granola-mcp

granola-mcp

Enables semantic search and insight extraction across Granola meeting notes, categorizing content into themes like pain points and decisions. It provides AI assistants with tools to query meeting transcripts, summaries, and speaker-attributed quotes via a local vector index.

Category
Visit Server

README

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 participants
  • Theme 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

npm install
npm run build

Quick Start

# One command to sync everything (export + index)
OPENAI_API_KEY=sk-... node dist/index.js sync

# Or with a custom data directory
OPENAI_API_KEY=sk-... node dist/index.js sync ./my-data

# Test search
OPENAI_API_KEY=sk-... node dist/index.js search "user pain points"

CLI Commands

Sync (Recommended)

The easiest way to keep your data up to date - exports from Granola and rebuilds the index in one step:

OPENAI_API_KEY=sk-... node dist/index.js sync

# With options
OPENAI_API_KEY=sk-... node dist/index.js sync ./my-data
OPENAI_API_KEY=sk-... node dist/index.js sync --skip-extraction  # Faster, reuses existing insights

Export from Granola

Export only (without indexing):

node dist/index.js export ./output
node dist/index.js export ./output --format markdown
node dist/index.js export ./output --format json

Build Search Index

# Full indexing with insight extraction (~$0.02/document)
OPENAI_API_KEY=sk-... node dist/index.js index ./export

# Skip extraction (use existing insights, just rebuild embeddings)
OPENAI_API_KEY=sk-... node dist/index.js index ./export --skip-extraction

Search from CLI

OPENAI_API_KEY=sk-... node dist/index.js search "pricing concerns"
OPENAI_API_KEY=sk-... node dist/index.js search "feature requests" --folder "User interviews"

Export for ChatGPT

OPENAI_API_KEY=sk-... node dist/index.js export-combined ./chatgpt.md
OPENAI_API_KEY=sk-... node dist/index.js export-combined ./chatgpt.md --query "user feedback"

Other Commands

node dist/index.js list              # List documents
node dist/index.js workspaces        # List workspaces
node dist/index.js folders           # List folders
node dist/index.js transcript <id>   # Get specific transcript

MCP Server Setup

Claude Code

Add to .mcp.json in your project:

{
  "mcpServers": {
    "granola": {
      "type": "stdio",
      "command": "node",
      "args": ["/path/to/granola-mcp/dist/mcp/server.js"],
      "env": {
        "GRANOLA_DATA_DIR": "/path/to/granola-mcp/export",
        "OPENAI_API_KEY": "sk-..."
      }
    }
  }
}

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "granola": {
      "command": "node",
      "args": ["/path/to/granola-mcp/dist/mcp/server.js"],
      "env": {
        "GRANOLA_DATA_DIR": "/path/to/granola-mcp/export",
        "OPENAI_API_KEY": "sk-..."
      }
    }
  }
}

MCP Tools

Tool Description
search Semantic search across meetings, returns summaries + quotes
search_themes Find documents by theme (pain-points, feature-requests, etc.)
list_folders List all folders with document counts
list_documents List documents with brief summaries
get_document Get full document details (all themes + quotes)
get_transcript Get raw transcript (use sparingly)
get_themes 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

export/
├── vectors.lance/           # LanceDB vector index
├── Meeting_Title_1/
│   ├── document.json        # Raw document data
│   ├── notes.md             # Converted notes
│   ├── transcript.json      # Raw transcript with speaker info
│   ├── transcript.md        # Formatted transcript
│   └── transcript.txt       # Plain text transcript
└── Meeting_Title_2/
    └── ...

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

OPENAI_API_KEY=sk-... node dist/index.js sync

Automated Updates (Cron)

Add to your crontab (crontab -e):

# Sync every night at 2am
0 2 * * * cd /path/to/granola-mcp && OPENAI_API_KEY=sk-... /usr/local/bin/node dist/index.js sync >> /tmp/granola-sync.log 2>&1

# Or every 6 hours
0 */6 * * * cd /path/to/granola-mcp && OPENAI_API_KEY=sk-... /usr/local/bin/node dist/index.js sync >> /tmp/granola-sync.log 2>&1

macOS LaunchAgent

Create ~/Library/LaunchAgents/com.granola-mcp.sync.plist:

<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
    <key>Label</key>
    <string>com.granola-mcp.sync</string>
    <key>ProgramArguments</key>
    <array>
        <string>/usr/local/bin/node</string>
        <string>/path/to/granola-mcp/dist/index.js</string>
        <string>sync</string>
    </array>
    <key>EnvironmentVariables</key>
    <dict>
        <key>OPENAI_API_KEY</key>
        <string>sk-...</string>
    </dict>
    <key>StartCalendarInterval</key>
    <dict>
        <key>Hour</key>
        <integer>2</integer>
        <key>Minute</key>
        <integer>0</integer>
    </dict>
    <key>StandardOutPath</key>
    <string>/tmp/granola-sync.log</string>
    <key>StandardErrorPath</key>
    <string>/tmp/granola-sync.log</string>
</dict>
</plist>

Load it with: launchctl load ~/Library/LaunchAgents/com.granola-mcp.sync.plist

How It Works

  1. Export: Reads credentials from ~/Library/Application Support/Granola/supabase.json and fetches all documents via Granola's API

  2. Index:

    • Extracts themes and key quotes using GPT-4o-mini
    • Generates embeddings using text-embedding-3-small
    • Stores in LanceDB for fast vector search
  3. 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

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured