Mac Messages MCP

The Mac Messages MCP provides a lightweight Python bridge that exposes the native macOS Messages application to AI assistants via the Model Context Protocol. It resolves a common pain point for developers who want their conversational agents to interact with personal communications without building custom integrations from scratch. By converting the local Messages database into a set of MCP resources and tools, the server lets assistants read, search, and send messages as if they were calling a cloud API. This removes the need for external messaging services or manual data extraction, while preserving privacy because all operations stay on the local machine.

At its core, the server offers a suite of high‑level actions that mirror everyday messaging workflows. Developers can send text to any contact, and the tool will automatically decide whether to use iMessage or fall back to SMS/RCS based on the recipient’s capabilities. It also provides reading functionality: fetching recent messages, filtering by contact or phone number, and performing fuzzy content searches. Additionally, a dedicated tool lets agents detect whether a phone number is iMessage‑enabled before attempting delivery. These capabilities are packaged as simple, strongly typed MCP methods that can be invoked from Claude or Cursor with a single line of JSON.

The value for AI‑centric development lies in the seamless integration pattern. Once the server is running, a Claude Desktop or Cursor client can declare it in its configuration and immediately gain access to the Messages API. The assistant can then build sophisticated conversational flows—such as “remind me to text Mom tomorrow” or “fetch the last three messages from John”—without writing any platform‑specific code. The server’s full disk access requirement is a small trade‑off for the ability to query the native SQLite database directly, ensuring low latency and up‑to‑date data.

Real‑world scenarios that benefit from this MCP include:

• Automated reminders that ping a user’s phone via iMessage.
• Cross‑platform communication where an assistant can message Android users through SMS while using iMessage for Apple contacts.
• Contextual chatbots that pull recent conversation history to maintain continuity in long dialogues.
• Data‑driven analytics where developers analyze message patterns without exposing personal data to third‑party services.

Because the server runs locally and exposes only a narrow, well‑defined interface, it offers unique advantages over generic messaging APIs: no external network traffic, full control over privacy, and instant response times. Its design aligns with MCP’s goal of modular, reusable tool definitions, making it an attractive choice for any project that needs to bridge local macOS messaging with AI assistants.