Zed MCP Server Basic Memory

Overview

The Zed MCP Server Extension for Basic Memory turns a local Markdown‑based knowledge base into a live context provider for AI assistants running inside the Zed editor. By exposing Basic Memory’s persistent “notes” and search capabilities over the Model Context Protocol, developers can query and manipulate their personal knowledge graph directly from the assistant’s prompt language. This solves a common pain point for AI‑powered coding workflows: the lack of a lightweight, version‑controlled memory that stays in sync with your codebase and does not require cloud services or complex database setups.

At its core, the server exposes a set of natural‑language commands—such as “Create a note about software architecture patterns” or “Search my notes for information about React hooks”—as MCP tools. When invoked, these commands read from or write to Markdown files stored in a project‑specific directory (or the default location). The server then returns structured JSON responses that the assistant can incorporate into its next turn, enabling context‑aware suggestions, documentation generation, or even automated code comments based on previously stored knowledge. For developers, this means they can keep a curated set of best practices, design patterns, or personal research notes directly alongside their source code and let the assistant surface relevant information on demand.

Key capabilities include:

• Persistent, file‑based storage that remains under version control and can be shared across machines without a central server.
• Multi‑project support, allowing separate knowledge bases for different codebases or domains, which keeps context relevant and uncluttered.
• Full text search across all notes, enabling quick retrieval of information without manual browsing.
• Write‑through API, so new insights captured during a conversation are automatically persisted for future sessions.

Real‑world scenarios where this MCP server shines include onboarding new team members (they can query the knowledge base for architectural guidelines), rapid prototyping (the assistant can pull in pattern references on the fly), and continuous learning (developers capture lessons learned during code reviews into Markdown notes that are then surfaced in subsequent discussions). Because the server operates locally, it respects privacy and compliance requirements that might preclude sending data to external services.

Integration into AI workflows is straightforward: once the Zed editor’s MCP tools are enabled, the assistant can call these tools as part of its reasoning loop. The assistant’s prompt templates can reference “notes” or “search results,” and the MCP server will return the relevant text, which the model can then use to generate richer, context‑aware outputs. This tight coupling between local knowledge and AI reasoning eliminates the need for separate documentation tools, streamlines the developer experience, and keeps all information in a human‑readable format that can be edited outside of the editor when needed.