | 6 +\n app/Listeners/Activities/Crm/Summary/AbstractSummaryListener.php | 5 +-\n app/Listeners/Activities/Sms/Intercom/CreateSmsReceivedEvent.php | 6 +\n app/Models/Activity/TopicTrigger.php | 2 +-\n app/Models/Activity/Transcription.php | 29 +--\n app/Providers/EventServiceProvider.php | 1 -\n app/Providers/JiminnyServiceProvider.php | 23 ---\n app/Repositories/QuestionRepository.php | 32 +--\n app/Services/Activity/HubSpot/AuthenticatedHttpClient.php | 75 +------\n app/Services/Activity/HubSpot/RedirectUrlResolver.php | 91 +++++++++\n app/Services/Activity/HubSpot/Service.php | 17 +-\n app/Services/Activity/HubSpot/ZoomRecordingDetector.php | 27 +++\n app/Services/Activity/MeetingBotService.php | 5 +-\n app/Services/Calendar/GoogleCalendarService.php | 3 +\n app/Services/Calendar/OfficeCalendarService.php | 25 +--\n app/Services/Crm/Hubspot/SyncRelatedActivityManager.php | 9 +-\n composer.json | 1 -\n composer.lock | 51 +----\n front-end/src/components/DealInsights/Modals/AskAnythingModal.vue | 1 -\n front-end/src/components/Settings/Kiosk/ActivitiesSearch/ActivityTranscription.vue | 12 +-\n front-end/src/components/shared/Filters/InputText.vue | 2 +\n tests/Unit/Component/Activity/ActivityProcessingStateManagerTest.php | 17 +-\n tests/Unit/Component/ActivityAnalytics/Service/ActivityAnalyticsServiceTest.php | 137 ++++++++++++-\n tests/Unit/Component/ActivityAnalytics/Service/LocalTriggerMatcherServiceTest.php | 135 +++++++------\n tests/Unit/Component/ActivityAnalytics/Service/QuestionServiceTest.php | 610 ++++++++++++---------------------------------------------\n tests/Unit/Component/ActivityAnalytics/VO/TopicTriggerMatchTest.php | 15 ++\n tests/Unit/Component/AiAutomation/Actions/UpdateCrmFieldActionTest.php | 66 +++++++\n tests/Unit/Component/AiAutomation/Services/CrmFieldContentTrimmerTest.php | 282 ++++++++++++++++++++++++++\n tests/Unit/Component/ES/UpdateProcessManagerTest.php | 2 +-\n tests/Unit/Component/Kiosk/Services/GetTranscriptionComparisonServiceTest.php | 79 +++-----\n tests/Unit/Component/LanguageDetection/Services/DetectLanguageServiceTest.php | 132 +++++++++----\n tests/Unit/Component/MediaPipeline/Handlers/LanguageDetectionPipeHandlerTest.php | 97 +++++++++\n tests/Unit/Component/MediaPipeline/Handlers/TranscriptionParagraphsPipeHandlerTest.php | 100 ++++++++++\n tests/Unit/Component/MediaPipeline/Services/GetMediaPipelineStatesServiceTest.php | 6 +\n tests/Unit/Component/ParagraphBreaker/Services/ParagraphBuilderTest.php | 53 ++---\n tests/Unit/Component/ParagraphBreaker/Services/TranscriptionParagraphServiceTest.php | 547 +++++++++++++++++++++++++++++++++++++++++++++++++++\n tests/Unit/Component/ParagraphBreaker/Services/UtteranceBuilderTest.php | 73 ++++---\n tests/Unit/Component/Transcription/Formatter/TranscriptionFormatterTest.php | 8 +-\n tests/Unit/Component/Transcription/Job/TranscriptSegmentCompileJobTest.php | 101 ----------\n tests/Unit/Component/Transcription/Job/TranscriptSegmentsCompileJobTest.php | 220 +++++++++++++++++++++\n tests/Unit/Component/Transcription/Service/SearchServiceTest.php | 193 ++++++++++++++++++\n tests/Unit/Component/Transcription/Service/TranscriptSegmentsServiceTest.php | 465 -------------------------------------------\n tests/Unit/Component/Transcription/Service/TranscriptionServiceTest.php | 61 ++----\n tests/Unit/Component/Transcription/Service/TranslationServiceTest.php | 31 +--\n tests/Unit/Component/Transcription/TranscriptionProcessor/AssemblyAI/AssemblyAITest.php | 355 ++-------------------------------\n .../TranscriptionProcessor/AssemblyAI/Services/SubmitAudioFileForLanguageDetectionServiceTest.php | 220 +++++++++++++++++++++\n tests/Unit/Component/Transcription/VO/TranscriptSegmentTransformerTest.php | 68 -------\n tests/Unit/Jobs/Mailbox/SyncInboxTest.php | 2 +-\n tests/Unit/Services/Activity/HubSpot/AuthenticatedHttpClientTest.php | 207 ++------------------\n tests/Unit/Services/Activity/HubSpot/RedirectUrlResolverTest.php | 184 +++++++++++++++++\n tests/Unit/Services/Activity/HubSpot/ServiceTest.php | 205 ++++++++++++++++++-\n tests/Unit/Services/Activity/MeetingBotServiceOnSharingDataTest.php | 196 ++++++++++++++++++\n tests/Unit/Services/Crm/Hubspot/SyncRelatedActivityManagerTest.php | 105 ++++++++++\n 96 files changed, 4708 insertions(+), 3522 deletions(-)\n create mode 100644 .github/workflows/secfix.yml\n delete mode 100644 app/Component/ActivityAnalytics/Service/SentenceBreaker.php\n create mode 100644 app/Component/AiAutomation/Services/CrmFieldContentTrimmer.php\n create mode 100644 app/Component/MediaPipeline/Handlers/LanguageDetectionPipeHandler.php\n create mode 100644 app/Component/MediaPipeline/Handlers/TranscriptionParagraphsPipeHandler.php\n create mode 100644 app/Component/ParagraphBreaker/DTOs/TranscriptParagraph.php\n create mode 100644 app/Component/ParagraphBreaker/Services/TranscriptionParagraphsService.php\n delete mode 100644 app/Component/Transcription/Listener/TranscriptSegmentsCompileListener.php\n delete mode 100644 app/Component/Transcription/Service/TranscriptSegmentsService.php\n create mode 100644 app/Component/Transcription/TranscriptionProcessor/AssemblyAI/Services/FetchTranscriptService.php\n create mode 100644 app/Component/Transcription/TranscriptionProcessor/AssemblyAI/Services/SubmitAudioFileForLanguageDetectionService.php\n delete mode 100644 app/Component/Transcription/VO/TranscriptSegmentTransformer.php\n create mode 100644 app/Services/Activity/HubSpot/RedirectUrlResolver.php\n create mode 100644 app/Services/Activity/HubSpot/ZoomRecordingDetector.php\n create mode 100644 tests/Unit/Component/AiAutomation/Services/CrmFieldContentTrimmerTest.php\n create mode 100644 tests/Unit/Component/MediaPipeline/Handlers/LanguageDetectionPipeHandlerTest.php\n create mode 100644 tests/Unit/Component/MediaPipeline/Handlers/TranscriptionParagraphsPipeHandlerTest.php\n create mode 100644 tests/Unit/Component/ParagraphBreaker/Services/TranscriptionParagraphServiceTest.php\n delete mode 100644 tests/Unit/Component/Transcription/Job/TranscriptSegmentCompileJobTest.php\n create mode 100644 tests/Unit/Component/Transcription/Job/TranscriptSegmentsCompileJobTest.php\n delete mode 100644 tests/Unit/Component/Transcription/Service/TranscriptSegmentsServiceTest.php\n create mode 100644 tests/Unit/Component/Transcription/TranscriptionProcessor/AssemblyAI/Services/SubmitAudioFileForLanguageDetectionServiceTest.php\n delete mode 100644 tests/Unit/Component/Transcription/VO/TranscriptSegmentTransformerTest.php\n create mode 100644 tests/Unit/Services/Activity/HubSpot/RedirectUrlResolverTest.php\n create mode 100644 tests/Unit/Services/Activity/MeetingBotServiceOnSharingDataTest.php\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ git status\nOn branch JY-18909-automated-reports-ask-jiminny\nYour branch is ahead of 'origin/JY-18909-automated-reports-ask-jiminny' by 2 commits.\n (use \"git push\" to publish your local commits)\n\nChanges not staged for commit:\n (use \"git add <file>...\" to update what will be committed)\n (use \"git restore <file>...\" to discard changes in working directory)\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: .env.local\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: app/Console/Commands/JiminnyDebugCommand.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: app/Http/Controllers/API/ActivityController.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: app/Http/Controllers/Webhook/ReportController.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: app/Jobs/Team/SyncToIntercom.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: app/Services/PlaybackService.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: config/logging.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: routes/web.php\n\nUntracked files:\n (use \"git add <file>...\" to include in what will be committed)\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\t.env.nikilocal\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\t.env.other\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tWEBHOOK_FILTERING_IMPLEMENTATION.md\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tapp/Console/Commands/Crm/Hubspot/SimulateWebhooksCommand.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tapp/Console/Commands/Reports/CreateMockAskJiminnyReportResultCommand.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tids.txt\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\traw_sql_query.sql\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\ttests/Unit/Policies/CanAccessAiReportsTest.php\n\nno changes added to commit (use \"git add\" and/or \"git commit -a\")\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ git push\nEnumerating objects: 63, done.\nCounting objects: 100% (55/55), done.\nDelta compression using up to 8 threads\nCompressing objects: 100% (28/28), done.\nWriting objects: 100% (29/29), 3.77 KiB | 3.77 MiB/s, done.\nTotal 29 (delta 22), reused 0 (delta 0), pack-reused 0\nremote: Resolving deltas: 100% (22/22), completed with 20 local objects.\nremote: \nremote: GitHub found 24 vulnerabilities on jiminny/app's default branch (2 critical, 12 high, 9 moderate, 1 low). To find out more, visit:\nremote: https://github.com/jiminny/app/security/dependabot\nremote: \nTo github.com:jiminny/app.git\n e5eb1c8958..42e3e90859 JY-18909-automated-reports-ask-jiminny -> JY-18909-automated-reports-ask-jiminny\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix\ndocker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff \nPHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.\nPHP runtime: 8.3.30\nRunning analysis on 7 cores with 10 files per process.\nParallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!\nLoaded config default from \".php-cs-fixer.dist.php\".\n 5589/5589 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%\n\n\nFixed 0 of 5589 files in 39.991 seconds, 67.00 MB memory used\n\nWhat's next:\n Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1\n Learn more at https://docs.docker.com/go/debug-cli/\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix\ndocker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff \nPHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.\nPHP runtime: 8.3.30\nRunning analysis on 7 cores with 10 files per process.\nParallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!\nLoaded config default from \".php-cs-fixer.dist.php\".\n 5589/5589 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%\n\n\nFixed 0 of 5589 files in 39.687 seconds, 67.00 MB memory used\n\nWhat's next:\n Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1\n Learn more at https://docs.docker.com/go/debug-cli/\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix\ndocker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff \nPHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.\nPHP runtime: 8.3.30\nRunning analysis on 7 cores with 10 files per process.\nParallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!\nLoaded config default from \".php-cs-fixer.dist.php\".\n 5589/5589 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%\n\n\nFixed 0 of 5589 files in 52.171 seconds, 67.00 MB memory used\n\nWhat's next:\n Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1\n Learn more at https://docs.docker.com/go/debug-cli/\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix\ndocker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff \nPHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.\nPHP runtime: 8.3.30\nRunning analysis on 7 cores with 10 files per process.\nParallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!\nLoaded config default from \".php-cs-fixer.dist.php\".\n 5589/5589 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%\n\n\nFixed 0 of 5589 files in 49.458 seconds, 67.00 MB memory used\n\nWhat's next:\n Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1\n Learn more at https://docs.docker.com/go/debug-cli/\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $","is_focused":true},{"role":"AXRadioButton","text":"DOCKER","depth":2,"bounds":{"left":0.23320313,"top":1.0,"width":0.06914063,"height":-0.03680551},"role_description":"radio button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Close Tab","depth":3,"bounds":{"left":0.23554687,"top":1.0,"width":0.00625,"height":-0.039583325},"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXRadioButton","text":"DEV (docker)","depth":2,"bounds":{"left":0.30234376,"top":1.0,"width":0.06914063,"height":-0.03680551},"role_description":"radio button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Close Tab","depth":3,"bounds":{"left":0.3046875,"top":1.0,"width":0.00625,"height":-0.039583325},"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXRadioButton","text":"APP (-zsh)","depth":2,"bounds":{"left":0.37148437,"top":1.0,"width":0.06914063,"height":-0.03680551},"role_description":"radio button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Close Tab","depth":3,"bounds":{"left":0.3738281,"top":1.0,"width":0.00625,"height":-0.039583325},"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXRadioButton","text":"ec2-user@ip-10-30-93-249:~ (nc)","depth":2,"bounds":{"left":0.440625,"top":1.0,"width":0.06914063,"height":-0.03680551},"role_description":"radio button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Close Tab","depth":3,"bounds":{"left":0.44296876,"top":1.0,"width":0.00625,"height":-0.039583325},"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXRadioButton","text":"-zsh","depth":2,"bounds":{"left":0.5097656,"top":1.0,"width":0.06914063,"height":-0.03680551},"role_description":"radio button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Close Tab","depth":3,"bounds":{"left":0.5121094,"top":1.0,"width":0.00625,"height":-0.039583325},"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXRadioButton","text":"-zsh","depth":2,"bounds":{"left":0.57890624,"top":1.0,"width":0.06914063,"height":-0.03680551},"role_description":"radio button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Close Tab","depth":3,"bounds":{"left":0.58125,"top":1.0,"width":0.00625,"height":-0.039583325},"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXRadioButton","text":"-zsh","depth":2,"bounds":{"left":0.64804685,"top":1.0,"width":0.06914063,"height":-0.03680551},"role_description":"radio button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Close Tab","depth":3,"bounds":{"left":0.6503906,"top":1.0,"width":0.00625,"height":-0.039583325},"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXRadioButton","text":"✳ Unable to access screenpipe activity data (claude)","depth":2,"bounds":{"left":0.7171875,"top":1.0,"width":0.06914063,"height":-0.03680551},"role_description":"radio button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Close Tab","depth":3,"bounds":{"left":0.71953124,"top":1.0,"width":0.00625,"height":-0.039583325},"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"⌥⌘1","depth":1,"bounds":{"left":0.7703125,"top":1.0,"width":0.021875,"height":-0.02013886},"automation_id":"_NS:8","role_description":"text"},{"role":"AXStaticText","text":"APP (-zsh)","depth":1,"bounds":{"left":0.5,"top":1.0,"width":0.02890625,"height":-0.020833373},"role_description":"text"}]...
|
-7207020035503630112
|
7426414321496521432
|
visual_change
|
accessibility
|
NULL
|
Last login: Sat Apr 11 12:38:35 on ttys007
Poetry Last login: Sat Apr 11 12:38:35 on ttys007
Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parents
Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parents
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix
docker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff
PHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.
PHP runtime: 8.3.30
Running analysis on 7 cores with 10 files per process.
Parallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!
Loaded config default from ".php-cs-fixer.dist.php".
5579/5579 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%
1) app/Services/Kiosk/AutomatedReports/AskJiminnyReportActivityService.php (trailing_comma_in_multiline)
---------- begin diff ----------
--- /home/jiminny/app/Services/Kiosk/AutomatedReports/AskJiminnyReportActivityService.php
+++ /home/jiminny/app/Services/Kiosk/AutomatedReports/AskJiminnyReportActivityService.php
@@ -51,7 +51,7 @@
array_merge($requestParams, [
'limit' => self::DEFAULT_TOP_ACTIVITIES_COUNT,
'page' => 1,
- 'sequence_number' => 1
+ 'sequence_number' => 1,
]),
$user->getTimezone()
);
----------- end diff -----------
Fixed 1 of 5579 files in 108.308 seconds, 67.00 MB memory used
What's next:
Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1
Learn more at [URL_WITH_CREDENTIALS] ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ git status
On branch JY-18909-automated-reports-ask-jiminny
Your branch is ahead of 'origin/JY-18909-automated-reports-ask-jiminny' by 2 commits.
(use "git push" to publish your local commits)
Changes not staged for commit:
(use "git add <file>..." to update what will be committed)
(use "git restore <file>..." to discard changes in working directory)
modified: .env.local
modified: app/Console/Commands/JiminnyDebugCommand.php
modified: app/Http/Controllers/API/ActivityController.php
modified: app/Http/Controllers/Webhook/ReportController.php
modified: app/Jobs/Team/SyncToIntercom.php
modified: app/Services/PlaybackService.php
modified: config/logging.php
modified: routes/web.php
Untracked files:
(use "git add <file>..." to include in what will be committed)
.env.nikilocal
.env.other
WEBHOOK_FILTERING_IMPLEMENTATION.md
app/Console/Commands/Crm/Hubspot/SimulateWebhooksCommand.php
app/Console/Commands/Reports/CreateMockAskJiminnyReportResultCommand.php
ids.txt
raw_sql_query.sql
tests/Unit/Policies/CanAccessAiReportsTest.php
no changes added to commit (use "git add" and/or "git commit -a")
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ git push
Enumerating objects: 63, done.
Counting objects: 100% (55/55), done.
Delta compression using up to 8 threads
Compressing objects: 100% (28/28), done.
Writing objects: 100% (29/29), 3.77 KiB | 3.77 MiB/s, done.
Total 29 (delta 22), reused 0 (delta 0), pack-reused 0
remote: Resolving deltas: 100% (22/22), completed with 20 local objects.
remote:
remote: GitHub found 24 vulnerabilities on jiminny/app's default branch (2 critical, 12 high, 9 moderate, 1 low). To find out more, visit:
remote: [URL_WITH_CREDENTIALS] ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix
docker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff
PHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.
PHP runtime: 8.3.30
Running analysis on 7 cores with 10 files per process.
Parallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!
Loaded config default from ".php-cs-fixer.dist.php".
5589/5589 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%
Fixed 0 of 5589 files in 39.991 seconds, 67.00 MB memory used
What's next:
Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1
Learn more at [URL_WITH_CREDENTIALS] ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix
docker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff
PHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.
PHP runtime: 8.3.30
Running analysis on 7 cores with 10 files per process.
Parallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!
Loaded config default from ".php-cs-fixer.dist.php".
5589/5589 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%
Fixed 0 of 5589 files in 52.171 seconds, 67.00 MB memory used
What's next:
Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1
Learn more at [URL_WITH_CREDENTIALS] ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $
DOCKER
Close Tab
DEV (docker)
Close Tab
APP (-zsh)
Close Tab
ec2-user@ip-10-30-93-249:~ (nc)
Close Tab
-zsh
Close Tab
-zsh
Close Tab
-zsh
Close Tab
✳ Unable to access screenpipe activity data (claude)
Close Tab
⌥⌘1
APP (-zsh)...
|
NULL
|
|
13047
|
283
|
31
|
2026-04-14T11:58:42.694445+00:00
|
/Users/lukas/.screenpipe/data/data/2026-04-14/1776 /Users/lukas/.screenpipe/data/data/2026-04-14/1776167922694_m2.jpg...
|
iTerm2
|
APP (-zsh)
|
True
|
NULL
|
monitor_2
|
NULL
|
NULL
|
NULL
|
NULL
|
Last login: Sat Apr 11 12:38:35 on ttys007
Poetry Last login: Sat Apr 11 12:38:35 on ttys007
Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parents
Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parents
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix
docker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff
PHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.
