Bear MCP Server

The Bear MCP Server bridges the gap between a local note‑taking application and AI assistants that rely on the Model Context Protocol. By exposing Bear Notes—stored in a lightweight SQLite database—to an MCP interface, the server allows Claude and other AI agents to read, search, and organize personal knowledge directly from within their conversational context. This eliminates the need for manual exporting or duplication of notes, keeping the workflow streamlined and secure.

At its core, the server implements three straightforward yet powerful tools: , , and . pulls the entire note collection, while enumerates every tag used in Bear, enabling AI agents to understand the user’s categorization scheme. performs a text‑based search, returning notes that contain user‑specified keywords. These capabilities provide AI assistants with a contextual knowledge base that is both up‑to‑date and deeply personalized, which is invaluable for tasks such as drafting emails, generating summaries, or brainstorming ideas that draw on the user’s own notes.

Developers benefit from the server’s simplicity and its native integration with macOS. Since Bear stores data in a single SQLite file, the MCP server interacts directly with that database without requiring any external services or cloud synchronization. This local approach preserves privacy and reduces latency, ensuring that AI responses are generated quickly from the freshest data. The server is written in Node.js, making it easy to deploy and maintain across environments that already support JavaScript tooling.

Real‑world use cases abound: a researcher can ask Claude to pull relevant notes while drafting a paper; a project manager might request the latest status updates from tagged meeting notes; or a writer could search for inspiration across scattered ideas stored in Bear. In each scenario, the MCP server supplies the AI with precise, context‑rich content that would otherwise require manual lookup. The ability to search by text also opens the door to advanced querying, such as filtering notes that mention a specific project or contain a particular keyword.

What sets this MCP server apart is its focused feature set combined with zero‑touch integration. Once the configuration points to the server’s executable, all three tools appear automatically in the AI assistant’s toolbox. Developers can then leverage these tools in prompts, chain them together for complex workflows, or even extend the server with additional SQL queries if needed. The result is a lightweight, privacy‑respecting bridge that empowers AI assistants to become true extensions of the user’s personal knowledge ecosystem.