GoodNotes MCP Server

An MCP (Model Context Protocol) server that reads handwritten notes from GoodNotes on macOS. It exposes your handwritten notebooks as structured data that AI assistants like Claude can read, search, and process.

How It Works

GoodNotes stores all data in local SQLite databases on macOS:

• projection.sqlite — document metadata (names, folders, page ordering)

• fts.sqlite — full-text search index with OCR'd handwriting (multiple recognition candidates per word)

This MCP server reads those databases (read-only) and exposes 6 tools:

OCR Candidate Format

GoodNotes OCR produces multiple word candidates. The server returns them separated by |:

Temple|Tomple|temple Voice|Voica recognition

Your AI assistant picks the best word using semantic context — much more accurate than taking the top candidate alone.

Related MCP server: Apple Notes MCP Server

Requirements

• macOS (GoodNotes stores its databases locally)

• GoodNotes installed and synced

• Python 3.11+

• uv (recommended) or pip

Installation

git clone https://github.com/withsivram/goodnotes-mcp.git
cd goodnotes-mcp
uv venv && uv pip install -e .

Configuration

Claude Code

Add to your Claude Code MCP settings:

{
  "mcpServers": {
    "goodnotes": {
      "type": "stdio",
      "command": "/path/to/goodnotes-mcp/.venv/bin/python",
      "args": ["/path/to/goodnotes-mcp/server.py"]
    }
  }
}

Claude Desktop

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

{
  "mcpServers": {
    "goodnotes": {
      "command": "/path/to/goodnotes-mcp/.venv/bin/python",
      "args": ["/path/to/goodnotes-mcp/server.py"]
    }
  }
}

Environment Variables

Usage

Once configured, your AI assistant can:

1. List your notebooks: "What notebooks do I have in GoodNotes?"

2. Read notes: "Read my latest notebook"

3. Search: "Search my handwritten notes for 'meeting action items'"

4. Process pipeline: Use get_unprocessed + mark_processed to build automated workflows (e.g., handwriting → structured Obsidian notes)

Example: Processing Notes into Obsidian

The server is designed as a minimal data pipe — all intelligence (OCR resolution, categorization, structuring) happens in the AI assistant. A typical workflow:

1. get_unprocessed → find new pages

2. read_notebook → get raw OCR with word candidates

3. AI resolves Temple|Tomple|temple → "Temple" using context

4. AI categorizes and structures into markdown

5. Write to Obsidian (or any markdown-based system)

6. mark_processed → track what's been handled

Architecture

iPad (GoodNotes) → iCloud Sync → macOS SQLite DBs → MCP Server → AI Assistant

The server is intentionally minimal (~390 lines, zero external dependencies beyond mcp). All intelligence lives in the AI layer, making the server easy to maintain and extend.

License

MIT License — see LICENSE.