PHP runtime: 8.3.30
Running analysis on 7 cores with 10 files per process.
Parallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!
Loaded config default from ".php-cs-fixer.dist.php".
5579/5579 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%
1) app/Services/Kiosk/AutomatedReports/AskJiminnyReportActivityService.php (trailing_comma_in_multiline)
---------- begin diff ----------
--- /home/jiminny/app/Services/Kiosk/AutomatedReports/AskJiminnyReportActivityService.php
+++ /home/jiminny/app/Services/Kiosk/AutomatedReports/AskJiminnyReportActivityService.php
@@ -51,7 +51,7 @@
array_merge($requestParams, [
'limit' => self::DEFAULT_TOP_ACTIVITIES_COUNT,
'page' => 1,
- 'sequence_number' => 1
+ 'sequence_number' => 1,
]),
$user->getTimezone()
);
----------- end diff -----------
Fixed 1 of 5579 files in 108.308 seconds, 67.00 MB memory used
What's next:
Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1
Learn more at [URL_WITH_CREDENTIALS] ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ git status
On branch JY-18909-automated-reports-ask-jiminny
Your branch is ahead of 'origin/JY-18909-automated-reports-ask-jiminny' by 2 commits.
(use "git push" to publish your local commits)
Changes not staged for commit:
(use "git add <file>..." to update what will be committed)
(use "git restore <file>..." to discard changes in working directory)
modified: .env.local
modified: app/Console/Commands/JiminnyDebugCommand.php
modified: app/Http/Controllers/API/ActivityController.php
modified: app/Http/Controllers/Webhook/ReportController.php
modified: app/Jobs/Team/SyncToIntercom.php
modified: app/Services/PlaybackService.php
modified: config/logging.php
modified: routes/web.php
Untracked files:
(use "git add <file>..." to include in what will be committed)
.env.nikilocal
.env.other
WEBHOOK_FILTERING_IMPLEMENTATION.md
app/Console/Commands/Crm/Hubspot/SimulateWebhooksCommand.php
app/Console/Commands/Reports/CreateMockAskJiminnyReportResultCommand.php
ids.txt
raw_sql_query.sql
tests/Unit/Policies/CanAccessAiReportsTest.php
no changes added to commit (use "git add" and/or "git commit -a")
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ git push
Enumerating objects: 63, done.
Counting objects: 100% (55/55), done.
Delta compression using up to 8 threads
Compressing objects: 100% (28/28), done.
Writing objects: 100% (29/29), 3.77 KiB | 3.77 MiB/s, done.
Total 29 (delta 22), reused 0 (delta 0), pack-reused 0
remote: Resolving deltas: 100% (22/22), completed with 20 local objects.
remote:
remote: GitHub found 24 vulnerabilities on jiminny/app's default branch (2 critical, 12 high, 9 moderate, 1 low). To find out more, visit:
remote: [URL_WITH_CREDENTIALS] ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix
docker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff
PHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.
PHP runtime: 8.3.30
Running analysis on 7 cores with 10 files per process.
Parallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!
Loaded config default from ".php-cs-fixer.dist.php".
5589/5589 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%
Fixed 0 of 5589 files in 39.991 seconds, 67.00 MB memory used
What's next:
Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1
Learn more at [URL_WITH_CREDENTIALS] ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix
docker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff
PHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.
PHP runtime: 8.3.30
Running analysis on 7 cores with 10 files per process.
Parallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!
Loaded config default from ".php-cs-fixer.dist.php".
5589/5589 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%
Fixed 0 of 5589 files in 52.171 seconds, 67.00 MB memory used
What's next:
Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1
Learn more at https://docs.docker.com/go/debug-cli/
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csf
DOCKER
Close Tab
DEV (docker)
Close Tab
APP (-zsh)
Close Tab
ec2-user@ip-10-30-93-249:~ (nc)
Close Tab
-zsh
Close Tab
-zsh
Close Tab
-zsh
Close Tab
✳ Unable to access screenpipe activity data (claude)
Close Tab
⌥⌘1
APP (-zsh)...
|
[{"role":"AXTextArea","text [{"role":"AXTextArea","text":"Last login: Sat Apr 11 12:38:35 on ttys007\n\nPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parents\n\nPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parents\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix\ndocker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff \nPHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.\nPHP runtime: 8.3.30\nRunning analysis on 7 cores with 10 files per process.\nParallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!\nLoaded config default from \".php-cs-fixer.dist.php\".\n 5579/5579 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%\n\n 1) app/Services/Kiosk/AutomatedReports/AskJiminnyReportActivityService.php (trailing_comma_in_multiline)\n ---------- begin diff ----------\n--- /home/jiminny/app/Services/Kiosk/AutomatedReports/AskJiminnyReportActivityService.php\n+++ /home/jiminny/app/Services/Kiosk/AutomatedReports/AskJiminnyReportActivityService.php\n@@ -51,7 +51,7 @@\n array_merge($requestParams, [\n 'limit' => self::DEFAULT_TOP_ACTIVITIES_COUNT,\n 'page' => 1,\n- 'sequence_number' => 1\n+ 'sequence_number' => 1,\n ]),\n $user->getTimezone()\n );\n\n ----------- end diff -----------\n\n\nFixed 1 of 5579 files in 108.308 seconds, 67.00 MB memory used\n\nWhat's next:\n Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1\n Learn more at https://docs.docker.com/go/debug-cli/\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ git pull\nremote: Enumerating objects: 185, done.\nremote: Counting objects: 100% (185/185), done.\nremote: Compressing objects: 100% (53/53), done.\nremote: Total 185 (delta 138), reused 176 (delta 130), pack-reused 0 (from 0)\nReceiving objects: 100% (185/185), 168.96 KiB | 1.05 MiB/s, done.\nResolving deltas: 100% (138/138), completed with 74 local objects.\nFrom github.com:jiminny/app\n * [new branch] JY-20255-quick-search-show-duration-and-call-conference-type -> origin/JY-20255-quick-search-show-duration-and-call-conference-type\n * [new branch] JY-20297-reposition-the-old-acs-feature -> origin/JY-20297-reposition-the-old-acs-feature\n * [new branch] JY-20541-stale-records-pr-1 -> origin/JY-20541-stale-records-pr-1\nMerge made by the 'ort' strategy.\n .env.production | 2 +-\n .env.production-eu | 2 +-\n .github/workflows/secfix.yml | 10 +\n app/Component/Activity/ActivityProcessingStateManager.php | 4 +\n app/Component/ActivityAnalytics/Service/ActivityAnalyticsService.php | 68 +++----\n app/Component/ActivityAnalytics/Service/LocalTriggerMatcherService.php | 40 ++--\n app/Component/ActivityAnalytics/Service/QuestionService.php | 137 +++++++------\n app/Component/ActivityAnalytics/Service/SentenceBreaker.php | 110 -----------\n app/Component/ActivityAnalytics/VO/TopicTriggerMatch.php | 4 +-\n app/Component/AiAutomation/Actions/UpdateCrmFieldAction.php | 14 +-\n app/Component/AiAutomation/Services/CrmFieldContentTrimmer.php | 92 +++++++++\n app/Component/Kiosk/Services/GetTranscriptionComparisonService.php | 61 +-----\n app/Component/LanguageDetection/Services/DetectLanguageService.php | 74 ++++++-\n app/Component/MediaPipeline/Handlers/LanguageDetectionPipeHandler.php | 37 ++++\n app/Component/MediaPipeline/Handlers/TranscriptionParagraphsPipeHandler.php | 45 +++++\n app/Component/MediaPipeline/MediaPipelineOrchestrator.php | 2 +\n app/Component/MediaPipeline/MediaPipelineServiceProvider.php | 8 +\n app/Component/ParagraphBreaker/DTOs/TranscriptParagraph.php | 46 +++++\n app/Component/ParagraphBreaker/Services/ParagraphBuilder.php | 29 ++-\n app/Component/ParagraphBreaker/Services/TranscriptionParagraphsService.php | 179 +++++++++++++++++\n app/Component/ParagraphBreaker/Services/UtteranceBuilder.php | 192 ++++++++----------\n app/Component/PlaybackPage/Snapshots/Listeners/GenerateSnapshotsListener.php | 6 +\n app/Component/Transcription/Formatter/TranscriptionFormatter.php | 11 +-\n app/Component/Transcription/Job/StartTranscriptDiarizationJob.php | 2 +-\n app/Component/Transcription/Job/TranscriptSegmentsCompileJob.php | 43 +++-\n app/Component/Transcription/Listener/TranscriptSegmentsCompileListener.php | 16 --\n app/Component/Transcription/Service/SearchService.php | 179 ++++++++++-------\n app/Component/Transcription/Service/TranscriptSegmentsService.php | 396 -------------------------------------\n app/Component/Transcription/Service/TranscriptionService.php | 142 ++------------\n app/Component/Transcription/Service/TranslationService.php | 59 ++----\n app/Component/Transcription/TranscriptionProcessor/AssemblyAI/AssemblyAI.php | 172 +---------------\n app/Component/Transcription/TranscriptionProcessor/AssemblyAI/Services/FetchTranscriptService.php | 48 +++++\n .../Transcription/TranscriptionProcessor/AssemblyAI/Services/SubmitAudioFileForLanguageDetectionService.php | 111 +++++++++++\n app/Component/Transcription/VO/TranscriptSegmentTransformer.php | 37 ----\n app/Console/Commands/Crm/Hubspot/RestoreDealAssociationsCommand.php | 344 ++++++++++++++++++++++++--------\n app/Console/Commands/EngagementStats/JiminnyEngagementStatsExplainCommand.php | 76 ++++---\n app/Console/Commands/Mailboxes/InboxSync.php | 9 +-\n app/Http/Controllers/Auth/SocialController.php | 2 +-\n app/Jobs/Mailbox/SyncInbox.php | 2 +-\n app/Listeners/Activities/Coaching/Intercom/CreateActivitySummarizedEvent.php | 6 +\n app/Listeners/Activities/Coaching/Intercom/CreateCommentedEvent.php | 6 +\n app/Listeners/Activities/Coaching/Intercom/CreateManagerCoachedEvent.php | 6 +\n app/Listeners/Activities/Coaching/Intercom/CreatePlayedEvent.php | 6 +\n app/Listeners/Activities/Conferences/Intercom/CreateHeldActivityEvent.php | 6 +\n app/Listeners/Activities/Crm/Summary/AbstractSummaryListener.php | 5 +-\n app/Listeners/Activities/Sms/Intercom/CreateSmsReceivedEvent.php | 6 +\n app/Models/Activity/TopicTrigger.php | 2 +-\n app/Models/Activity/Transcription.php | 29 +--\n app/Providers/EventServiceProvider.php | 1 -\n app/Providers/JiminnyServiceProvider.php | 23 ---\n app/Repositories/QuestionRepository.php | 32 +--\n app/Services/Activity/HubSpot/AuthenticatedHttpClient.php | 75 +------\n app/Services/Activity/HubSpot/RedirectUrlResolver.php | 91 +++++++++\n app/Services/Activity/HubSpot/Service.php | 17 +-\n app/Services/Activity/HubSpot/ZoomRecordingDetector.php | 27 +++\n app/Services/Activity/MeetingBotService.php | 5 +-\n app/Services/Calendar/GoogleCalendarService.php | 3 +\n app/Services/Calendar/OfficeCalendarService.php | 25 +--\n app/Services/Crm/Hubspot/SyncRelatedActivityManager.php | 9 +-\n composer.json | 1 -\n composer.lock | 51 +----\n front-end/src/components/DealInsights/Modals/AskAnythingModal.vue | 1 -\n front-end/src/components/Settings/Kiosk/ActivitiesSearch/ActivityTranscription.vue | 12 +-\n front-end/src/components/shared/Filters/InputText.vue | 2 +\n tests/Unit/Component/Activity/ActivityProcessingStateManagerTest.php | 17 +-\n tests/Unit/Component/ActivityAnalytics/Service/ActivityAnalyticsServiceTest.php | 137 ++++++++++++-\n tests/Unit/Component/ActivityAnalytics/Service/LocalTriggerMatcherServiceTest.php | 135 +++++++------\n tests/Unit/Component/ActivityAnalytics/Service/QuestionServiceTest.php | 610 ++++++++++++---------------------------------------------\n tests/Unit/Component/ActivityAnalytics/VO/TopicTriggerMatchTest.php | 15 ++\n tests/Unit/Component/AiAutomation/Actions/UpdateCrmFieldActionTest.php | 66 +++++++\n tests/Unit/Component/AiAutomation/Services/CrmFieldContentTrimmerTest.php | 282 ++++++++++++++++++++++++++\n tests/Unit/Component/ES/UpdateProcessManagerTest.php | 2 +-\n tests/Unit/Component/Kiosk/Services/GetTranscriptionComparisonServiceTest.php | 79 +++-----\n tests/Unit/Component/LanguageDetection/Services/DetectLanguageServiceTest.php | 132 +++++++++----\n tests/Unit/Component/MediaPipeline/Handlers/LanguageDetectionPipeHandlerTest.php | 97 +++++++++\n tests/Unit/Component/MediaPipeline/Handlers/TranscriptionParagraphsPipeHandlerTest.php | 100 ++++++++++\n tests/Unit/Component/MediaPipeline/Services/GetMediaPipelineStatesServiceTest.php | 6 +\n tests/Unit/Component/ParagraphBreaker/Services/ParagraphBuilderTest.php | 53 ++---\n tests/Unit/Component/ParagraphBreaker/Services/TranscriptionParagraphServiceTest.php | 547 +++++++++++++++++++++++++++++++++++++++++++++++++++\n tests/Unit/Component/ParagraphBreaker/Services/UtteranceBuilderTest.php | 73 ++++---\n tests/Unit/Component/Transcription/Formatter/TranscriptionFormatterTest.php | 8 +-\n tests/Unit/Component/Transcription/Job/TranscriptSegmentCompileJobTest.php | 101 ----------\n tests/Unit/Component/Transcription/Job/TranscriptSegmentsCompileJobTest.php | 220 +++++++++++++++++++++\n tests/Unit/Component/Transcription/Service/SearchServiceTest.php | 193 ++++++++++++++++++\n tests/Unit/Component/Transcription/Service/TranscriptSegmentsServiceTest.php | 465 -------------------------------------------\n tests/Unit/Component/Transcription/Service/TranscriptionServiceTest.php | 61 ++----\n tests/Unit/Component/Transcription/Service/TranslationServiceTest.php | 31 +--\n tests/Unit/Component/Transcription/TranscriptionProcessor/AssemblyAI/AssemblyAITest.php | 355 ++-------------------------------\n .../TranscriptionProcessor/AssemblyAI/Services/SubmitAudioFileForLanguageDetectionServiceTest.php | 220 +++++++++++++++++++++\n tests/Unit/Component/Transcription/VO/TranscriptSegmentTransformerTest.php | 68 -------\n tests/Unit/Jobs/Mailbox/SyncInboxTest.php | 2 +-\n tests/Unit/Services/Activity/HubSpot/AuthenticatedHttpClientTest.php | 207 ++------------------\n tests/Unit/Services/Activity/HubSpot/RedirectUrlResolverTest.php | 184 +++++++++++++++++\n tests/Unit/Services/Activity/HubSpot/ServiceTest.php | 205 ++++++++++++++++++-\n tests/Unit/Services/Activity/MeetingBotServiceOnSharingDataTest.php | 196 ++++++++++++++++++\n tests/Unit/Services/Crm/Hubspot/SyncRelatedActivityManagerTest.php | 105 ++++++++++\n 96 files changed, 4708 insertions(+), 3522 deletions(-)\n create mode 100644 .github/workflows/secfix.yml\n delete mode 100644 app/Component/ActivityAnalytics/Service/SentenceBreaker.php\n create mode 100644 app/Component/AiAutomation/Services/CrmFieldContentTrimmer.php\n create mode 100644 app/Component/MediaPipeline/Handlers/LanguageDetectionPipeHandler.php\n create mode 100644 app/Component/MediaPipeline/Handlers/TranscriptionParagraphsPipeHandler.php\n create mode 100644 app/Component/ParagraphBreaker/DTOs/TranscriptParagraph.php\n create mode 100644 app/Component/ParagraphBreaker/Services/TranscriptionParagraphsService.php\n delete mode 100644 app/Component/Transcription/Listener/TranscriptSegmentsCompileListener.php\n delete mode 100644 app/Component/Transcription/Service/TranscriptSegmentsService.php\n create mode 100644 app/Component/Transcription/TranscriptionProcessor/AssemblyAI/Services/FetchTranscriptService.php\n create mode 100644 app/Component/Transcription/TranscriptionProcessor/AssemblyAI/Services/SubmitAudioFileForLanguageDetectionService.php\n delete mode 100644 app/Component/Transcription/VO/TranscriptSegmentTransformer.php\n create mode 100644 app/Services/Activity/HubSpot/RedirectUrlResolver.php\n create mode 100644 app/Services/Activity/HubSpot/ZoomRecordingDetector.php\n create mode 100644 tests/Unit/Component/AiAutomation/Services/CrmFieldContentTrimmerTest.php\n create mode 100644 tests/Unit/Component/MediaPipeline/Handlers/LanguageDetectionPipeHandlerTest.php\n create mode 100644 tests/Unit/Component/MediaPipeline/Handlers/TranscriptionParagraphsPipeHandlerTest.php\n create mode 100644 tests/Unit/Component/ParagraphBreaker/Services/TranscriptionParagraphServiceTest.php\n delete mode 100644 tests/Unit/Component/Transcription/Job/TranscriptSegmentCompileJobTest.php\n create mode 100644 tests/Unit/Component/Transcription/Job/TranscriptSegmentsCompileJobTest.php\n delete mode 100644 tests/Unit/Component/Transcription/Service/TranscriptSegmentsServiceTest.php\n create mode 100644 tests/Unit/Component/Transcription/TranscriptionProcessor/AssemblyAI/Services/SubmitAudioFileForLanguageDetectionServiceTest.php\n delete mode 100644 tests/Unit/Component/Transcription/VO/TranscriptSegmentTransformerTest.php\n create mode 100644 tests/Unit/Services/Activity/HubSpot/RedirectUrlResolverTest.php\n create mode 100644 tests/Unit/Services/Activity/MeetingBotServiceOnSharingDataTest.php\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ git status\nOn branch JY-18909-automated-reports-ask-jiminny\nYour branch is ahead of 'origin/JY-18909-automated-reports-ask-jiminny' by 2 commits.\n (use \"git push\" to publish your local commits)\n\nChanges not staged for commit:\n (use \"git add <file>...\" to update what will be committed)\n (use \"git restore <file>...\" to discard changes in working directory)\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: .env.local\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: app/Console/Commands/JiminnyDebugCommand.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: app/Http/Controllers/API/ActivityController.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: app/Http/Controllers/Webhook/ReportController.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: app/Jobs/Team/SyncToIntercom.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: app/Services/PlaybackService.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: config/logging.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: routes/web.php\n\nUntracked files:\n (use \"git add <file>...\" to include in what will be committed)\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\t.env.nikilocal\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\t.env.other\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tWEBHOOK_FILTERING_IMPLEMENTATION.md\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tapp/Console/Commands/Crm/Hubspot/SimulateWebhooksCommand.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tapp/Console/Commands/Reports/CreateMockAskJiminnyReportResultCommand.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tids.txt\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\traw_sql_query.sql\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\ttests/Unit/Policies/CanAccessAiReportsTest.php\n\nno changes added to commit (use \"git add\" and/or \"git commit -a\")\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ git push\nEnumerating objects: 63, done.\nCounting objects: 100% (55/55), done.\nDelta compression using up to 8 threads\nCompressing objects: 100% (28/28), done.\nWriting objects: 100% (29/29), 3.77 KiB | 3.77 MiB/s, done.\nTotal 29 (delta 22), reused 0 (delta 0), pack-reused 0\nremote: Resolving deltas: 100% (22/22), completed with 20 local objects.\nremote: \nremote: GitHub found 24 vulnerabilities on jiminny/app's default branch (2 critical, 12 high, 9 moderate, 1 low). To find out more, visit:\nremote: https://github.com/jiminny/app/security/dependabot\nremote: \nTo github.com:jiminny/app.git\n e5eb1c8958..42e3e90859 JY-18909-automated-reports-ask-jiminny -> JY-18909-automated-reports-ask-jiminny\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix\ndocker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff \nPHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.\nPHP runtime: 8.3.30\nRunning analysis on 7 cores with 10 files per process.\nParallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!\nLoaded config default from \".php-cs-fixer.dist.php\".\n 5589/5589 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%\n\n\nFixed 0 of 5589 files in 39.991 seconds, 67.00 MB memory used\n\nWhat's next:\n Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1\n Learn more at https://docs.docker.com/go/debug-cli/\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix\ndocker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff \nPHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.\nPHP runtime: 8.3.30\nRunning analysis on 7 cores with 10 files per process.\nParallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!\nLoaded config default from \".php-cs-fixer.dist.php\".\n 5589/5589 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%\n\n\nFixed 0 of 5589 files in 39.687 seconds, 67.00 MB memory used\n\nWhat's next:\n Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1\n Learn more at https://docs.docker.com/go/debug-cli/\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix\ndocker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff \nPHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.