Beeper Texts MCP Server

Beeper Texts MCP Server

Local-only MCP server that exposes Beeper Texts data (messages, chats, contacts) from the macOS Beeper Desktop SQLite database for AI assistants and automation tools, supporting search and media retrieval across multiple platforms.

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Beeper Texts MCP Server

A local-only MCP (Model Context Protocol) server tested on macOS that exposes Beeper Texts data (messages, contacts, chats) for use with AI assistants and automation tools. Local-only means the MCP accesses your local Beeper SQLite database only, and does not make or trigger any network requests.

Features

  • Chat Management: List and browse conversations across platforms
  • Message Access: Retrieve messages from specific chats
  • Search: Search messages by content, chat name, or by sender
  • Media Access: Fetch attachment bytes/paths via a URI returned in messages
  • Multi-Platform Support: Tested with WhatsApp, Telegram, Signal, Instagram, Twitter/X and LinkedIn. Should work with any Beeper-supported platform.

Requirements

  • macOS only: This server relies on Beeper Desktop's local database structure only
  • Beeper Desktop: Must be installed, configured with at least one connected account and running to receive new messages
  • Python 3.10+: Required for running the server

Installation

Install from PyPI using pip or uvx:

pip install mcp-beeper-texts

Or use uvx for isolated execution:

uvx mcp-beeper-texts

Configuration

Claude Desktop

Add the following to your Claude Desktop configuration file (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "beeper": {
      "command": "uvx",
      "args": ["mcp-beeper-texts"]
    }
  }
}

Other MCP Clients

For other MCP clients, use the command:

uvx mcp-beeper-texts

The server communicates over stdio transport.

TODO

Add support for:

  • [ ] Creating, Editing, and Deleting drafts
  • [ ] Sending messages
  • [ ] Sending media attachments

Available Tools

list_chats

List group and DM chats with metadata.

  • label (optional): Filter set: inbox, archive, favourite, all, unread (default: inbox)
  • sort_by (optional): latest_message, last_active, or name (default: latest_message)
  • limit (optional): Max chats to return (default: 25)
  • recent_messages_limit (optional): Include last N messages per chat (default: 3, set 0 to disable)
  • max_participants (optional): Max participant names for groups (default: 5)
  • include_low_priority (optional): Include low priority in non-inbox views (default: false)

get_messages

Get chronologically ordered messages from a specific chat.

  • chat_id (required)
  • limit (optional): Default 50
  • before/after (optional): ISO-8601 timestamps to page

search_message_contents

Search message contents across chats with optional context.

  • query (required)
  • chat_id (optional): Limit to one chat
  • limit (optional): Default 25
  • include_context (optional): Include surrounding messages (default: true)

search_chat_names

Search chats by name/title with label filtering.

  • query (required)
  • label (optional): Default all
  • limit (optional): Default 25

get_person_messages

Get messages sent by a specific person across chats.

  • person_name (required)
  • limit (optional): Max per chat (default: 50)
  • platform (optional): Platform filter
  • chat_type (optional): dm, group, or all
  • days_back (optional): Only include messages from the last N days
  • include_context (optional): Include surrounding messages

get_media_attachment

Retrieve media attachment bytes/path by URI returned in Message.attachments.

  • attachment_uri (required): e.g., beeper://attachment/{message_id}/{attachment_index}
  • optimize_for_context (optional): For images, resize to ≤1568px for efficiency (default: true)

Development

Setup

  1. Clone the repository
  2. Install dependencies: uv sync
  3. Run tests: uv run pytest
  4. Format code: uv run ruff format .
  5. Lint code: uv run ruff check . --fix

Testing

Run the test suite:

uv run pytest tests/ -v

MCP Inspector

Use the MCP Inspector for development and testing:

uv run mcp dev src/mcp_beeper_texts/server.py

Claude Desktop

For Claude Desktop testing (or other local MCP clients), use this configuration in ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "Beeper": {
      "command": "/opt/homebrew/bin/uv",
      "args": [
        "run",
        "--dev",
        "--project",
        "/path/to/your/mcp-beeper-texts",
        "mcp-beeper-texts"
      ]
    }
  }
}

Replace /path/to/your/mcp-beeper-texts with the actual path to your local repository.

Hot-Reload Development

For faster development with automatic reloading when files change, you can use MCP Reloader:

# Install MCP Reloader (one-time setup)
git clone https://github.com/mizchi/mcp-reloader.git
cd mcp-reloader
npm install
npm run build

# Run with hot-reload (from your project directory)
npx mcp-reloader --command "uv run src/mcp_beeper_texts/server.py"

This automatically restarts the server when you modify any Python files, providing a faster development workflow.

Database Access

The server reads from Beeper's local SQLite databases located at:

~/Library/Application Support/BeeperTexts/

  • index.db: Beeper UI-optimized message and chat index and metadata
  • local-{platform}/megabridge.db: Platform-specific data and contacts

The server only requires read access for most operations, with write access eventually needed for draft management and message sending.

Troubleshooting

"Beeper directory not found"

Ensure Beeper Desktop is installed and has been run at least once. The application creates its data directory on first launch.

"Database not found"

Make sure Beeper Desktop is properly configured with at least one connected account. The databases are created when platforms are connected.

Permission errors

Ensure the server has read access to ~/Library/Application Support/BeeperTexts/. This should be automatic on macOS.

License

MIT License - see LICENSE file for details.

Contributing

Contributions are welcome. Please follow these steps:

  1. Fork the repository
  2. Create a feature branch (git checkout -b feat/new-feature)
  3. Add functionality and tests if applicable
  4. Run the test suite (uv run pytest)
  5. Format and lint your code (uv run ruff format . && uv run ruff check . --fix)
  6. Commit, Push, and create a Pull Request

Changelog

0.0.1

  • Initial release
  • Basic chat, message, and contact management
  • Search functionality across all platforms
  • Basic test suite

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