iMessage MCP

The iMessage MCP server is a lightweight, Deno‑based service that exposes the local iMessage database on macOS to large language models via the Model Context Protocol. It bridges the gap between an AI assistant and a user’s personal messaging history, enabling context‑aware conversations that can reference past chats, retrieve contact details, and even pull in recent messages for real‑time dialogue. By converting raw SQLite data into a set of well‑defined MCP tools, developers can integrate iMessage content without needing to write custom database queries or handle the intricacies of macOS data protection.

What Problem Does It Solve?

Many AI assistants operate in isolation, lacking access to a user’s private communications. For tasks such as drafting replies, summarizing conversations, or automating message‑based workflows, direct access to iMessage is essential. The MCP server solves this by providing a secure, read‑only interface that respects macOS privacy controls. It allows developers to build applications where the assistant can reference past interactions, identify participants by phone number or email, and retrieve contact information—all while keeping the underlying database untouched.

Core Functionality and Value

At its heart, the server offers a collection of tools that map naturally to common iMessage operations:

• search_messages – Find messages by text, sender, or date range.
• get_recent_messages – Pull the latest chat history for quick context.
• get_chats – Enumerate all conversations, useful for navigation or selection.
• get_handles – List all contact identifiers (phone numbers, emails).
• get_messages_from_chat – Retrieve a specific conversation’s history.
• search_contacts – Query macOS Contacts to resolve names into iMessage handles.

Each tool accepts optional pagination parameters, returning metadata that lets clients fetch large datasets incrementally. This design keeps the service lightweight while scaling to users with extensive message archives.

Use Cases and Real‑World Scenarios

• Smart Reply Generation – An assistant can pull recent messages from a thread and suggest context‑appropriate responses.
• Conversation Summaries – By querying a chat’s history, the AI can produce concise summaries for users who missed earlier exchanges.
• Contact Management – Developers can build tools that let users search for friends or colleagues by name and immediately see their iMessage handles.
• Automation Workflows – Integrations with task managers or calendar apps can trigger actions based on message content (e.g., “Schedule a meeting when an iMessage says ‘Let’s meet next week’”).
• Data Migration Helpers – The server can feed migration scripts that export iMessage data to other platforms while preserving context.

Integration with AI Workflows

The MCP server is designed to plug into any LLM that supports the Model Context Protocol, such as Claude or other AI assistants. A client simply declares the server in its configuration (e.g., ) and invokes the exposed tools. Because each tool returns structured JSON, developers can easily compose higher‑level workflows or chain multiple calls together. The read‑only nature of the server also means it can run with minimal permissions, aligning with best practices for security and privacy.

Unique Advantages

• Native macOS Integration – Direct access to and the AddressBook database eliminates external dependencies.
• Zero‑Coding Database Layer – Developers interact with high‑level tools instead of raw SQL, reducing boilerplate and potential bugs.
• Pagination Support – Even users with thousands of messages can retrieve data efficiently without overwhelming the assistant or the network.
• Deno Monorepo Structure – The core library and MCP server share code, ensuring consistency and making maintenance straightforward.

In summary, the iMessage MCP server equips AI assistants with rich, contextual access to a user’s messaging ecosystem on macOS. By abstracting the complexities of iMessage data handling into intuitive MCP tools, it opens a wide range of productivity and automation possibilities while respecting privacy and system integrity.