\nPHP runtime: 8.3.30\nRunning analysis on 7 cores with 10 files per process.\nParallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!\nLoaded config default from \".php-cs-fixer.dist.php\".\n 5589/5589 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%\n\n\nFixed 0 of 5589 files in 52.171 seconds, 67.00 MB memory used\n\nWhat's next:\n Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1\n Learn more at https://docs.docker.com/go/debug-cli/\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csf","depth":4,"value":"Last login: Sat Apr 11 12:38:35 on ttys007\n\nPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parents\n\nPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parents\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix\ndocker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff \nPHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.\nPHP runtime: 8.3.30\nRunning analysis on 7 cores with 10 files per process.\nParallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!\nLoaded config default from \".php-cs-fixer.dist.php\".\n 5579/5579 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%\n\n 1) app/Services/Kiosk/AutomatedReports/AskJiminnyReportActivityService.php (trailing_comma_in_multiline)\n ---------- begin diff ----------\n--- /home/jiminny/app/Services/Kiosk/AutomatedReports/AskJiminnyReportActivityService.php\n+++ /home/jiminny/app/Services/Kiosk/AutomatedReports/AskJiminnyReportActivityService.php\n@@ -51,7 +51,7 @@\n array_merge($requestParams, [\n 'limit' => self::DEFAULT_TOP_ACTIVITIES_COUNT,\n 'page' => 1,\n- 'sequence_number' => 1\n+ 'sequence_number' => 1,\n ]),\n $user->getTimezone()\n );\n\n ----------- end diff -----------\n\n\nFixed 1 of 5579 files in 108.308 seconds, 67.00 MB memory used\n\nWhat's next:\n Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1\n Learn more at https://docs.docker.com/go/debug-cli/\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ git pull\nremote: Enumerating objects: 185, done.\nremote: Counting objects: 100% (185/185), done.\nremote: Compressing objects: 100% (53/53), done.\nremote: Total 185 (delta 138), reused 176 (delta 130), pack-reused 0 (from 0)\nReceiving objects: 100% (185/185), 168.96 KiB | 1.05 MiB/s, done.\nResolving deltas: 100% (138/138), completed with 74 local objects.\nFrom github.com:jiminny/app\n * [new branch] JY-20255-quick-search-show-duration-and-call-conference-type -> origin/JY-20255-quick-search-show-duration-and-call-conference-type\n * [new branch] JY-20297-reposition-the-old-acs-feature -> origin/JY-20297-reposition-the-old-acs-feature\n * [new branch] JY-20541-stale-records-pr-1 -> origin/JY-20541-stale-records-pr-1\nMerge made by the 'ort' strategy.\n .env.production | 2 +-\n .env.production-eu | 2 +-\n .github/workflows/secfix.yml | 10 +\n app/Component/Activity/ActivityProcessingStateManager.php | 4 +\n app/Component/ActivityAnalytics/Service/ActivityAnalyticsService.php | 68 +++----\n app/Component/ActivityAnalytics/Service/LocalTriggerMatcherService.php | 40 ++--\n app/Component/ActivityAnalytics/Service/QuestionService.php | 137 +++++++------\n app/Component/ActivityAnalytics/Service/SentenceBreaker.php | 110 -----------\n app/Component/ActivityAnalytics/VO/TopicTriggerMatch.php | 4 +-\n app/Component/AiAutomation/Actions/UpdateCrmFieldAction.php | 14 +-\n app/Component/AiAutomation/Services/CrmFieldContentTrimmer.php | 92 +++++++++\n app/Component/Kiosk/Services/GetTranscriptionComparisonService.php | 61 +-----\n app/Component/LanguageDetection/Services/DetectLanguageService.php | 74 ++++++-\n app/Component/MediaPipeline/Handlers/LanguageDetectionPipeHandler.php | 37 ++++\n app/Component/MediaPipeline/Handlers/TranscriptionParagraphsPipeHandler.php | 45 +++++\n app/Component/MediaPipeline/MediaPipelineOrchestrator.php | 2 +\n app/Component/MediaPipeline/MediaPipelineServiceProvider.php | 8 +\n app/Component/ParagraphBreaker/DTOs/TranscriptParagraph.php | 46 +++++\n app/Component/ParagraphBreaker/Services/ParagraphBuilder.php | 29 ++-\n app/Component/ParagraphBreaker/Services/TranscriptionParagraphsService.php | 179 +++++++++++++++++\n app/Component/ParagraphBreaker/Services/UtteranceBuilder.php | 192 ++++++++----------\n app/Component/PlaybackPage/Snapshots/Listeners/GenerateSnapshotsListener.php | 6 +\n app/Component/Transcription/Formatter/TranscriptionFormatter.php | 11 +-\n app/Component/Transcription/Job/StartTranscriptDiarizationJob.php | 2 +-\n app/Component/Transcription/Job/TranscriptSegmentsCompileJob.php | 43 +++-\n app/Component/Transcription/Listener/TranscriptSegmentsCompileListener.php | 16 --\n app/Component/Transcription/Service/SearchService.php | 179 ++++++++++-------\n app/Component/Transcription/Service/TranscriptSegmentsService.php | 396 -------------------------------------\n app/Component/Transcription/Service/TranscriptionService.php | 142 ++------------\n app/Component/Transcription/Service/TranslationService.php | 59 ++----\n app/Component/Transcription/TranscriptionProcessor/AssemblyAI/AssemblyAI.php | 172 +---------------\n app/Component/Transcription/TranscriptionProcessor/AssemblyAI/Services/FetchTranscriptService.php | 48 +++++\n .../Transcription/TranscriptionProcessor/AssemblyAI/Services/SubmitAudioFileForLanguageDetectionService.php | 111 +++++++++++\n app/Component/Transcription/VO/TranscriptSegmentTransformer.php | 37 ----\n app/Console/Commands/Crm/Hubspot/RestoreDealAssociationsCommand.php | 344 ++++++++++++++++++++++++--------\n app/Console/Commands/EngagementStats/JiminnyEngagementStatsExplainCommand.php | 76 ++++---\n app/Console/Commands/Mailboxes/InboxSync.php | 9 +-\n app/Http/Controllers/Auth/SocialController.php | 2 +-\n app/Jobs/Mailbox/SyncInbox.php | 2 +-\n app/Listeners/Activities/Coaching/Intercom/CreateActivitySummarizedEvent.php | 6 +\n app/Listeners/Activities/Coaching/Intercom/CreateCommentedEvent.php | 6 +\n app/Listeners/Activities/Coaching/Intercom/CreateManagerCoachedEvent.php | 6 +\n app/Listeners/Activities/Coaching/Intercom/CreatePlayedEvent.php | 6 +\n app/Listeners/Activities/Conferences/Intercom/CreateHeldActivityEvent.php | 6 +\n app/Listeners/Activities/Crm/Summary/AbstractSummaryListener.php | 5 +-\n app/Listeners/Activities/Sms/Intercom/CreateSmsReceivedEvent.php | 6 +\n app/Models/Activity/TopicTrigger.php | 2 +-\n app/Models/Activity/Transcription.php | 29 +--\n app/Providers/EventServiceProvider.php | 1 -\n app/Providers/JiminnyServiceProvider.php | 23 ---\n app/Repositories/QuestionRepository.php | 32 +--\n app/Services/Activity/HubSpot/AuthenticatedHttpClient.php | 75 +------\n app/Services/Activity/HubSpot/RedirectUrlResolver.php | 91 +++++++++\n app/Services/Activity/HubSpot/Service.php | 17 +-\n app/Services/Activity/HubSpot/ZoomRecordingDetector.php | 27 +++\n app/Services/Activity/MeetingBotService.php | 5 +-\n app/Services/Calendar/GoogleCalendarService.php | 3 +\n app/Services/Calendar/OfficeCalendarService.php | 25 +--\n app/Services/Crm/Hubspot/SyncRelatedActivityManager.php | 9 +-\n composer.json | 1 -\n composer.lock | 51 +----\n front-end/src/components/DealInsights/Modals/AskAnythingModal.vue | 1 -\n front-end/src/components/Settings/Kiosk/ActivitiesSearch/ActivityTranscription.vue | 12 +-\n front-end/src/components/shared/Filters/InputText.vue | 2 +\n tests/Unit/Component/Activity/ActivityProcessingStateManagerTest.php | 17 +-\n tests/Unit/Component/ActivityAnalytics/Service/ActivityAnalyticsServiceTest.php | 137 ++++++++++++-\n tests/Unit/Component/ActivityAnalytics/Service/LocalTriggerMatcherServiceTest.php | 135 +++++++------\n tests/Unit/Component/ActivityAnalytics/Service/QuestionServiceTest.php | 610 ++++++++++++---------------------------------------------\n tests/Unit/Component/ActivityAnalytics/VO/TopicTriggerMatchTest.php | 15 ++\n tests/Unit/Component/AiAutomation/Actions/UpdateCrmFieldActionTest.php | 66 +++++++\n tests/Unit/Component/AiAutomation/Services/CrmFieldContentTrimmerTest.php | 282 ++++++++++++++++++++++++++\n tests/Unit/Component/ES/UpdateProcessManagerTest.php | 2 +-\n tests/Unit/Component/Kiosk/Services/GetTranscriptionComparisonServiceTest.php | 79 +++-----\n tests/Unit/Component/LanguageDetection/Services/DetectLanguageServiceTest.php | 132 +++++++++----\n tests/Unit/Component/MediaPipeline/Handlers/LanguageDetectionPipeHandlerTest.php | 97 +++++++++\n tests/Unit/Component/MediaPipeline/Handlers/TranscriptionParagraphsPipeHandlerTest.php | 100 ++++++++++\n tests/Unit/Component/MediaPipeline/Services/GetMediaPipelineStatesServiceTest.php | 6 +\n tests/Unit/Component/ParagraphBreaker/Services/ParagraphBuilderTest.php | 53 ++---\n tests/Unit/Component/ParagraphBreaker/Services/TranscriptionParagraphServiceTest.php | 547 +++++++++++++++++++++++++++++++++++++++++++++++++++\n tests/Unit/Component/ParagraphBreaker/Services/UtteranceBuilderTest.php | 73 ++++---\n tests/Unit/Component/Transcription/Formatter/TranscriptionFormatterTest.php | 8 +-\n tests/Unit/Component/Transcription/Job/TranscriptSegmentCompileJobTest.php | 101 ----------\n tests/Unit/Component/Transcription/Job/TranscriptSegmentsCompileJobTest.php | 220 +++++++++++++++++++++\n tests/Unit/Component/Transcription/Service/SearchServiceTest.php | 193 ++++++++++++++++++\n tests/Unit/Component/Transcription/Service/TranscriptSegmentsServiceTest.php | 465 -------------------------------------------\n tests/Unit/Component/Transcription/Service/TranscriptionServiceTest.php | 61 ++----\n tests/Unit/Component/Transcription/Service/TranslationServiceTest.php | 31 +--\n tests/Unit/Component/Transcription/TranscriptionProcessor/AssemblyAI/AssemblyAITest.php | 355 ++-------------------------------\n .../TranscriptionProcessor/AssemblyAI/Services/SubmitAudioFileForLanguageDetectionServiceTest.php | 220 +++++++++++++++++++++\n tests/Unit/Component/Transcription/VO/TranscriptSegmentTransformerTest.php | 68 -------\n tests/Unit/Jobs/Mailbox/SyncInboxTest.php | 2 +-\n tests/Unit/Services/Activity/HubSpot/AuthenticatedHttpClientTest.php | 207 ++------------------\n tests/Unit/Services/Activity/HubSpot/RedirectUrlResolverTest.php | 184 +++++++++++++++++\n tests/Unit/Services/Activity/HubSpot/ServiceTest.php | 205 ++++++++++++++++++-\n tests/Unit/Services/Activity/MeetingBotServiceOnSharingDataTest.php | 196 ++++++++++++++++++\n tests/Unit/Services/Crm/Hubspot/SyncRelatedActivityManagerTest.php | 105 ++++++++++\n 96 files changed, 4708 insertions(+), 3522 deletions(-)\n create mode 100644 .github/workflows/secfix.yml\n delete mode 100644 app/Component/ActivityAnalytics/Service/SentenceBreaker.php\n create mode 100644 app/Component/AiAutomation/Services/CrmFieldContentTrimmer.php\n create mode 100644 app/Component/MediaPipeline/Handlers/LanguageDetectionPipeHandler.php\n create mode 100644 app/Component/MediaPipeline/Handlers/TranscriptionParagraphsPipeHandler.php\n create mode 100644 app/Component/ParagraphBreaker/DTOs/TranscriptParagraph.php\n create mode 100644 app/Component/ParagraphBreaker/Services/TranscriptionParagraphsService.php\n delete mode 100644 app/Component/Transcription/Listener/TranscriptSegmentsCompileListener.php\n delete mode 100644 app/Component/Transcription/Service/TranscriptSegmentsService.php\n create mode 100644 app/Component/Transcription/TranscriptionProcessor/AssemblyAI/Services/FetchTranscriptService.php\n create mode 100644 app/Component/Transcription/TranscriptionProcessor/AssemblyAI/Services/SubmitAudioFileForLanguageDetectionService.php\n delete mode 100644 app/Component/Transcription/VO/TranscriptSegmentTransformer.php\n create mode 100644 app/Services/Activity/HubSpot/RedirectUrlResolver.php\n create mode 100644 app/Services/Activity/HubSpot/ZoomRecordingDetector.php\n create mode 100644 tests/Unit/Component/AiAutomation/Services/CrmFieldContentTrimmerTest.php\n create mode 100644 tests/Unit/Component/MediaPipeline/Handlers/LanguageDetectionPipeHandlerTest.php\n create mode 100644 tests/Unit/Component/MediaPipeline/Handlers/TranscriptionParagraphsPipeHandlerTest.php\n create mode 100644 tests/Unit/Component/ParagraphBreaker/Services/TranscriptionParagraphServiceTest.php\n delete mode 100644 tests/Unit/Component/Transcription/Job/TranscriptSegmentCompileJobTest.php\n create mode 100644 tests/Unit/Component/Transcription/Job/TranscriptSegmentsCompileJobTest.php\n delete mode 100644 tests/Unit/Component/Transcription/Service/TranscriptSegmentsServiceTest.php\n create mode 100644 tests/Unit/Component/Transcription/TranscriptionProcessor/AssemblyAI/Services/SubmitAudioFileForLanguageDetectionServiceTest.php\n delete mode 100644 tests/Unit/Component/Transcription/VO/TranscriptSegmentTransformerTest.php\n create mode 100644 tests/Unit/Services/Activity/HubSpot/RedirectUrlResolverTest.php\n create mode 100644 tests/Unit/Services/Activity/MeetingBotServiceOnSharingDataTest.php\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ git status\nOn branch JY-18909-automated-reports-ask-jiminny\nYour branch is ahead of 'origin/JY-18909-automated-reports-ask-jiminny' by 2 commits.\n (use \"git push\" to publish your local commits)\n\nChanges not staged for commit:\n (use \"git add <file>...\" to update what will be committed)\n (use \"git restore <file>...\" to discard changes in working directory)\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: .env.local\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: app/Console/Commands/JiminnyDebugCommand.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: app/Http/Controllers/API/ActivityController.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: app/Http/Controllers/Webhook/ReportController.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: app/Jobs/Team/SyncToIntercom.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: app/Services/PlaybackService.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: config/logging.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tmodified: routes/web.php\n\nUntracked files:\n (use \"git add <file>...\" to include in what will be committed)\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\t.env.nikilocal\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\t.env.other\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tWEBHOOK_FILTERING_IMPLEMENTATION.md\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tapp/Console/Commands/Crm/Hubspot/SimulateWebhooksCommand.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tapp/Console/Commands/Reports/CreateMockAskJiminnyReportResultCommand.php\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\tids.txt\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\traw_sql_query.sql\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\ttests/Unit/Policies/CanAccessAiReportsTest.php\n\nno changes added to commit (use \"git add\" and/or \"git commit -a\")\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ git push\nEnumerating objects: 63, done.\nCounting objects: 100% (55/55), done.\nDelta compression using up to 8 threads\nCompressing objects: 100% (28/28), done.\nWriting objects: 100% (29/29), 3.77 KiB | 3.77 MiB/s, done.\nTotal 29 (delta 22), reused 0 (delta 0), pack-reused 0\nremote: Resolving deltas: 100% (22/22), completed with 20 local objects.\nremote: \nremote: GitHub found 24 vulnerabilities on jiminny/app's default branch (2 critical, 12 high, 9 moderate, 1 low). To find out more, visit:\nremote: https://github.com/jiminny/app/security/dependabot\nremote: \nTo github.com:jiminny/app.git\n e5eb1c8958..42e3e90859 JY-18909-automated-reports-ask-jiminny -> JY-18909-automated-reports-ask-jiminny\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix\ndocker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff \nPHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.\nPHP runtime: 8.3.30\nRunning analysis on 7 cores with 10 files per process.\nParallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!\nLoaded config default from \".php-cs-fixer.dist.php\".\n 5589/5589 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%\n\n\nFixed 0 of 5589 files in 39.991 seconds, 67.00 MB memory used\n\nWhat's next:\n Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1\n Learn more at https://docs.docker.com/go/debug-cli/\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix\ndocker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff \nPHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.\nPHP runtime: 8.3.30\nRunning analysis on 7 cores with 10 files per process.\nParallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!\nLoaded config default from \".php-cs-fixer.dist.php\".\n 5589/5589 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%\n\n\nFixed 0 of 5589 files in 39.687 seconds, 67.00 MB memory used\n\nWhat's next:\n Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1\n Learn more at https://docs.docker.com/go/debug-cli/\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix\ndocker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff \nPHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.\nPHP runtime: 8.3.30\nRunning analysis on 7 cores with 10 files per process.\nParallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!\nLoaded config default from \".php-cs-fixer.dist.php\".\n 5589/5589 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%\n\n\nFixed 0 of 5589 files in 52.171 seconds, 67.00 MB memory used\n\nWhat's next:\n Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1\n Learn more at https://docs.docker.com/go/debug-cli/\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csf","is_focused":true},{"role":"AXRadioButton","text":"DOCKER","depth":2,"bounds":{"left":0.23320313,"top":1.0,"width":0.06914063,"height":-0.03680551},"role_description":"radio button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Close Tab","depth":3,"bounds":{"left":0.23554687,"top":1.0,"width":0.00625,"height":-0.039583325},"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXRadioButton","text":"DEV (docker)","depth":2,"bounds":{"left":0.30234376,"top":1.0,"width":0.06914063,"height":-0.03680551},"role_description":"radio button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Close Tab","depth":3,"bounds":{"left":0.3046875,"top":1.0,"width":0.00625,"height":-0.039583325},"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXRadioButton","text":"APP (-zsh)","depth":2,"bounds":{"left":0.37148437,"top":1.0,"width":0.06914063,"height":-0.03680551},"role_description":"radio button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Close Tab","depth":3,"bounds":{"left":0.3738281,"top":1.0,"width":0.00625,"height":-0.039583325},"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXRadioButton","text":"ec2-user@ip-10-30-93-249:~ (nc)","depth":2,"bounds":{"left":0.440625,"top":1.0,"width":0.06914063,"height":-0.03680551},"role_description":"radio button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Close Tab","depth":3,"bounds":{"left":0.44296876,"top":1.0,"width":0.00625,"height":-0.039583325},"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXRadioButton","text":"-zsh","depth":2,"bounds":{"left":0.5097656,"top":1.0,"width":0.06914063,"height":-0.03680551},"role_description":"radio button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Close Tab","depth":3,"bounds":{"left":0.5121094,"top":1.0,"width":0.00625,"height":-0.039583325},"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXRadioButton","text":"-zsh","depth":2,"bounds":{"left":0.57890624,"top":1.0,"width":0.06914063,"height":-0.03680551},"role_description":"radio button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Close Tab","depth":3,"bounds":{"left":0.58125,"top":1.0,"width":0.00625,"height":-0.039583325},"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXRadioButton","text":"-zsh","depth":2,"bounds":{"left":0.64804685,"top":1.0,"width":0.06914063,"height":-0.03680551},"role_description":"radio button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Close Tab","depth":3,"bounds":{"left":0.6503906,"top":1.0,"width":0.00625,"height":-0.039583325},"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXRadioButton","text":"✳ Unable to access screenpipe activity data (claude)","depth":2,"bounds":{"left":0.7171875,"top":1.0,"width":0.06914063,"height":-0.03680551},"role_description":"radio button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Close Tab","depth":3,"bounds":{"left":0.71953124,"top":1.0,"width":0.00625,"height":-0.039583325},"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"⌥⌘1","depth":1,"bounds":{"left":0.7703125,"top":1.0,"width":0.021875,"height":-0.02013886},"automation_id":"_NS:8","role_description":"text"},{"role":"AXStaticText","text":"APP (-zsh)","depth":1,"bounds":{"left":0.5,"top":1.0,"width":0.02890625,"height":-0.020833373},"role_description":"text"}]...
