Utilizes OpenAI's embedding and language models to enable semantic search, extract categorized themes, and summarize key insights from meeting transcripts.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@granola-mcpFind all user pain points and feature requests from my recent meetings"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
granola-mcp
MCP server for semantic search across Granola meeting notes. Extracts insights, themes (pain-points, feature-requests, decisions, etc.), and key quotes with speaker attribution. Uses LanceDB for fast local vector search.
Based on reverse engineering research by Joseph Thacker and getprobo.
Features
Export: Extract all your Granola meetings with transcripts
Semantic Search: Vector-indexed search across meetings with pre-extracted insights
Speaker Attribution: Distinguishes between host (
me) and participantsTheme Extraction: Auto-categorizes content into themes (pain-points, feature-requests, etc.)
MCP Server: Exposes search to Claude Code, Claude Desktop, and other AI tools
Prerequisites
Node.js 18+
Granola desktop app installed and logged in
OpenAI API key (for embeddings and insight extraction)
Installation
Quick Start
CLI Commands
Sync (Recommended)
The easiest way to keep your data up to date - exports from Granola and rebuilds the index in one step:
Export from Granola
Export only (without indexing):
Build Search Index
Search from CLI
Export for ChatGPT
Other Commands
MCP Server Setup
Claude Code
Add to .mcp.json in your project:
Claude Desktop
Add to ~/Library/Application Support/Claude/claude_desktop_config.json:
MCP Tools
Tool | Description |
| Semantic search across meetings, returns summaries + quotes |
| Find documents by theme (pain-points, feature-requests, etc.) |
| List all folders with document counts |
| List documents with brief summaries |
| Get full document details (all themes + quotes) |
| Get raw transcript (use sparingly) |
| List available themes with definitions |
Speaker Attribution
The system distinguishes between speakers:
speaker: "me"- The meeting host (you)speaker: "participant"- Other people in the meeting
This helps AI understand what's your own idea vs external feedback.
Pre-defined Themes
pain-points: User frustrations, problems, complaints
feature-requests: Desired features, wishlist items
positive-feedback: What users liked, praised
pricing: Cost concerns, value perception
competition: Competitor mentions, alternatives
workflow: How users currently do things
decisions: Key decisions made, action items
questions: Open questions needing clarification
Output Structure
Keeping Data Updated
The system doesn't auto-sync with Granola. Run sync manually after new meetings, or set up a cron job:
Manual Update
Automated Updates (Cron)
Add to your crontab (crontab -e):
macOS LaunchAgent
Create ~/Library/LaunchAgents/com.granola-mcp.sync.plist:
Load it with: launchctl load ~/Library/LaunchAgents/com.granola-mcp.sync.plist
How It Works
Export: Reads credentials from
~/Library/Application Support/Granola/supabase.jsonand fetches all documents via Granola's APIIndex:
Extracts themes and key quotes using GPT-4o-mini
Generates embeddings using text-embedding-3-small
Stores in LanceDB for fast vector search
Search:
Embeds your query
Finds semantically similar documents
Returns summaries + relevant quotes (not raw transcripts)
Cost Estimates
Documents | Insight Extraction | Embeddings | Total |
25 | ~$0.50 | ~$0.01 | ~$0.51 |
100 | ~$2.00 | ~$0.02 | ~$2.02 |
500 | ~$10.00 | ~$0.10 | ~$10.10 |
Search queries are free (vector similarity, no LLM calls).
License
MIT