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-6100822178674153346
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7426412122741684952
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click
|
accessibility
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NULL
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Last login: Sat Apr 11 12:38:35 on ttys007
Poetry Last login: Sat Apr 11 12:38:35 on ttys007
Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parents
Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parents
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix
docker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff
PHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.
PHP runtime: 8.3.30
Running analysis on 7 cores with 10 files per process.
Parallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!
Loaded config default from ".php-cs-fixer.dist.php".
5579/5579 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%
1) app/Services/Kiosk/AutomatedReports/AskJiminnyReportActivityService.php (trailing_comma_in_multiline)
---------- begin diff ----------
--- /home/jiminny/app/Services/Kiosk/AutomatedReports/AskJiminnyReportActivityService.php
+++ /home/jiminny/app/Services/Kiosk/AutomatedReports/AskJiminnyReportActivityService.php
@@ -51,7 +51,7 @@
array_merge($requestParams, [
'limit' => self::DEFAULT_TOP_ACTIVITIES_COUNT,
'page' => 1,
- 'sequence_number' => 1
+ 'sequence_number' => 1,
]),
$user->getTimezone()
);
----------- end diff -----------
Fixed 1 of 5579 files in 108.308 seconds, 67.00 MB memory used
What's next:
Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1
Learn more at [URL_WITH_CREDENTIALS] ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ git status
On branch JY-18909-automated-reports-ask-jiminny
Your branch is ahead of 'origin/JY-18909-automated-reports-ask-jiminny' by 2 commits.
(use "git push" to publish your local commits)
Changes not staged for commit:
(use "git add <file>..." to update what will be committed)
(use "git restore <file>..." to discard changes in working directory)
modified: .env.local
modified: app/Console/Commands/JiminnyDebugCommand.php
modified: app/Http/Controllers/API/ActivityController.php
modified: app/Http/Controllers/Webhook/ReportController.php
modified: app/Jobs/Team/SyncToIntercom.php
modified: app/Services/PlaybackService.php
modified: config/logging.php
modified: routes/web.php
Untracked files:
(use "git add <file>..." to include in what will be committed)
.env.nikilocal
.env.other
WEBHOOK_FILTERING_IMPLEMENTATION.md
app/Console/Commands/Crm/Hubspot/SimulateWebhooksCommand.php
app/Console/Commands/Reports/CreateMockAskJiminnyReportResultCommand.php
ids.txt
raw_sql_query.sql
tests/Unit/Policies/CanAccessAiReportsTest.php
no changes added to commit (use "git add" and/or "git commit -a")
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ git push
Enumerating objects: 63, done.
Counting objects: 100% (55/55), done.
Delta compression using up to 8 threads
Compressing objects: 100% (28/28), done.
Writing objects: 100% (29/29), 3.77 KiB | 3.77 MiB/s, done.
Total 29 (delta 22), reused 0 (delta 0), pack-reused 0
remote: Resolving deltas: 100% (22/22), completed with 20 local objects.
remote:
remote: GitHub found 24 vulnerabilities on jiminny/app's default branch (2 critical, 12 high, 9 moderate, 1 low). To find out more, visit:
remote: [URL_WITH_CREDENTIALS] ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix
docker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff
PHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.
PHP runtime: 8.3.30
Running analysis on 7 cores with 10 files per process.
Parallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!
Loaded config default from ".php-cs-fixer.dist.php".
5589/5589 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%
Fixed 0 of 5589 files in 39.991 seconds, 67.00 MB memory used
What's next:
Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1
Learn more at [URL_WITH_CREDENTIALS] ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csfix
docker exec -it docker_lamp_1 ./vendor/bin/php-cs-fixer fix --config=.php-cs-fixer.dist.php -v --using-cache=no --diff
PHP CS Fixer 3.87.1 Alexander by Fabien Potencier, Dariusz Ruminski and contributors.
PHP runtime: 8.3.30
Running analysis on 7 cores with 10 files per process.
Parallel runner is an experimental feature and may be unstable, use it at your own risk. Feedback highly appreciated!
Loaded config default from ".php-cs-fixer.dist.php".
5589/5589 [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 100%
Fixed 0 of 5589 files in 52.171 seconds, 67.00 MB memory used
What's next:
Try Docker Debug for seamless, persistent debugging tools in any container or image → docker debug docker_lamp_1
Learn more at https://docs.docker.com/go/debug-cli/
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-18909-automated-reports-ask-jiminny) $ csf
DOCKER
Close Tab
DEV (docker)
Close Tab
APP (-zsh)
Close Tab
ec2-user@ip-10-30-93-249:~ (nc)
Close Tab
-zsh
Close Tab
-zsh
Close Tab
-zsh
Close Tab
✳ Unable to access screenpipe activity data (claude)
Close Tab
⌥⌘1
APP (-zsh)...
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13046
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81
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Firefox
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True
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Pause
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00:55:48
01:44:04
Pause
Original
1.0X
...
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Pause
Origin Melania
00:45:33
01:44:04
Pause
Original
1.0X
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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...
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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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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...
|
72023
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71980
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1739
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19
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2026-04-22T14:36:36.023851+00:00
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/Users/lukas/.screenpipe/data/data/2026-04-22/1776 /Users/lukas/.screenpipe/data/data/2026-04-22/1776868596023_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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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...
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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. 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This will make it easier for customers to find us and to connect Jiminny","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide customers with example workflows to get started quickly","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"medium","depth":22,"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":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"See examples in table below.","depth":22,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to use predefined Jiminny skills for common workflows","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 [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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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
[SRD-6787] Issue with reconnecting Zoho - Jira
[SRD-6787] Issue with reconnecting Zoho - Jira
[SRD-6787] Issue with reconnecting Zoho - Jira
[SRD-6787] Issue with reconnecting Zoho - Jira
JY-20698 handle failed field sync on playbook import activity types by LakyLak · Pull Request #11988 · jiminny/app
JY-20698 handle failed field sync on playbook import activity types by LakyLak · Pull Request #11988 · jiminny/app
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
[JY-20676] Notify the user if a Panorama prompts is deleted but is used in AJ Report - Jira
[JY-20676] Notify the user if a Panorama prompts is deleted but is used in AJ Report - Jira
Jiminny Mail
Jiminny Mail
[JY-20500] Batch initial sync for Salesforce - Jira
[JY-20500] Batch initial sync for Salesforce - Jira
Feed — jiminny — Sentry
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[SRD-6787] Issue with reconnecting Zoho - Jira
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JY-20698 handle failed field sync on playbook import activity types by LakyLak · Pull Request #11988 · jiminny/app
JY-20698 handle failed field sync on playbook import activity types by LakyLak · Pull Request #11988 · jiminny/app
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
[SRD-6787] Issue with reconnecting Zoho - Jira
[SRD-6787] Issue with reconnecting Zoho - Jira
[SRD-6787] Issue with reconnecting Zoho - Jira
[SRD-6787] Issue with reconnecting Zoho - Jira
JY-20698 handle failed field sync on playbook import activity types by LakyLak · Pull Request #11988 · jiminny/app
JY-20698 handle failed field sync on playbook import activity types by LakyLak · Pull Request #11988 · jiminny/app
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Jiminny MCP Connector - Product - Confluence
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[JY-20676] Notify the user if a Panorama prompts is deleted but is used in AJ Report - Jira
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
[SRD-6787] Issue with reconnecting Zoho - Jira
[SRD-6787] Issue with reconnecting Zoho - Jira
[SRD-6787] Issue with reconnecting Zoho - Jira
[SRD-6787] Issue with reconnecting Zoho - Jira
JY-20698 handle failed field sync on playbook import activity types by LakyLak · Pull Request #11988 · jiminny/app
JY-20698 handle failed field sync on playbook import activity types by LakyLak · Pull Request #11988 · jiminny/app
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
[JY-20676] Notify the user if a Panorama prompts is deleted but is used in AJ Report - Jira
[JY-20676] Notify the user if a Panorama prompts is deleted but is used in AJ Report - Jira
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Jiminny Mail
[JY-20500] Batch initial sync for Salesforce - Jira
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28m
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
[SRD-6787] Issue with reconnecting Zoho - Jira
[SRD-6787] Issue with reconnecting Zoho - Jira
[SRD-6787] Issue with reconnecting Zoho - Jira
[SRD-6787] Issue with reconnecting Zoho - Jira
JY-20698 handle failed field sync on playbook import activity types by LakyLak · Pull Request #11988 · jiminny/app
JY-20698 handle failed field sync on playbook import activity types by LakyLak · Pull Request #11988 · jiminny/app
Jiminny MCP Connector - Product - Confluence
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[JY-20676] Notify the user if a Panorama prompts is deleted but is used in AJ Report - Jira
[JY-20676] Notify the user if a Panorama prompts is deleted but is used in AJ Report - Jira
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1
times played
Adriana Voyce at Credentially
Meeting with Stoyan 🚀 (Adriana Voyce)
Meeting with Stoyan 🚀 (Adriana Voyce)
1
times played
Diane Farah at Flosonics Medical
Meeting with Stoyan 🚀 (Diane Farah)
Meeting with Stoyan 🚀 (Diane Farah)
1
times played
Melissa Desousa at Lloyd's List Intelligence
Melissa De Sousa and Petko Kashinski
Melissa De Sousa and Petko Kashinski
1
times played
Graeme Hennessey at RealTime eClinical Solutions
Jiminny - Commercials Review & Next Steps 🚀
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1
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Oscar Linares at Storyclash GmbH
Oscar Linares and Petko Kashinski
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1
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James Casey at Referoo
Notetaker added by Oliver Harris
Notetaker added by Oliver Harris
1
times played
Live Feed
Live Feed
Stoyan Tanev
listened to call
Yesterday, 5:28 PM
Support / Troubleshooting
with
Diane Farah
Held:
17 Apr, 4:58 PM
Duration:
7m
Value:
$22,656
Lauren Hudson
listened to call
Yesterday, 5:16 PM
Kick-Off / Onboarding
with
Todd Upchurch
Held:
8 Apr, 4:28 PM
Duration:
33m
Value:
$4,800
Becky Butler
listened to call
Yesterday, 4:43 PM
Disco/Demo
with
Graeme Hennessey
Held:
10 Apr, 5:29 PM
Duration:
31m
Value:
$19,716
Calum Scott
listened to call
Yesterday, 3:17 PM
Trial Strategy Session
with
James Casey
Held:
24 Mar, 4:34 PM
Duration:
22m
Value:
$9,600
Calum Scott
listened to call
Yesterday, 3:14 PM
Kick-Off / Onboarding
with
unknown customer
Held:
16 Mar, 6:03 PM
Duration:
28m
Value:
$0
Calum Scott
listened to call
Yesterday, 3:08 PM
End of Trial check in
with
James Casey...
|
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
[SRD-6787] Issue with reconnecting Zoho - Jira
[SRD-6787] Issue with reconnecting Zoho - Jira
[SRD-6787] Issue with reconnecting Zoho - Jira
[SRD-6787] Issue with reconnecting Zoho - Jira
JY-20698 handle failed field sync on playbook import activity types by LakyLak · Pull Request #11988 · jiminny/app
JY-20698 handle failed field sync on playbook import activity types by LakyLak · Pull Request #11988 · jiminny/app
Jiminny MCP Connector - Product - Confluence
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[JY-20676] Notify the user if a Panorama prompts is deleted but is used in AJ Report - Jira
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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. 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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"},{"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"},{"role":"AXStaticText","text":"Pull key points, commitments, and next steps.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Apply the chosen tone and structure.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Draft the email in a consistent format.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Customer-ready follow-up email draft","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Summarise call","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 users quickly extract the most important points from a conversation","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Generate a concise summary of one call for internal or external use","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 summary style","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"optional audience","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Retrieve transcript, metadata, and structured insights.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Identify key topics, decisions, risks, and next steps.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Format output for the chosen audience.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Short or structured call summary with actions and risks","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Generate deal recap","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 and managers get a fast overview of a deal","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Summarise the current state of a deal based on recent calls and linked CRM context","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 date range","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Retrieve calls and meetings linked to the deal.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Pull deal 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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:...
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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. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Goal","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Metric","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"% of customers connecting external AI tools","depth":23,"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":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Decrease the manual copying of transcripts","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"decrease by X %","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"User interaction and design","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Simple setup flow:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Generate API key / connect MCP","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Clear instructions for Claude / Zapier setup","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Example prompts for Claude","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Pre-built workflow templates for Zapier","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Clear documentation - with use cases","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"👉 Focus: usable by non-technical users","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Detailed Requirements","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"List the functional and non-functional requirements. 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stack","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to access call data through AI tools","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":"Expose Jiminny data:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"list calls (filter by date, user, account, deal)","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"get call","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"get transcript (speaker-separated)","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"get call 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tools","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":"Support keyword and semantic search across conversations","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide structured insights so customers can generate better outputs","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":"For each call, expose:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Summary","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Action items","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Key points","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Activity type","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Notes","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Call Score","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"CRM information","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to understand deal status across multiple calls/emails","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":"For 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 -","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\": 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Sales\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"Share security documentation\"","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":"Ensure the AI can easily understand the schema of the data coming from Jiminny","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":"Models perform much better when tools have:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"clear 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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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7412361299820697113
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visual_change
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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:...
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JY-9712 | Nuges to expire after one year by nikola JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app — 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...
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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. 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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"},{"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"},{"role":"AXStaticText","text":"Pull key points, commitments, and next steps.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Apply the chosen tone and structure.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Draft the email in a consistent format.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Customer-ready follow-up email draft","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Summarise call","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 users quickly extract the most important points from a conversation","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Generate a concise summary of one call for internal or external use","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 summary style","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"optional audience","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Retrieve transcript, metadata, and structured insights.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Identify key topics, decisions, risks, and next steps.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Format output for the chosen audience.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Short or structured call summary with actions and risks","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Generate deal recap","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 and managers get a fast overview of a deal","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Summarise the current state of a deal based on recent calls and linked CRM context","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 date range","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Retrieve calls and meetings linked to the deal.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Pull deal metadata and CRM-linked context.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Identify recent changes, blockers, risks, and next steps.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Summarise into a concise recap.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Deal recap with status, blockers, risks, and recommended next steps","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Prepare weekly recap","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 managers or reps review what changed over a time 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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"}]...
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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...
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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"
}...
|
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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"}]...
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769140000846151667
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7412361299820696153
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visual_change
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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
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"
}...
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1763
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12
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2026-04-22T15:36:20.835690+00:00
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/Users/lukas/.screenpipe/data/data/2026-04-22/1776 /Users/lukas/.screenpipe/data/data/2026-04-22/1776872180835_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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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...
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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. 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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 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"},{"role":"AXStaticText","text":"Pull key points, commitments, and next steps.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Apply the chosen tone and structure.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Draft the email in a consistent format.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Customer-ready follow-up email draft","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Summarise call","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 users quickly extract the most important points from a conversation","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Generate a concise summary of one call for internal or external use","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 summary style","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"optional audience","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Retrieve transcript, metadata, and structured insights.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Identify key topics, decisions, risks, and next steps.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Format output for the chosen audience.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Short or structured call summary with actions and risks","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Generate deal recap","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 and managers get a fast overview of a deal","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Summarise the current state of a deal based on recent calls and linked CRM context","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 date range","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Retrieve calls and meetings linked to the deal.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Pull deal metadata and CRM-linked context.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Identify recent changes, blockers, risks, and next steps.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Summarise into a concise recap.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Deal recap with status, blockers, risks, and recommended next steps","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Prepare weekly recap","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 managers or reps review what changed over a time period","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Weekly recap of calls, themes, and actions for one rep, team, or account","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":"team ID or account ID","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"time period","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Retrieve relevant calls and summaries for the selected time period.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Aggregate themes, action items, and changes.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Highlight notable risks, opportunities, and follow-ups.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Weekly recap with top themes, risks, opportunities, and actions","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Prepare deal review","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 reps and managers review deal status using multiple sources of context","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Review a deal before pipeline review or forecast meeting","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Deal ID","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"}]...
|
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7412361299820696153
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visual_change
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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
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...
|
72339
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72328
|
1762
|
2
|
2026-04-22T15:35:13.384518+00:00
|
/Users/lukas/.screenpipe/data/data/2026-04-22/1776 /Users/lukas/.screenpipe/data/data/2026-04-22/1776872113384_m1.jpg...
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Firefox
|
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_1
|
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...
|
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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"}]...
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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...
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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...
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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. 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"},{"role":"AXStaticText","text":"Pull key points, commitments, and next steps.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Apply the chosen tone and structure.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Draft the email in a consistent format.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Customer-ready follow-up email draft","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Summarise call","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 users quickly extract the most important points from a conversation","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Generate a concise summary of one call for internal or external use","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 summary style","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"optional audience","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Retrieve transcript, metadata, and structured insights.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Identify key topics, decisions, risks, and next steps.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Format output for the chosen audience.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Short or structured call summary with actions and risks","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Generate deal recap","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 and managers get a fast overview of a deal","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Summarise the current state of a deal based on recent calls and linked CRM context","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 date range","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Retrieve calls and meetings linked to the deal.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Pull deal metadata and CRM-linked context.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Identify recent changes, blockers, risks, and next steps.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Summarise into a concise recap.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Deal recap with status, blockers, risks, and recommended next steps","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Prepare weekly recap","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 managers or reps review what changed over a time 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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 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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 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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"}]...
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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...
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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...
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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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using the Jiminny API is not suitable for non technical sales people","depth":23,"bounds":{"left":0.30868056,"top":0.4677778,"width":0.1,"height":0.24055555},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Peer pressure as almost all competitors have this","depth":23,"bounds":{"left":0.30868056,"top":0.71666664,"width":0.0875,"height":0.116111115},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"💥 Impact and benefits","depth":23,"bounds":{"left":0.30868056,"top":0.9027778,"width":0.07083333,"height":0.054444443},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"For customers","depth":23,"bounds":{"left":0.30868056,"top":0.96555555,"width":0.072222225,"height":0.022777777},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Better AI outputs using real customer conversations","depth":23,"bounds":{"left":0.30868056,"top":0.99666667,"width":0.099305555,"height":0.0033333302},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Ability to build automated workflows across tools","depth":23,"bounds":{"left":0.30868056,"top":1.0,"width":0.08715278,"height":-0.09000003},"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. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Goal","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Metric","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"% of customers connecting external AI tools","depth":23,"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":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Decrease the manual copying of transcripts","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"decrease by X %","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"User interaction and design","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Simple setup flow:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Generate API key / connect MCP","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Clear instructions for Claude / Zapier setup","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Example prompts for Claude","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Pre-built workflow templates for Zapier","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Clear documentation - with use cases","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"👉 Focus: usable by non-technical users","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Detailed Requirements","depth":23,"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":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Requirement","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Importance","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Notes","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to connect Jiminny to their AI tools (e.g. Claude, OpenAI)","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":"Provide MCP endpoint so customers can easily integrate Jiminny into their AI 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items","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Key points","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Activity type","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Notes","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Call Score","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"CRM information","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Allow customers to understand deal status across multiple calls/emails","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":"For 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 -","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 2026-04-01\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"Pricing discussion on 2026-04-03\"","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"],","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"risks\": [","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"Pricing concerns\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"Security review pending\"","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"], \"next_steps\": [","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"Send follow-up to VP Sales\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"Share security documentation\"","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":"Ensure the AI can easily understand the schema of the data coming from Jiminny","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":"Models perform much better when tools have:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"clear 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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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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...
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JY-9712 | Nuges to expire after one year by nikola JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app — 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...
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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. 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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"},{"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"},{"role":"AXStaticText","text":"Pull key points, commitments, and next steps.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Apply the chosen tone and structure.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Draft the email in a consistent format.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Customer-ready follow-up email draft","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Summarise call","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 users quickly extract the most important points from a conversation","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Generate a concise summary of one call for internal or external use","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 summary style","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"optional audience","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Retrieve transcript, metadata, and structured insights.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Identify key topics, decisions, risks, and next steps.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Format output for the chosen audience.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Short or structured call summary with actions and risks","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Generate deal recap","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 and managers get a fast overview of a deal","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Summarise the current state of a deal based on recent calls and linked CRM context","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 date range","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Retrieve calls and meetings linked to the deal.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Pull deal metadata and CRM-linked context.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Identify recent changes, blockers, risks, and next steps.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Summarise into a concise recap.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Deal recap with status, blockers, risks, and recommended next steps","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Prepare weekly recap","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 managers or reps review what changed over a time period","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Weekly recap of calls, themes, and actions for one rep, team, or account","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":"team ID or account ID","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"}]...
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7412220562600776281
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visual_change
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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
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...
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2026-04-22T14:52:03.284454+00:00
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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...
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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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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 -","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 2026-04-01\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"Pricing discussion on 2026-04-03\"","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"],","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"risks\": [","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"Pricing concerns\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"Security review pending\"","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"], \"next_steps\": [","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"Send follow-up to VP Sales\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"Share security documentation\"","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":"Ensure the AI can easily understand the schema of the data coming from Jiminny","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":"Models perform much better when tools have:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"clear names","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"rich descriptions","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"predictable schemas","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide clear, actionable error states for customers when MCP connections fail","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":"Error messages should guide agents toward solutions with specific suggestions and next steps.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"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.”","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"}]...
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click
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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...
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9
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2026-04-22T14:42:31.999168+00:00
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/Users/lukas/.screenpipe/data/data/2026-04-22/1776 /Users/lukas/.screenpipe/data/data/2026-04-22/1776868951999_m2.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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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
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...
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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. 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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"},{"role":"AXStaticText","text":"Pull key points, commitments, and next steps.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Apply the chosen tone and structure.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Draft the email in a consistent format.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Customer-ready follow-up email draft","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Summarise call","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 users quickly extract the most important points from a conversation","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Generate a concise summary of one call for internal or external use","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 summary style","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"optional audience","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Retrieve transcript, metadata, and structured insights.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Identify key topics, decisions, risks, and next steps.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Format output for the chosen audience.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Short or structured call summary with actions and risks","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Generate deal 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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
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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...
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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. Include both qualitative and quantitative metrics (e.g., user adoption, retention rate, revenue targets).","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Goal","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Metric","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Increase stickiness by ensuring Jiminny is embedded in the customer’s workflows","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"% of customers connecting external AI tools","depth":23,"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":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Decrease the manual copying of transcripts","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"decrease by X %","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"User interaction and design","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Simple setup flow:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Generate API key / connect MCP","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Clear instructions for Claude / Zapier setup","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Example prompts for Claude","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Pre-built workflow templates for Zapier","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Clear documentation - with use cases","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"👉 Focus: usable by non-technical users","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Detailed Requirements","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"List the functional and non-functional 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 [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...
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Firefox
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Work — Mozilla Firefox
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True
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github.com/jiminny/app/pull/11983
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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...
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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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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...
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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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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 [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 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
|
7412220562332342809
|
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 [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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NULL
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72179
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1751
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19
|
2026-04-22T15:08:20.398365+00:00
|
/Users/lukas/.screenpipe/data/data/2026-04-22/1776 /Users/lukas/.screenpipe/data/data/2026-04-22/1776870500398_m2.jpg...
|
Firefox
|
Work — Mozilla Firefox
|
True
|
github.com/jiminny/app/pull/11981
|
monitor_2
|
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...
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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. 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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"}]...
|
-2829648071569778537
|
7412220562332340761
|
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 [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...
|
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72282
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1757
|
22
|
2026-04-22T15:23:21.919562+00:00
|
/Users/lukas/.screenpipe/data/data/2026-04-22/1776 /Users/lukas/.screenpipe/data/data/2026-04-22/1776871401919_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
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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...
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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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Sales\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"Share security documentation\"","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":"Ensure the AI can easily understand the schema of the data coming from Jiminny","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":"Models perform much better when tools have:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"clear names","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"rich descriptions","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"predictable schemas","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide clear, actionable error states for customers when MCP connections fail","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":"Error messages should guide agents toward solutions with specific suggestions and next steps.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"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.”","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"}]...
|
9021723979828606460
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7412220562332340761
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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 [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...
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2026-04-22T14:39:54.980170+00:00
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Jiminny MCP Connector - Product - Confluence — Wor Jiminny MCP Connector - Product - Confluence — Work...
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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...
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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"}]...
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879278402273496789
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7411094662693935641
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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...
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2026-04-22T14:39:14.502923+00:00
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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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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...
|
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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 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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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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2026-04-22T14:39:54.980161+00:00
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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...
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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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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. 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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"}]...
|
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7411094662693919321
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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
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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2026-04-22T15:08:13.297923+00:00
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/Users/lukas/.screenpipe/data/data/2026-04-22/1776 /Users/lukas/.screenpipe/data/data/2026-04-22/1776870493297_m1.jpg...
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Firefox
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JY-9712 | change nudges schema by nikolaybiaivanov JY-9712 | change nudges schema by nikolaybiaivanov · Pull Request #11983 · jiminny/app — Work...
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True
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github.com/jiminny/app/pull/11983
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monitor_1
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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:
{...
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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. 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"}]...
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3338122994377541266
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7411094662425500185
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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:
{...
|
72174
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72204
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1752
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7
|
2026-04-22T15:11:33.666375+00:00
|
/Users/lukas/.screenpipe/data/data/2026-04-22/1776 /Users/lukas/.screenpipe/data/data/2026-04-22/1776870693666_m1.jpg...
|
Firefox
|
Architecture overview - Model Context Protocol — W Architecture overview - Model Context Protocol — Work...
|
True
|
modelcontextprotocol.io/docs/learn/architecture
|
monitor_1
|
NULL
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NULL
|
NULL
|
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 [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...
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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 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"}]...
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-5527921122764584061
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7411094662425498137
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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 [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...
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19
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2026-04-22T15:08:21.048787+00:00
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/Users/lukas/.screenpipe/data/data/2026-04-22/1776 /Users/lukas/.screenpipe/data/data/2026-04-22/1776870501048_m1.jpg...
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JY-9712 | Nuges to expire after one year by nikola JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app — Work...
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True
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github.com/jiminny/app/pull/11981
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monitor_1
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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...
|
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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"}]...
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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...
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JY-9712 | Nuges to expire after one year by nikola JY-9712 | Nuges to expire after one year by nikolaybiaivanov · Pull Request #11981 · jiminny/app — Work...
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True
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github.com/jiminny/app/pull/11981/changes
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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...
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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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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 -","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 2026-04-01\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"Pricing discussion on 2026-04-03\"","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"],","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"risks\": [","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"Pricing concerns\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"Security review pending\"","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"], \"next_steps\": [","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"Send follow-up to VP Sales\",","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"Share security documentation\"","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":"Ensure the AI can easily understand the schema of the data coming from Jiminny","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":"Models perform much better when tools have:","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"clear names","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"rich descriptions","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"predictable schemas","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Provide clear, actionable error states for customers when MCP connections fail","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":"Error messages should guide agents toward solutions with specific suggestions and next steps.","depth":23,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"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.”","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. 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"}]...
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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...
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Preskočiť na hlavný obsah
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Prezerať audiokomentár
Domovská stránka služby H B O Max
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Moje tituly
Ponuky Profil a Nastavenia. Profil používateľa Lukáš.. Avatar: Meerkat from Crikey! It's the Irwins
Hriešnici
Hodnotené: 14, Sex, Jazyk, Omamné látky, Trvanie: 53 minút, Uvedené v roku 2014
Obnoviť Hriešnici Séria 4, epizóda 3: Časť 3, zostáva: 31 min
Reštartovať Hriešnici Séria 4, epizóda 3: Časť 3
Pridať do Môjho zoznamu: Hriešnici
Ohodnotiť tento seriál
Séria 4, epizóda 3: Časť 3
S4 E3: Časť 3
Fiona a Mikeov drogovo závislý brat Robbie spolu dobre vychádzajú.
Žánre: Komédia, Čierna komédia
Epizód
Séria 4
Predchádzajúce tituly
Pozerať znova: Séria 4, epizóda 1: Časť 1. 1 z 12. Hodnotené: 14, Sex, Jazyk, Omamné látky. Trvanie: 54 minút. Uvedené v roku 2014. Gallagherovci sa snažia ísť ďalej.
E1: Časť 1
54 min
2014
Gallagherovci sa snažia ísť ďalej.
Pozerať znova: Séria 4, epizóda 2: Časť 2. 2 z 12. Hodnotené: 14, Sex, Jazyk, Omamné látky. Trvanie: 48 minút. Uvedené v roku 2014. Keď Fiona odmietne darovať pečeň, musí sa Frank poobzerať inde.
E2: Časť 2
48 min
2014
Keď Fiona odmietne darovať pečeň, musí sa Frank poobzerať inde.
Pozerať Séria 4, epizóda 3: Časť 3. 3 z 12. Zostáva: 32 minút. Hodnotené: 14, Sex, Jazyk, Omamné látky. Trvanie: 53 minút. Uvedené v roku 2014. Fiona a Mikeov drogovo závislý brat Robbie spolu dobre vychádzajú.
E3: Časť 3
53 min
2014
Fiona a Mikeov drogovo závislý brat Robbie spolu dobre vychádzajú.
Ponuka pre epizódu 3: Časť 3
Séria 4, epizóda 4: Časť 4. 4 z 12. Hodnotené: 14, Sex, Jazyk, Omamné látky. Trvanie: 55 minút. Uvedené v roku 2014. Frank si vymyslí "nehodu", aby mohol zaplatiť za transplantáciu.
E4: Časť 4
55 min
2014
Frank si vymyslí "nehodu", aby mohol zaplatiť za transplantáciu.
Ponuka pre epizódu 4: Časť 4
Séria 4, epizóda 5: Časť 5. 5 z 12. Hodnotené: 14, Sex, Jazyk, Omamné látky. Trvanie: 50 minút. Uvedené v roku 2014. Robbie povie Mikeovi o svojom pomere s Fionou.
E5: Časť 5
50 min
2014
Robbie povie Mikeovi o svojom pomere s Fionou.
Ponuka pre epizódu 5: Časť 5
Séria 4, epizóda 6: Časť 6. 6 z 12. Hodnotené: 14, Sex, Jazyk, Omamné látky. Trvanie: 55 minút. Uvedené v roku 2014. Následky Liamovej nehody dostanú Fionu do väzenia.
E6: Časť 6
55 min
2014
Následky Liamovej nehody dostanú Fionu do väzenia.
Ponuka pre epizódu 6: Časť 6
Séria 4, epizóda 7: Časť 7. 7 z 12. Hodnotené: 14, Sex, Jazyk, Omamné látky. Trvanie: 49 minút. Uvedené v roku 2014. Lip robí všetko pre to, aby rodinu udržal pohromade.
E7: Časť 7
49 min
2014
Lip robí všetko pre to, aby rodinu udržal pohromade.
Séria 4, epizóda 8: Časť 8. 8 z 12. Hodnotené: 14, Sex, Jazyk, Omamné látky. Trvanie: 54 minút. Uvedené v roku 2014. Ian sa konečne vracia domov.
E8: Časť 8
54 min
2014
Ian sa konečne vracia domov.
Séria 4, epizóda 9: Časť 9. 9 z 12. Hodnotené: 14, Sex, Jazyk, Omamné látky. Trvanie: 52 minút. Uvedené v roku 2014. Carl sa zoznámi s problémovou dievčinou menom Bonnie.
E9: Časť 9
52 min
2014
Carl sa zoznámi s problémovou dievčinou menom Bonnie.
Séria 4, epizóda 10: Časť 10. 10 z 12. Hodnotené: 14, Sex, Jazyk, Omamné látky. Trvanie: 50 minút. Uvedené v roku 2014. Fiona poruší podmienku.
E10: Časť 10
50 min
2014
Fiona poruší podmienku.
Séria 4, epizóda 11: Časť 11. 11 z 12. Hodnotené: 14, Sex, Jazyk, Omamné látky. Trvanie: 48 minút. Uvedené v roku 2014. Fiona je poslaná do nápravného zariadenia.
E11: Časť 11
48 min
2014
Fiona je poslaná do nápravného zariadenia.
Séria 4, epizóda 12: Časť 12. 12 z 12. Hodnotené: 14, Sex, Jazyk, Omamné látky. Trvanie: 54 minút. Uvedené v roku 2014. Frank sa začína zotavovať a cíti sa ako predtým.
E12: Časť 12
54 min
2014
Frank sa začína zotavovať a cíti sa ako predtým.
Nasledujúce tituly
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Prvé manželstvo Georgieho a Mandy. Riadok 1 z 5, stĺpec 4 z 6. Nová epizóda
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Girls. Riadok 3 z 5, stĺpec 1 z 6
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Roztomilé mrchy. Riadok 3 z 5, stĺpec 3 z 6
Silicon Valley. Riadok 3 z 5, stĺpec 4 z 6
Sexuálny život vysokoškoláčok. Riadok 3 z 5, stĺpec 5 z 6
Hung - Na veľkosti záleží. Riadok 3 z 5, stĺpec 6 z 6
Boj o riaditeľňu. Riadok 4 z 5, stĺpec 1 z 6
Biely lotos. Riadok 4 z 5, stĺpec 2 z 6
John Oliver: Čo týždeň dal a vzal. Riadok 4 z 5, stĺpec 3 z 6
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Hráči. Riadok 4 z 5, stĺpec 5 z 6
Moje nové Ja. Riadok 4 z 5, stĺpec 6 z 6
Viceprezident(ka). Riadok 5 z 5, stĺpec 1 z 6
V mene nášho Pána. Riadok 5 z 5, stĺpec 2 z 6
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Vo štvorici. Riadok 5 z 5, stĺpec 4 z 6
21 Jump street. Riadok 5 z 5, stĺpec 5 z 6
Normálka. Riadok 5 z 5, stĺpec 6 z 6
H B O Max You tube (otvorí sa na novej karte), 1 z 12
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Pomoc (otvorí sa na novej karte), 12 z 12
Pomoc
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Hriešnici
https://play.hbomax.com/show/06f986dd-dd6a-4596-9a0a-f473ef99c64b
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Ponuky Profil a Nastavenia. Profil používateľa Lukáš.. Avatar: Meerkat from Crikey! It's the Irwins
Hriešnici
Hodnotené: 14, Sex, Jazyk, Omamné látky, Trvanie: 53 minút, Uvedené v roku 2014
Obnoviť Hriešnici Séria 4, epizóda 3: Časť 3, zostáva: 31 min
Reštartovať Hriešnici Séria 4, epizóda 3: Časť 3
Pridať do Môjho zoznamu: Hriešnici
Ohodnotiť tento seriál
Séria 4, epizóda 3: Časť 3
S4 E3: Časť 3
Fiona a Mikeov drogovo závislý brat Robbie spolu dobre vychádzajú.
Žánre: Komédia, Čierna komédia
Epizód
Séria 4
Predchádzajúce tituly
Pozerať znova: Séria 4, epizóda 1: Časť 1. 1 z 12. Hodnotené: 14, Sex, Jazyk, Omamné látky. Trvanie: 54 minút. Uvedené v roku 2014. Gallagherovci sa snažia ísť ďalej.
E1: Časť 1
54 min
2014
Gallagherovci sa snažia ísť ďalej.
Pozerať znova: Séria 4, epizóda 2: Časť 2. 2 z 12. Hodnotené: 14, Sex, Jazyk, Omamné látky. Trvanie: 48 minút. Uvedené v roku 2014. Keď Fiona odmietne darovať pečeň, musí sa Frank poobzerať inde.
E2: Časť 2
48 min
2014
Keď Fiona odmietne darovať pečeň, musí sa Frank poobzerať inde.
Pozerať Séria 4, epizóda 3: Časť 3. 3 z 12. Zostáva: 32 minút. Hodnotené: 14, Sex, Jazyk, Omamné látky. Trvanie: 53 minút. Uvedené v roku 2014. Fiona a Mikeov drogovo závislý brat Robbie spolu dobre vychádzajú.
E3: Časť 3
53 min
2014
Fiona a Mikeov drogovo závislý brat Robbie spolu dobre vychádzajú.
Ponuka pre epizódu 3: Časť 3
Séria 4, epizóda 4: Časť 4. 4 z 12. Hodnotené: 14, Sex, Jazyk, Omamné látky. Trvanie: 55 minút. Uvedené v roku 2014. Frank si vymyslí "nehodu", aby mohol zaplatiť za transplantáciu.
E4: Časť 4
55 min
2014
Frank si vymyslí "nehodu", aby mohol zaplatiť za transplantáciu.
Ponuka pre epizódu 4: Časť 4
Séria 4, epizóda 5: Časť 5. 5 z 12. Hodnotené: 14, Sex, Jazyk, Omamné látky. Trvanie: 50 minút. Uvedené v roku 2014. Robbie povie Mikeovi o svojom pomere s Fionou.
E5: Časť 5
50 min
2014
Robbie povie Mikeovi o svojom pomere s Fionou.
Ponuka pre epizódu 5: Časť 5
Séria 4, epizóda 6: Časť 6. 6 z 12. Hodnotené: 14, Sex, Jazyk, Omamné látky. Trvanie: 55 minút. Uvedené v roku 2014. Následky Liamovej nehody dostanú Fionu do väzenia.
E6: Časť 6
55 min
2014
Následky Liamovej nehody dostanú Fionu do väzenia.
Ponuka pre epizódu 6: Časť 6
Séria 4, epizóda 7: Časť 7. 7 z 12. Hodnotené: 14, Sex, Jazyk, Omamné látky. Trvanie: 49 minút. Uvedené v roku 2014. Lip robí všetko pre to, aby rodinu udržal pohromade.
E7: Časť 7
49 min
2014
Lip robí všetko pre to, aby rodinu udržal pohromade.
Séria 4, epizóda 8: Časť 8. 8 z 12. Hodnotené: 14, Sex, Jazyk, Omamné látky. Trvanie: 54 minút. Uvedené v roku 2014. Ian sa konečne vracia domov.
E8: Časť 8
54 min
2014
Ian sa konečne vracia domov.
Séria 4, epizóda 9: Časť 9. 9 z 12. Hodnotené: 14, Sex, Jazyk, Omamné látky. Trvanie: 52 minút. Uvedené v roku 2014. Carl sa zoznámi s problémovou dievčinou menom Bonnie.
E9: Časť 9
52 min
2014
Carl sa zoznámi s problémovou dievčinou menom Bonnie.
Séria 4, epizóda 10: Časť 10. 10 z 12. Hodnotené: 14, Sex, Jazyk, Omamné látky. Trvanie: 50 minút. Uvedené v roku 2014. Fiona poruší podmienku.
E10: Časť 10
50 min
2014
Fiona poruší podmienku.
Séria 4, epizóda 11: Časť 11. 11 z 12. Hodnotené: 14, Sex, Jazyk, Omamné látky. Trvanie: 48 minút. Uvedené v roku 2014. Fiona je poslaná do nápravného zariadenia.
E11: Časť 11
48 min
2014
Fiona je poslaná do nápravného zariadenia.
Séria 4, epizóda 12: Časť 12. 12 z 12. Hodnotené: 14, Sex, Jazyk, Omamné látky. Trvanie: 54 minút. Uvedené v roku 2014. Frank sa začína zotavovať a cíti sa ako predtým.
E12: Časť 12
54 min
2014
Frank sa začína zotavovať a cíti sa ako predtým.
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© 2026 HBO Europe s.r.o. Max and related elements are property of Home Box Office, Inc. All Rights Reserved.
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https://play.hbomax.com/show/06f986dd-dd6a-4596-9a0a-f473ef99c64b
(110) How to End American Power || Peter Zeihan - YouTube
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[SRD-6793] Les Mills activity types not pulling in [SRD-6793] Les Mills activity types not pulling in - Jira — Work...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
[SRD-6793] Les Mills activity types not pulling in - Jira
[SRD-6793] Les Mills activity types not pulling in - Jira
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[SRD-6787] Issue with reconnecting Zoho - Jira
[SRD-6787] Issue with reconnecting Zoho - Jira
JY-20698 handle failed field sync on playbook import activity types by LakyLak · Pull Request #11988 · jiminny/app
JY-20698 handle failed field sync on playbook import activity types by LakyLak · Pull Request #11988 · jiminny/app
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
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[JY-20676] Notify the user if a Panorama prompts is deleted but is used in AJ Report - Jira
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[JY-20500] Batch initial sync for Salesforce - Jira
[JY-20500] Batch initial sync for Salesforce - Jira
Feed — jiminny — Sentry
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JY-20701 | Reschedule HubSpot Sync Objects by yalokin-jiminny · Pull Request #11989 · jiminny/app
JY-20701 | Reschedule HubSpot Sync Objects by yalokin-jiminny · Pull Request #11989 · jiminny/app
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SRD-6793
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Les Mills activity types not pulling in- edit summary, edit
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Stoyan Tomov
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Description
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Hey team,
While testing something on Les Mills’s instance, I saw that we are not pulling in their activity types when a new Playbook is created.
I tried creating “
LMUS
CX
Playbook test” Playbook (ID 5515), but no activity types got pulled in.
When I tried reproducing the same on our Jiminny instance, the activity types appeared.
Can someone please check why we are not pulling the activity types for Les Mills?
Data Centre
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US
Steps to reproduce
Steps to reproduce
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Edit Steps to reproduce, edit
Create a new playbook in Les Mills's instance
Customer type
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Enterprise
Actual outcome
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No activity types are being imported upon new Playbook creation
Expected outcome
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Via have no access to FieldDefinition (needed to sync activity types).
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Les Mills activity types not pulling in
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Pulsar seems to be working fine, I created new playbooks and the fields were populated also on the one the client claims activity types were missing. For Les Mills t
Pulsar seems to be working fine, I created new playbooks and the fields were populated also on the one the client claims activity types were missing. For Les Mills t
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FYI, Pulsar Group (EU) is facing the same issue.
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
[SRD-6793] Les Mills activity types not pulling in - Jira
[SRD-6793] Les Mills activity types not pulling in - Jira
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[SRD-6787] Issue with reconnecting Zoho - Jira
[SRD-6787] Issue with reconnecting Zoho - Jira
JY-20698 handle failed field sync on playbook import activity types by LakyLak · Pull Request #11988 · jiminny/app
JY-20698 handle failed field sync on playbook import activity types by LakyLak · Pull Request #11988 · jiminny/app
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
[JY-20676] Notify the user if a Panorama prompts is deleted but is used in AJ Report - Jira
[JY-20676] Notify the user if a Panorama prompts is deleted but is used in AJ Report - Jira
Jiminny Mail
Jiminny Mail
[JY-20500] Batch initial sync for Salesforce - Jira
[JY-20500] Batch initial sync for Salesforce - Jira
Feed — jiminny — Sentry
Feed — jiminny — Sentry
Jiminny
Jiminny
JY-20701 | Reschedule HubSpot Sync Objects by yalokin-jiminny · Pull Request #11989 · jiminny/app
JY-20701 | Reschedule HubSpot Sync Objects by yalokin-jiminny · Pull Request #11989 · jiminny/app
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Bug - Change work type
SRD-6793
SRD-6793
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Les Mills activity types not pulling in- edit summary, edit
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Stoyan Tomov
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Hey team,
While testing something on Les Mills’s instance, I saw that we are not pulling in their activity types when a new Playbook is created.
I tried creating “
LMUS
CX
Playbook test” Playbook (ID 5515), but no activity types got pulled in.
When I tried reproducing the same on our Jiminny instance, the activity types appeared.
Can someone please check why we are not pulling the activity types for Les Mills?
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Create a new playbook in Les Mills's instance
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No activity types are being imported upon new Playbook creation
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The activity types to get imported
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S2
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None
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Via have no access to FieldDefinition (needed to sync activity types).
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Pulsar seems to be working fine, I created new playbooks and the fields were populated also on the one the client claims activity types were missing. For Les Mills t
Pulsar seems to be working fine, I created new playbooks and the fields were populated also on the one the client claims activity types were missing. For Les Mills t
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5 days ago
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FYI, Pulsar Group (EU) is facing the same issue.
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[SRD-6793] Les Mills activity types not pulling in [SRD-6793] Les Mills activity types not pulling in - Jira — Work...
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jiminny.atlassian.net/jira/servicedesk/projects/SR jiminny.atlassian.net/jira/servicedesk/projects/SRD/queues/custom/37/SRD-6793...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
[SRD-6793] Les Mills activity types not pulling in - Jira
[SRD-6793] Les Mills activity types not pulling in - Jira
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[SRD-6787] Issue with reconnecting Zoho - Jira
[SRD-6787] Issue with reconnecting Zoho - Jira
JY-20698 handle failed field sync on playbook import activity types by LakyLak · Pull Request #11988 · jiminny/app
JY-20698 handle failed field sync on playbook import activity types by LakyLak · Pull Request #11988 · jiminny/app
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
[JY-20676] Notify the user if a Panorama prompts is deleted but is used in AJ Report - Jira
[JY-20676] Notify the user if a Panorama prompts is deleted but is used in AJ Report - Jira
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Jiminny Mail
[JY-20500] Batch initial sync for Salesforce - Jira
[JY-20500] Batch initial sync for Salesforce - Jira
Feed — jiminny — Sentry
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JY-20701 | Reschedule HubSpot Sync Objects by yalokin-jiminny · Pull Request #11989 · jiminny/app
JY-20701 | Reschedule HubSpot Sync Objects by yalokin-jiminny · Pull Request #11989 · jiminny/app
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Bug - Change work type
SRD-6793
SRD-6793
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Les Mills activity types not pulling in- edit summary, edit
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Stoyan Tomov
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Description
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Hey team,
While testing something on Les Mills’s instance, I saw that we are not pulling in their activity types when a new Playbook is created.
I tried creating “
LMUS
CX
Playbook test” Playbook (ID 5515), but no activity types got pulled in.
When I tried reproducing the same on our Jiminny instance, the activity types appeared.
Can someone please check why we are not pulling the activity types for Les Mills?
Data Centre
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Edit Data Centre
US
Steps to reproduce
Steps to reproduce
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Edit Steps to reproduce, edit
Create a new playbook in Les Mills's instance
Customer type
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Enterprise
Actual outcome
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No activity types are being imported upon new Playbook creation
Expected outcome
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The activity types to get imported
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S2
Impact
Impact
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None
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Via have no access to FieldDefinition (needed to sync activity types).
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Pulsar seems to be working fine, I created new playbooks and the fields were populated also on the one the client claims activity types were missing. For Les Mills that is a different story. The team owner we use for sync has no permiss
Pulsar seems to be working fine, I created new playbooks and the fields were populated also on the one the client claims activity types were missing. For Les Mills that is a different story. The team owner we use for sync has no permiss
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FYI, Pulsar Group (EU) is facing the same issue.
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ago","depth":12,"bounds":{"left":0.85754657,"top":1.0,"width":0.024102394,"height":-0.048683167},"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXLink","text":"Configure","depth":12,"bounds":{"left":0.9070811,"top":1.0,"width":0.037400264,"height":-0.025937796},"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Configure","depth":14,"bounds":{"left":0.9183843,"top":1.0,"width":0.022107713,"height":-0.0315243},"help_text":"","role_description":"text","subrole":"AXUnknown"}]...
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-8869985227998508217
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7401935562640123116
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visual_change
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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
[SRD-6793] Les Mills activity types not pulling in - Jira
[SRD-6793] Les Mills activity types not pulling in - Jira
Close tab
[SRD-6787] Issue with reconnecting Zoho - Jira
[SRD-6787] Issue with reconnecting Zoho - Jira
JY-20698 handle failed field sync on playbook import activity types by LakyLak · Pull Request #11988 · jiminny/app
JY-20698 handle failed field sync on playbook import activity types by LakyLak · Pull Request #11988 · jiminny/app
Jiminny MCP Connector - Product - Confluence
Jiminny MCP Connector - Product - Confluence
[JY-20676] Notify the user if a Panorama prompts is deleted but is used in AJ Report - Jira
[JY-20676] Notify the user if a Panorama prompts is deleted but is used in AJ Report - Jira
Jiminny Mail
Jiminny Mail
[JY-20500] Batch initial sync for Salesforce - Jira
[JY-20500] Batch initial sync for Salesforce - Jira
Feed — jiminny — Sentry
Feed — jiminny — Sentry
Jiminny
Jiminny
JY-20701 | Reschedule HubSpot Sync Objects by yalokin-jiminny · Pull Request #11989 · jiminny/app
JY-20701 | Reschedule HubSpot Sync Objects by yalokin-jiminny · Pull Request #11989 · jiminny/app
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Pipelines - jiminny/app
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SRD-6793
SRD-6793
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Les Mills activity types not pulling in- edit summary, edit
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Stoyan Tomov
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Description
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Hey team,
While testing something on Les Mills’s instance, I saw that we are not pulling in their activity types when a new Playbook is created.
I tried creating “
LMUS
CX
Playbook test” Playbook (ID 5515), but no activity types got pulled in.
When I tried reproducing the same on our Jiminny instance, the activity types appeared.
Can someone please check why we are not pulling the activity types for Les Mills?
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US
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Create a new playbook in Les Mills's instance
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Enterprise
Actual outcome
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No activity types are being imported upon new Playbook creation
Expected outcome
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The activity types to get imported
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S2
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None
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Via have no access to FieldDefinition (needed to sync activity types).
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JY-20698 is not done
JY-20698
Les Mills activity types not pulling in
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Pulsar seems to be working fine, I created new playbooks and the fields were populated also on the one the client claims activity types were missing. For Les Mills that is a different story. The team owner we use for sync has no permiss
Pulsar seems to be working fine, I created new playbooks and the fields were populated also on the one the client claims activity types were missing. For Les Mills that is a different story. The team owner we use for sync has no permiss
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More information about Stoyan Tomov 5 days ago Internal note Copy link to comment
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Stoyan Tomov
5 days ago
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FYI, Pulsar Group (EU) is facing the same issue.
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Lukas Kovalik- edit Assignee
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Stoyan Tomov- edit Reporter
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Les Mills
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Christopher Wilton
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Created
5 days ago
Updated
13 hours ago
Configure
Configure...
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0
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2026-04-21T06:09:27.535489+00:00
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/Users/lukas/.screenpipe/data/data/2026-04-21/1776 /Users/lukas/.screenpipe/data/data/2026-04-21/1776751767535_m2.jpg...
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iTerm2
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screenpipe"
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True
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monitor_2
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Last login: Mon Apr 20 20:51:30 on ttys009
Poetry Last login: Mon Apr 20 20:51:30 on ttys009
Poetry could not find a pyproject.toml file in /Users/lukas or its parents
Poetry could not find a pyproject.toml file in /Users/lukas or its parents
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~ $ sp-start
detected hardware tier: Mid
warning: parakeet is not supported on this platform, using whisper-tiny instead
2026-04-21T09:09:26.267672Z INFO screenpipe_engine::auth_key: api auth: key resolved via secret store
checking permissions...
screen recording: ok
accessibility: ok
2026-04-21T09:09:26.330305Z INFO screenpipe_screen::monitor::macos_version: Detected macOS version: 14.6
2026-04-21T09:09:26.876072Z INFO screenpipe_engine::sleep_monitor: Starting macOS sleep/wake monitor
2026-04-21T09:09:26.877941Z INFO screenpipe_engine::sleep_monitor: Screen lock/unlock observers registered (CFNotificationCenter)
2026-04-21T09:09:26.878456Z INFO screenpipe_engine::sleep_monitor: Display reconfiguration watcher registered (CGDisplayRegisterReconfigurationCallback)
2026-04-21T09:09:26.911814Z INFO screenpipe_engine::permission_monitor: permission monitor started screen=true mic=true accessibility=true keychain=true
2026-04-21T09:09:26.911854Z INFO screenpipe: meeting detector enabled — independent of transcription mode
2026-04-21T09:09:26.912050Z INFO screenpipe: API server listening on [IP_ADDRESS]:3030 (localhost only)
2026-04-21T09:09:26.912073Z INFO screenpipe: API auth enabled — run `screenpipe auth token` to view your key
2026-04-21T09:09:26.912102Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction worker started (min_age=600s, poll=300s)
2026-04-21T09:09:26.912144Z INFO screenpipe_engine::vision_manager::manager: Starting VisionManager
2026-04-21T09:09:26.912110Z INFO screenpipe_engine::power::manager: power manager started (poll interval: 10s)
2026-04-21T09:09:26.921311Z INFO screenpipe_engine::power::manager: initial power profile: Performance (on_ac=true, battery=Some(100))
2026-04-21T09:09:26.927270Z INFO screenpipe_core::pipes: loaded pipe: day-recap
2026-04-21T09:09:26.927460Z INFO screenpipe_core::pipes: loaded pipe: standup-update
2026-04-21T09:09:26.928100Z INFO screenpipe_core::pipes: loaded pipe: ai-habits
2026-04-21T09:09:26.928278Z INFO screenpipe_core::pipes: loaded pipe: time-breakdown
2026-04-21T09:09:26.928424Z INFO screenpipe_core::pipes: loaded pipe: video-export
2026-04-21T09:09:26.929174Z INFO screenpipe_core::pipes: loaded pipe: meeting-summary
2026-04-21T09:09:26.929195Z INFO screenpipe_core::pipes: loaded 6 pipes from "/Users/lukas/.screenpipe/pipes"
_
__________________ ___ ____ ____ (_____ ___
/ ___/ ___/ ___/ _ \/ _ \/ __ \ / __ \/ / __ \/ _ \
(__ / /__/ / / __/ __/ / / / / /_/ / / /_/ / __/
/____/\___/_/ \___/\___/_/ /_/ / .___/_/ .___/\___/
/_/ /_/
power AI by everything you've seen, said or heard
open source | runs locally | developer friendly
┌────────────────────────┬────────────────────────────────────┐
│ setting │ value │
├────────────────────────┼────────────────────────────────────┤
│ audio chunk duration │ 30 seconds │
│ port │ 3030 │
│ audio disabled │ true │
│ vision disabled │ false │
│ pause on DRM content │ false │
2026-04-21T09:09:26.929953Z INFO screenpipe_core::pipes: pipe scheduler started (generation 2)
│ audio engine │ Parakeet │
│ vad engine │ Silero │
│ data directory │ /Users/lukas/.screenpipe │
│ debug mode │ false │
│ telemetry │ true │
│ use pii removal │ true │
│ use all monitors │ true │
│ ignored windows │ ["Boosteroid"] │
│ included windows │ [] │
│ cloud sync │ disabled │
│ auto-destruct pid │ 0 │
│ deepgram key │ not set │
│ api auth │ enabled │
2026-04-21T09:09:26.933254Z WARN screenpipe: pi agent install failed: bun not found — install from https://bun.sh
│ encrypt secrets │ disabled │
│ retention days │ 14 │
├────────────────────────┼────────────────────────────────────┤
│ languages │ │
│ │ all languages │
├────────────────────────┼────────────────────────────────────┤
│ monitors │ │
│ │ id: 1 │
│ │ id: 2 │
├────────────────────────┼────────────────────────────────────┤
│ audio devices │ │
│ │ disabled │
└────────────────────────┴────────────────────────────────────┘
you are using local processing. all your data stays on your computer.
warning: telemetry is enabled. only error-level data will be sent.
to disable, use the --disable-telemetry flag.
check latest changes here: https://github.com/screenpipe/screenpipe/releases
2026-04-21T09:09:26.944288Z INFO screenpipe: starting UI event capture
2026-04-21T09:09:26.957412Z INFO screenpipe_engine::ui_recorder: Starting UI event capture
2026-04-21T09:09:26.970637Z INFO screenpipe_engine::ui_recorder: UI recording session started: a22e844d-73cb-42be-b71f-3243cecbaf5c
2026-04-21T09:09:26.970744Z INFO screenpipe_engine::calendar_speaker_id: speaker identification: started (user_name=<not set>)
2026-04-21T09:09:26.970792Z INFO screenpipe_engine::hot_frame_cache: hot_frame_cache: warming from DB (2026-04-20 06:09:26.970791 UTC to 2026-04-21 06:09:26.970791 UTC)
2026-04-21T09:09:26.971462Z INFO screenpipe_engine::meeting_detector: meeting v2: detection loop started (base_interval=5s, profiles=12)
2026-04-21T09:09:26.978353Z INFO screenpipe_engine::server: Server listening on [IP_ADDRESS]:3030
2026-04-21T09:09:26.982750Z INFO screenpipe_connect::mdns: mdns: advertising screenpipe on port 3030
2026-04-21T09:09:26.999552Z INFO screenpipe_engine::vision_manager::manager: Starting vision recording for monitor 1 (1440x900)
2026-04-21T09:09:26.999658Z INFO screenpipe_engine::vision_manager::manager: Starting event-driven capture for monitor 1 (device: monitor_1)
2026-04-21T09:09:26.999699Z INFO screenpipe_engine::event_driven_capture: event-driven capture started for monitor 1 (device: monitor_1)
2026-04-21T09:09:27.033668Z INFO screenpipe_engine::vision_manager::manager: Starting vision recording for monitor 2 (3008x1253)
2026-04-21T09:09:27.033695Z INFO screenpipe_engine::vision_manager::manager: Starting event-driven capture for monitor 2 (device: monitor_2)
2026-04-21T09:09:27.033706Z INFO screenpipe_engine::vision_manager::manager: VisionManager started with 2/2 monitor(s)
2026-04-21T09:09:27.033708Z INFO screenpipe_engine::event_driven_capture: event-driven capture started for monitor 2 (device: monitor_2)
2026-04-21T09:09:27.033714Z INFO screenpipe_engine::vision_manager::monitor_watcher: Starting monitor watcher (event-driven via CGDisplayRegisterReconfigurationCallback, 60s backstop poll)
2026-04-21T09:09:27.685135Z INFO sck_rs::stream_manager: persistent SCK stream started for display 1 (1440x900, 2fps, 2 excluded)
2026-04-21T09:09:27.761411Z INFO sck_rs::stream_manager: persistent SCK stream started for display 2 (3008x1253, 2fps, 2 excluded)
DOCKER
Close Tab
APP (-zsh)
Close Tab
-zsh
Close Tab
✳ Build full day activity summary from Screenpipe (node)
Close Tab
screenpipe"
Close Tab
-zsh
Close Tab
⌥⌘1
screenpipe"...
|
[{"role":"AXTextArea","text [{"role":"AXTextArea","text":"Last login: Mon Apr 20 20:51:30 on ttys009\n\nPoetry could not find a pyproject.toml file in /Users/lukas or its parents\n\nPoetry could not find a pyproject.toml file in /Users/lukas or its parents\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~ $ sp-start\ndetected hardware tier: Mid\nwarning: parakeet is not supported on this platform, using whisper-tiny instead\n2026-04-21T09:09:26.267672Z INFO screenpipe_engine::auth_key: api auth: key resolved via secret store\nchecking permissions...\n screen recording: ok\n accessibility: ok\n2026-04-21T09:09:26.330305Z INFO screenpipe_screen::monitor::macos_version: Detected macOS version: 14.6\n2026-04-21T09:09:26.876072Z INFO screenpipe_engine::sleep_monitor: Starting macOS sleep/wake monitor\n2026-04-21T09:09:26.877941Z INFO screenpipe_engine::sleep_monitor: Screen lock/unlock observers registered (CFNotificationCenter)\n2026-04-21T09:09:26.878456Z INFO screenpipe_engine::sleep_monitor: Display reconfiguration watcher registered (CGDisplayRegisterReconfigurationCallback)\n2026-04-21T09:09:26.911814Z INFO screenpipe_engine::permission_monitor: permission monitor started screen=true mic=true accessibility=true keychain=true\n2026-04-21T09:09:26.911854Z INFO screenpipe: meeting detector enabled — independent of transcription mode\n2026-04-21T09:09:26.912050Z INFO screenpipe: API server listening on 127.0.0.1:3030 (localhost only)\n2026-04-21T09:09:26.912073Z INFO screenpipe: API auth enabled — run `screenpipe auth token` to view your key\n2026-04-21T09:09:26.912102Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction worker started (min_age=600s, poll=300s)\n2026-04-21T09:09:26.912144Z INFO screenpipe_engine::vision_manager::manager: Starting VisionManager\n2026-04-21T09:09:26.912110Z INFO screenpipe_engine::power::manager: power manager started (poll interval: 10s)\n2026-04-21T09:09:26.921311Z INFO screenpipe_engine::power::manager: initial power profile: Performance (on_ac=true, battery=Some(100))\n2026-04-21T09:09:26.927270Z INFO screenpipe_core::pipes: loaded pipe: day-recap\n2026-04-21T09:09:26.927460Z INFO screenpipe_core::pipes: loaded pipe: standup-update\n2026-04-21T09:09:26.928100Z INFO screenpipe_core::pipes: loaded pipe: ai-habits\n2026-04-21T09:09:26.928278Z INFO screenpipe_core::pipes: loaded pipe: time-breakdown\n2026-04-21T09:09:26.928424Z INFO screenpipe_core::pipes: loaded pipe: video-export\n2026-04-21T09:09:26.929174Z INFO screenpipe_core::pipes: loaded pipe: meeting-summary\n2026-04-21T09:09:26.929195Z INFO screenpipe_core::pipes: loaded 6 pipes from \"/Users/lukas/.screenpipe/pipes\"\n\n\n\n _ \n __________________ ___ ____ ____ (_____ ___ \n / ___/ ___/ ___/ _ \\/ _ \\/ __ \\ / __ \\/ / __ \\/ _ \\\n (__ / /__/ / / __/ __/ / / / / /_/ / / /_/ / __/\n/____/\\___/_/ \\___/\\___/_/ /_/ / .___/_/ .___/\\___/ \n /_/ /_/ \n\n\n\npower AI by everything you've seen, said or heard\nopen source | runs locally | developer friendly\n\n\n┌────────────────────────┬────────────────────────────────────┐\n│ setting │ value │\n├────────────────────────┼────────────────────────────────────┤\n│ audio chunk duration │ 30 seconds │\n│ port │ 3030 │\n│ audio disabled │ true │\n│ vision disabled │ false │\n│ pause on DRM content │ false │\n2026-04-21T09:09:26.929953Z INFO screenpipe_core::pipes: pipe scheduler started (generation 2)\n│ audio engine │ Parakeet │\n│ vad engine │ Silero │\n│ data directory │ /Users/lukas/.screenpipe │\n│ debug mode │ false │\n│ telemetry │ true │\n│ use pii removal │ true │\n│ use all monitors │ true │\n│ ignored windows │ [\"Boosteroid\"] │\n│ included windows │ [] │\n│ cloud sync │ disabled │\n│ auto-destruct pid │ 0 │\n│ deepgram key │ not set │\n│ api auth │ enabled │\n2026-04-21T09:09:26.933254Z WARN screenpipe: pi agent install failed: bun not found — install from https://bun.sh\n│ encrypt secrets │ disabled │\n│ retention days │ 14 │\n├────────────────────────┼────────────────────────────────────┤\n│ languages │ │\n│ │ all languages │\n├────────────────────────┼────────────────────────────────────┤\n│ monitors │ │\n│ │ id: 1 │\n│ │ id: 2 │\n├────────────────────────┼────────────────────────────────────┤\n│ audio devices │ │\n│ │ disabled │\n└────────────────────────┴────────────────────────────────────┘\nyou are using local processing. all your data stays on your computer.\n\nwarning: telemetry is enabled. only error-level data will be sent.\nto disable, use the --disable-telemetry flag.\n\ncheck latest changes here: https://github.com/screenpipe/screenpipe/releases\n2026-04-21T09:09:26.944288Z INFO screenpipe: starting UI event capture\n2026-04-21T09:09:26.957412Z INFO screenpipe_engine::ui_recorder: Starting UI event capture\n2026-04-21T09:09:26.970637Z INFO screenpipe_engine::ui_recorder: UI recording session started: a22e844d-73cb-42be-b71f-3243cecbaf5c\n2026-04-21T09:09:26.970744Z INFO screenpipe_engine::calendar_speaker_id: speaker identification: started (user_name=<not set>)\n2026-04-21T09:09:26.970792Z INFO screenpipe_engine::hot_frame_cache: hot_frame_cache: warming from DB (2026-04-20 06:09:26.970791 UTC to 2026-04-21 06:09:26.970791 UTC)\n2026-04-21T09:09:26.971462Z INFO screenpipe_engine::meeting_detector: meeting v2: detection loop started (base_interval=5s, profiles=12)\n2026-04-21T09:09:26.978353Z INFO screenpipe_engine::server: Server listening on 127.0.0.1:3030\n2026-04-21T09:09:26.982750Z INFO screenpipe_connect::mdns: mdns: advertising screenpipe on port 3030\n2026-04-21T09:09:26.999552Z INFO screenpipe_engine::vision_manager::manager: Starting vision recording for monitor 1 (1440x900)\n2026-04-21T09:09:26.999658Z INFO screenpipe_engine::vision_manager::manager: Starting event-driven capture for monitor 1 (device: monitor_1)\n2026-04-21T09:09:26.999699Z INFO screenpipe_engine::event_driven_capture: event-driven capture started for monitor 1 (device: monitor_1)\n2026-04-21T09:09:27.033668Z INFO screenpipe_engine::vision_manager::manager: Starting vision recording for monitor 2 (3008x1253)\n2026-04-21T09:09:27.033695Z INFO screenpipe_engine::vision_manager::manager: Starting event-driven capture for monitor 2 (device: monitor_2)\n2026-04-21T09:09:27.033706Z INFO screenpipe_engine::vision_manager::manager: VisionManager started with 2/2 monitor(s)\n2026-04-21T09:09:27.033708Z INFO screenpipe_engine::event_driven_capture: event-driven capture started for monitor 2 (device: monitor_2)\n2026-04-21T09:09:27.033714Z INFO screenpipe_engine::vision_manager::monitor_watcher: Starting monitor watcher (event-driven via CGDisplayRegisterReconfigurationCallback, 60s backstop poll)\n2026-04-21T09:09:27.685135Z INFO sck_rs::stream_manager: persistent SCK stream started for display 1 (1440x900, 2fps, 2 excluded)\n2026-04-21T09:09:27.761411Z INFO sck_rs::stream_manager: persistent SCK stream started for display 2 (3008x1253, 2fps, 2 excluded)","depth":4,"bounds":{"left":0.27027926,"top":0.14365523,"width":0.4800532,"height":0.85634476},"value":"Last login: Mon Apr 20 20:51:30 on ttys009\n\nPoetry could not find a pyproject.toml file in /Users/lukas or its parents\n\nPoetry could not find a pyproject.toml file in /Users/lukas or its parents\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~ $ sp-start\ndetected hardware tier: Mid\nwarning: parakeet is not supported on this platform, using whisper-tiny instead\n2026-04-21T09:09:26.267672Z INFO screenpipe_engine::auth_key: api auth: key resolved via secret store\nchecking permissions...\n screen recording: ok\n accessibility: ok\n2026-04-21T09:09:26.330305Z INFO screenpipe_screen::monitor::macos_version: Detected macOS version: 14.6\n2026-04-21T09:09:26.876072Z INFO screenpipe_engine::sleep_monitor: Starting macOS sleep/wake monitor\n2026-04-21T09:09:26.877941Z INFO screenpipe_engine::sleep_monitor: Screen lock/unlock observers registered (CFNotificationCenter)\n2026-04-21T09:09:26.878456Z INFO screenpipe_engine::sleep_monitor: Display reconfiguration watcher registered (CGDisplayRegisterReconfigurationCallback)\n2026-04-21T09:09:26.911814Z INFO screenpipe_engine::permission_monitor: permission monitor started screen=true mic=true accessibility=true keychain=true\n2026-04-21T09:09:26.911854Z INFO screenpipe: meeting detector enabled — independent of transcription mode\n2026-04-21T09:09:26.912050Z INFO screenpipe: API server listening on 127.0.0.1:3030 (localhost only)\n2026-04-21T09:09:26.912073Z INFO screenpipe: API auth enabled — run `screenpipe auth token` to view your key\n2026-04-21T09:09:26.912102Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction worker started (min_age=600s, poll=300s)\n2026-04-21T09:09:26.912144Z INFO screenpipe_engine::vision_manager::manager: Starting VisionManager\n2026-04-21T09:09:26.912110Z INFO screenpipe_engine::power::manager: power manager started (poll interval: 10s)\n2026-04-21T09:09:26.921311Z INFO screenpipe_engine::power::manager: initial power profile: Performance (on_ac=true, battery=Some(100))\n2026-04-21T09:09:26.927270Z INFO screenpipe_core::pipes: loaded pipe: day-recap\n2026-04-21T09:09:26.927460Z INFO screenpipe_core::pipes: loaded pipe: standup-update\n2026-04-21T09:09:26.928100Z INFO screenpipe_core::pipes: loaded pipe: ai-habits\n2026-04-21T09:09:26.928278Z INFO screenpipe_core::pipes: loaded pipe: time-breakdown\n2026-04-21T09:09:26.928424Z INFO screenpipe_core::pipes: loaded pipe: video-export\n2026-04-21T09:09:26.929174Z INFO screenpipe_core::pipes: loaded pipe: meeting-summary\n2026-04-21T09:09:26.929195Z INFO screenpipe_core::pipes: loaded 6 pipes from \"/Users/lukas/.screenpipe/pipes\"\n\n\n\n _ \n __________________ ___ ____ ____ (_____ ___ \n / ___/ ___/ ___/ _ \\/ _ \\/ __ \\ / __ \\/ / __ \\/ _ \\\n (__ / /__/ / / __/ __/ / / / / /_/ / / /_/ / __/\n/____/\\___/_/ \\___/\\___/_/ /_/ / .___/_/ .___/\\___/ \n /_/ /_/ \n\n\n\npower AI by everything you've seen, said or heard\nopen source | runs locally | developer friendly\n\n\n┌────────────────────────┬────────────────────────────────────┐\n│ setting │ value │\n├────────────────────────┼────────────────────────────────────┤\n│ audio chunk duration │ 30 seconds │\n│ port │ 3030 │\n│ audio disabled │ true │\n│ vision disabled │ false │\n│ pause on DRM content │ false │\n2026-04-21T09:09:26.929953Z INFO screenpipe_core::pipes: pipe scheduler started (generation 2)\n│ audio engine │ Parakeet │\n│ vad engine │ Silero │\n│ data directory │ /Users/lukas/.screenpipe │\n│ debug mode │ false │\n│ telemetry │ true │\n│ use pii removal │ true │\n│ use all monitors │ true │\n│ ignored windows │ [\"Boosteroid\"] │\n│ included windows │ [] │\n│ cloud sync │ disabled │\n│ auto-destruct pid │ 0 │\n│ deepgram key │ not set │\n│ api auth │ enabled │\n2026-04-21T09:09:26.933254Z WARN screenpipe: pi agent install failed: bun not found — install from https://bun.sh\n│ encrypt secrets │ disabled │\n│ retention days │ 14 │\n├────────────────────────┼────────────────────────────────────┤\n│ languages │ │\n│ │ all languages │\n├────────────────────────┼────────────────────────────────────┤\n│ monitors │ │\n│ │ id: 1 │\n│ │ id: 2 │\n├────────────────────────┼────────────────────────────────────┤\n│ audio devices │ │\n│ │ disabled │\n└────────────────────────┴────────────────────────────────────┘\nyou are using local processing. all your data stays on your computer.\n\nwarning: telemetry is enabled. only error-level data will be sent.\nto disable, use the --disable-telemetry flag.\n\ncheck latest changes here: https://github.com/screenpipe/screenpipe/releases\n2026-04-21T09:09:26.944288Z INFO screenpipe: starting UI event capture\n2026-04-21T09:09:26.957412Z INFO screenpipe_engine::ui_recorder: Starting UI event capture\n2026-04-21T09:09:26.970637Z INFO screenpipe_engine::ui_recorder: UI recording session started: a22e844d-73cb-42be-b71f-3243cecbaf5c\n2026-04-21T09:09:26.970744Z INFO screenpipe_engine::calendar_speaker_id: speaker identification: started (user_name=<not set>)\n2026-04-21T09:09:26.970792Z INFO screenpipe_engine::hot_frame_cache: hot_frame_cache: warming from DB (2026-04-20 06:09:26.970791 UTC to 2026-04-21 06:09:26.970791 UTC)\n2026-04-21T09:09:26.971462Z INFO screenpipe_engine::meeting_detector: meeting v2: detection loop started (base_interval=5s, profiles=12)\n2026-04-21T09:09:26.978353Z INFO screenpipe_engine::server: Server listening on 127.0.0.1:3030\n2026-04-21T09:09:26.982750Z INFO screenpipe_connect::mdns: mdns: advertising screenpipe on port 3030\n2026-04-21T09:09:26.999552Z INFO screenpipe_engine::vision_manager::manager: Starting vision recording for monitor 1 (1440x900)\n2026-04-21T09:09:26.999658Z INFO screenpipe_engine::vision_manager::manager: Starting event-driven capture for monitor 1 (device: monitor_1)\n2026-04-21T09:09:26.999699Z INFO screenpipe_engine::event_driven_capture: event-driven capture started for monitor 1 (device: monitor_1)\n2026-04-21T09:09:27.033668Z INFO screenpipe_engine::vision_manager::manager: Starting vision recording for monitor 2 (3008x1253)\n2026-04-21T09:09:27.033695Z INFO screenpipe_engine::vision_manager::manager: Starting event-driven capture for monitor 2 (device: monitor_2)\n2026-04-21T09:09:27.033706Z INFO screenpipe_engine::vision_manager::manager: VisionManager started with 2/2 monitor(s)\n2026-04-21T09:09:27.033708Z INFO screenpipe_engine::event_driven_capture: event-driven capture started for monitor 2 (device: monitor_2)\n2026-04-21T09:09:27.033714Z INFO screenpipe_engine::vision_manager::monitor_watcher: Starting monitor watcher (event-driven via CGDisplayRegisterReconfigurationCallback, 60s backstop poll)\n2026-04-21T09:09:27.685135Z INFO sck_rs::stream_manager: persistent SCK stream started for display 1 (1440x900, 2fps, 2 excluded)\n2026-04-21T09:09:27.761411Z INFO sck_rs::stream_manager: persistent SCK stream started for display 2 (3008x1253, 2fps, 2 excluded)","is_focused":true},{"role":"AXRadioButton","text":"DOCKER","depth":2,"bounds":{"left":0.27027926,"top":1.0,"width":0.0787899,"height":-0.042298436},"role_description":"radio button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Close Tab","depth":3,"bounds":{"left":0.27227393,"top":1.0,"width":0.005319149,"height":-0.04549086},"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXRadioButton","text":"APP (-zsh)","depth":2,"bounds":{"left":0.34906915,"top":1.0,"width":0.0787899,"height":-0.042298436},"role_description":"radio button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Close Tab","depth":3,"bounds":{"left":0.35106382,"top":1.0,"width":0.005319149,"height":-0.04549086},"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXRadioButton","text":"-zsh","depth":2,"bounds":{"left":0.42785904,"top":1.0,"width":0.07862367,"height":-0.042298436},"role_description":"radio button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Close Tab","depth":3,"bounds":{"left":0.42985374,"top":1.0,"width":0.005319149,"height":-0.04549086},"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXRadioButton","text":"✳ Build full day activity summary from Screenpipe (node)","depth":2,"bounds":{"left":0.5064827,"top":1.0,"width":0.07862367,"height":-0.042298436},"role_description":"radio button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Close Tab","depth":3,"bounds":{"left":0.5084774,"top":1.0,"width":0.005319149,"height":-0.04549086},"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXRadioButton","text":"screenpipe\"","depth":2,"bounds":{"left":0.5851064,"top":1.0,"width":0.07862367,"height":-0.042298436},"role_description":"radio button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Close Tab","depth":3,"bounds":{"left":0.58710104,"top":1.0,"width":0.005319149,"height":-0.04549086},"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXRadioButton","text":"-zsh","depth":2,"bounds":{"left":0.66373,"top":1.0,"width":0.07862367,"height":-0.042298436},"role_description":"radio button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Close Tab","depth":3,"bounds":{"left":0.66572475,"top":1.0,"width":0.005319149,"height":-0.04549086},"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"⌥⌘1","depth":1,"bounds":{"left":0.7287234,"top":1.0,"width":0.01861702,"height":-0.023144484},"automation_id":"_NS:8","role_description":"text"},{"role":"AXStaticText","text":"screenpipe\"","depth":1,"bounds":{"left":0.49634308,"top":1.0,"width":0.027925532,"height":-0.02394259},"role_description":"text"}]...
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-3400411416856803862
|
7401480605826544929
|
manual
|
accessibility
|
NULL
|
Last login: Mon Apr 20 20:51:30 on ttys009
Poetry Last login: Mon Apr 20 20:51:30 on ttys009
Poetry could not find a pyproject.toml file in /Users/lukas or its parents
Poetry could not find a pyproject.toml file in /Users/lukas or its parents
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~ $ sp-start
detected hardware tier: Mid
warning: parakeet is not supported on this platform, using whisper-tiny instead
2026-04-21T09:09:26.267672Z INFO screenpipe_engine::auth_key: api auth: key resolved via secret store
checking permissions...
screen recording: ok
accessibility: ok
2026-04-21T09:09:26.330305Z INFO screenpipe_screen::monitor::macos_version: Detected macOS version: 14.6
2026-04-21T09:09:26.876072Z INFO screenpipe_engine::sleep_monitor: Starting macOS sleep/wake monitor
2026-04-21T09:09:26.877941Z INFO screenpipe_engine::sleep_monitor: Screen lock/unlock observers registered (CFNotificationCenter)
2026-04-21T09:09:26.878456Z INFO screenpipe_engine::sleep_monitor: Display reconfiguration watcher registered (CGDisplayRegisterReconfigurationCallback)
2026-04-21T09:09:26.911814Z INFO screenpipe_engine::permission_monitor: permission monitor started screen=true mic=true accessibility=true keychain=true
2026-04-21T09:09:26.911854Z INFO screenpipe: meeting detector enabled — independent of transcription mode
2026-04-21T09:09:26.912050Z INFO screenpipe: API server listening on [IP_ADDRESS]:3030 (localhost only)
2026-04-21T09:09:26.912073Z INFO screenpipe: API auth enabled — run `screenpipe auth token` to view your key
2026-04-21T09:09:26.912102Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction worker started (min_age=600s, poll=300s)
2026-04-21T09:09:26.912144Z INFO screenpipe_engine::vision_manager::manager: Starting VisionManager
2026-04-21T09:09:26.912110Z INFO screenpipe_engine::power::manager: power manager started (poll interval: 10s)
2026-04-21T09:09:26.921311Z INFO screenpipe_engine::power::manager: initial power profile: Performance (on_ac=true, battery=Some(100))
2026-04-21T09:09:26.927270Z INFO screenpipe_core::pipes: loaded pipe: day-recap
2026-04-21T09:09:26.927460Z INFO screenpipe_core::pipes: loaded pipe: standup-update
2026-04-21T09:09:26.928100Z INFO screenpipe_core::pipes: loaded pipe: ai-habits
2026-04-21T09:09:26.928278Z INFO screenpipe_core::pipes: loaded pipe: time-breakdown
2026-04-21T09:09:26.928424Z INFO screenpipe_core::pipes: loaded pipe: video-export
2026-04-21T09:09:26.929174Z INFO screenpipe_core::pipes: loaded pipe: meeting-summary
2026-04-21T09:09:26.929195Z INFO screenpipe_core::pipes: loaded 6 pipes from "/Users/lukas/.screenpipe/pipes"
_
__________________ ___ ____ ____ (_____ ___
/ ___/ ___/ ___/ _ \/ _ \/ __ \ / __ \/ / __ \/ _ \
(__ / /__/ / / __/ __/ / / / / /_/ / / /_/ / __/
/____/\___/_/ \___/\___/_/ /_/ / .___/_/ .___/\___/
/_/ /_/
power AI by everything you've seen, said or heard
open source | runs locally | developer friendly
┌────────────────────────┬────────────────────────────────────┐
│ setting │ value │
├────────────────────────┼────────────────────────────────────┤
│ audio chunk duration │ 30 seconds │
│ port │ 3030 │
│ audio disabled │ true │
│ vision disabled │ false │
│ pause on DRM content │ false │
2026-04-21T09:09:26.929953Z INFO screenpipe_core::pipes: pipe scheduler started (generation 2)
│ audio engine │ Parakeet │
│ vad engine │ Silero │
│ data directory │ /Users/lukas/.screenpipe │
│ debug mode │ false │
│ telemetry │ true │
│ use pii removal │ true │
│ use all monitors │ true │
│ ignored windows │ ["Boosteroid"] │
│ included windows │ [] │
│ cloud sync │ disabled │
│ auto-destruct pid │ 0 │
│ deepgram key │ not set │
│ api auth │ enabled │
2026-04-21T09:09:26.933254Z WARN screenpipe: pi agent install failed: bun not found — install from https://bun.sh
│ encrypt secrets │ disabled │
│ retention days │ 14 │
├────────────────────────┼────────────────────────────────────┤
│ languages │ │
│ │ all languages │
├────────────────────────┼────────────────────────────────────┤
│ monitors │ │
│ │ id: 1 │
│ │ id: 2 │
├────────────────────────┼────────────────────────────────────┤
│ audio devices │ │
│ │ disabled │
└────────────────────────┴────────────────────────────────────┘
you are using local processing. all your data stays on your computer.
warning: telemetry is enabled. only error-level data will be sent.
to disable, use the --disable-telemetry flag.
check latest changes here: https://github.com/screenpipe/screenpipe/releases
2026-04-21T09:09:26.944288Z INFO screenpipe: starting UI event capture
2026-04-21T09:09:26.957412Z INFO screenpipe_engine::ui_recorder: Starting UI event capture
2026-04-21T09:09:26.970637Z INFO screenpipe_engine::ui_recorder: UI recording session started: a22e844d-73cb-42be-b71f-3243cecbaf5c
2026-04-21T09:09:26.970744Z INFO screenpipe_engine::calendar_speaker_id: speaker identification: started (user_name=<not set>)
2026-04-21T09:09:26.970792Z INFO screenpipe_engine::hot_frame_cache: hot_frame_cache: warming from DB (2026-04-20 06:09:26.970791 UTC to 2026-04-21 06:09:26.970791 UTC)
2026-04-21T09:09:26.971462Z INFO screenpipe_engine::meeting_detector: meeting v2: detection loop started (base_interval=5s, profiles=12)
2026-04-21T09:09:26.978353Z INFO screenpipe_engine::server: Server listening on [IP_ADDRESS]:3030
2026-04-21T09:09:26.982750Z INFO screenpipe_connect::mdns: mdns: advertising screenpipe on port 3030
2026-04-21T09:09:26.999552Z INFO screenpipe_engine::vision_manager::manager: Starting vision recording for monitor 1 (1440x900)
2026-04-21T09:09:26.999658Z INFO screenpipe_engine::vision_manager::manager: Starting event-driven capture for monitor 1 (device: monitor_1)
2026-04-21T09:09:26.999699Z INFO screenpipe_engine::event_driven_capture: event-driven capture started for monitor 1 (device: monitor_1)
2026-04-21T09:09:27.033668Z INFO screenpipe_engine::vision_manager::manager: Starting vision recording for monitor 2 (3008x1253)
2026-04-21T09:09:27.033695Z INFO screenpipe_engine::vision_manager::manager: Starting event-driven capture for monitor 2 (device: monitor_2)
2026-04-21T09:09:27.033706Z INFO screenpipe_engine::vision_manager::manager: VisionManager started with 2/2 monitor(s)
2026-04-21T09:09:27.033708Z INFO screenpipe_engine::event_driven_capture: event-driven capture started for monitor 2 (device: monitor_2)
2026-04-21T09:09:27.033714Z INFO screenpipe_engine::vision_manager::monitor_watcher: Starting monitor watcher (event-driven via CGDisplayRegisterReconfigurationCallback, 60s backstop poll)
2026-04-21T09:09:27.685135Z INFO sck_rs::stream_manager: persistent SCK stream started for display 1 (1440x900, 2fps, 2 excluded)
2026-04-21T09:09:27.761411Z INFO sck_rs::stream_manager: persistent SCK stream started for display 2 (3008x1253, 2fps, 2 excluded)
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[JY-20717] Exclude recipients from API response for non-creator users in automated-reports request - Jira
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[SRD-6793] Les Mills activity types not pulling in [SRD-6793] Les Mills activity types not pulling in - Jira — Work...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
[SRD-6793] Les Mills activity types not pulling in - Jira
[SRD-6793] Les Mills activity types not pulling in - Jira
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[SRD-6787] Issue with reconnecting Zoho - Jira
[SRD-6787] Issue with reconnecting Zoho - Jira
JY-20698 handle failed field sync on playbook import activity types by LakyLak · Pull Request #11988 · jiminny/app
JY-20698 handle failed field sync on playbook import activity types by LakyLak · Pull Request #11988 · jiminny/app
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[JY-20676] Notify the user if a Panorama prompts is deleted but is used in AJ Report - Jira
[JY-20676] Notify the user if a Panorama prompts is deleted but is used in AJ Report - Jira
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[JY-20500] Batch initial sync for Salesforce - Jira
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JY-20701 | Reschedule HubSpot Sync Objects by yalokin-jiminny · Pull Request #11989 · jiminny/app
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SRD-6793
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Les Mills activity types not pulling in- edit summary, edit
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Stoyan Tomov
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Hey team,
While testing something on Les Mills’s instance, I saw that we are not pulling in their activity types when a new Playbook is created.
I tried creating “
LMUS
CX
Playbook test” Playbook (ID 5515), but no activity types got pulled in.
When I tried reproducing the same on our Jiminny instance, the activity types appeared.
Can someone please check why we are not pulling the activity types for Les Mills?
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Create a new playbook in Les Mills's instance
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No activity types are being imported upon new Playbook creation
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The activity types to get imported
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S2
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Via have no access to FieldDefinition (needed to sync activity types).
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Pulsar seems to be working fine, I created new playbooks and the fields were populated also on the one the client claims activity types were missing. For Les Mills that is a different story. The team owner we use for sync has no permission for FieldDefinition via Tooling API. There is a fallback logic that populates activity types from our database if the API request fails. However the changed access permissions will cause other problems for us. The client also doesn’t seem to have access to Organization. And therefore we can’t sync team metadata successfully.[2026-04-21 06:39:44] production.ERROR: [Salesforce] Request exception [404] The requested resource does not exist {"url":"https://lesmills.my.salesforce.com/services/data/v50.0/sobjects/Organization/00D90000000fUszEAE?fields=InstanceName,OrganizationType,IsSandbox", ... }
Pulsar seems to be working fine, I created new playbooks and the fields were populated also on the one the client claims activity types were missing. For Les Mills that is a different story. The team owner we use for sync has no permission for
FieldDefinition
via Tooling API.
There is a fallback logic that populates activity types from our database if the API request fails. However the changed access permissions will cause other problems for us. The client also doesn’t seem to have access to Organization. And therefore we can’t sync team metadata successfully.
[2026-04-21 06:39:44] production.ERROR: [Salesforce] Request exception [404] The requested resource does not exist {"url":"https://lesmills.my.salesforce.com/services/data/v50.0/sobjects/Organization/00D90000000fUszEAE?fields=InstanceName,OrganizationType,IsSandbox", ... }
[2026-04-21 06:39:44] production.ERROR: [Salesforce] Request exception [404] The requested resource does not exist
{
"url":"https://lesmills.my.salesforce.com/services/data/v50.0/sobjects/Organization/00D90000000fUszEAE?fields=InstanceName,OrganizationType,IsSandbox"
, ...
}
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More information about Stoyan Tomov 5 days ago Internal note Copy link to comment
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Stoyan Tomov
5 days ago
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FYI, Pulsar Group (EU) is facing the same issue.
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For Les Mills that is a different story. The team owner we use for sync has no permission for FieldDefinition via Tooling API. There is a fallback logic that populates activity types from our database if the API request fails. However the changed access permissions will cause other problems for us. The client also doesn’t seem to have access to Organization. And therefore we can’t sync team metadata successfully.[2026-04-21 06:39:44] production.ERROR: [Salesforce] Request exception [404] The requested resource does not exist {\"url\":\"https://lesmills.my.salesforce.com/services/data/v50.0/sobjects/Organization/00D90000000fUszEAE?fields=InstanceName,OrganizationType,IsSandbox\", ... }","depth":15,"bounds":{"left":0.3464096,"top":0.61372703,"width":0.4635971,"height":0.15642458},"value":"Pulsar seems to be working fine, I created new playbooks and the fields were populated also on the one the client claims activity types were missing. For Les Mills that is a different story. The team owner we use for sync has no permission for FieldDefinition via Tooling API. There is a fallback logic that populates activity types from our database if the API request fails. However the changed access permissions will cause other problems for us. The client also doesn’t seem to have access to Organization. And therefore we can’t sync team metadata successfully.[2026-04-21 06:39:44] production.ERROR: [Salesforce] Request exception [404] The requested resource does not exist {\"url\":\"https://lesmills.my.salesforce.com/services/data/v50.0/sobjects/Organization/00D90000000fUszEAE?fields=InstanceName,OrganizationType,IsSandbox\", ... }","help_text":"","role_description":"text entry area","subrole":"AXUnknown","is_enabled":true,"is_focused":true,"is_selected":false},{"role":"AXStaticText","text":"Pulsar seems to be working fine, I created new playbooks and the fields were populated also on the one the client claims activity types were missing. 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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
[SRD-6793] Les Mills activity types not pulling in - Jira
[SRD-6793] Les Mills activity types not pulling in - Jira
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[SRD-6787] Issue with reconnecting Zoho - Jira
[SRD-6787] Issue with reconnecting Zoho - Jira
JY-20698 handle failed field sync on playbook import activity types by LakyLak · Pull Request #11988 · jiminny/app
JY-20698 handle failed field sync on playbook import activity types by LakyLak · Pull Request #11988 · jiminny/app
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Jiminny MCP Connector - Product - Confluence
[JY-20676] Notify the user if a Panorama prompts is deleted but is used in AJ Report - Jira
[JY-20676] Notify the user if a Panorama prompts is deleted but is used in AJ Report - Jira
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Feed — jiminny — Sentry
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JY-20701 | Reschedule HubSpot Sync Objects by yalokin-jiminny · Pull Request #11989 · jiminny/app
JY-20701 | Reschedule HubSpot Sync Objects by yalokin-jiminny · Pull Request #11989 · jiminny/app
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SRD-6793
SRD-6793
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Les Mills activity types not pulling in- edit summary, edit
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Stoyan Tomov
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Hey team,
While testing something on Les Mills’s instance, I saw that we are not pulling in their activity types when a new Playbook is created.
I tried creating “
LMUS
CX
Playbook test” Playbook (ID 5515), but no activity types got pulled in.
When I tried reproducing the same on our Jiminny instance, the activity types appeared.
Can someone please check why we are not pulling the activity types for Les Mills?
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Create a new playbook in Les Mills's instance
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No activity types are being imported upon new Playbook creation
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The activity types to get imported
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S2
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None
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Via have no access to FieldDefinition (needed to sync activity types).
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JY-20698 is not done
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Les Mills activity types not pulling in
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Pulsar seems to be working fine, I created new playbooks and the fields were populated also on the one the client claims activity types were missing. For Les Mills that is a different story. The team owner we use for sync has no permission for FieldDefinition via Tooling API. There is a fallback logic that populates activity types from our database if the API request fails. However the changed access permissions will cause other problems for us. The client also doesn’t seem to have access to Organization. And therefore we can’t sync team metadata successfully.[2026-04-21 06:39:44] production.ERROR: [Salesforce] Request exception [404] The requested resource does not exist {"url":"https://lesmills.my.salesforce.com/services/data/v50.0/sobjects/Organization/00D90000000fUszEAE?fields=InstanceName,OrganizationType,IsSandbox", ... }
Pulsar seems to be working fine, I created new playbooks and the fields were populated also on the one the client claims activity types were missing. For Les Mills that is a different story. The team owner we use for sync has no permission for
FieldDefinition
via Tooling API.
There is a fallback logic that populates activity types from our database if the API request fails. However the changed access permissions will cause other problems for us. The client also doesn’t seem to have access to Organization. And therefore we can’t sync team metadata successfully.
[2026-04-21 06:39:44] production.ERROR: [Salesforce] Request exception [404] The requested resource does not exist {"url":"https://lesmills.my.salesforce.com/services/data/v50.0/sobjects/Organization/00D90000000fUszEAE?fields=InstanceName,OrganizationType,IsSandbox", ... }
[2026-04-21 06:39:44] production.ERROR: [Salesforce] Request exception [404] The requested resource does not exist
{
"url":"https://lesmills.my.salesforce.com/services/data/v50.0/sobjects/Organization/00D90000000fUszEAE?fields=InstanceName,OrganizationType,IsSandbox"
, ...
}
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More information about Stoyan Tomov 5 days ago Internal note Copy link to comment
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Stoyan Tomov
5 days ago
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FYI, Pulsar Group (EU) is facing the same issue.
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Lukas Kovalik- edit Assignee
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Stoyan Tomov- edit Reporter
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Les Mills
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