Codebase Memory MCP — Code Intelligence for AI Agents

What it is

Codebase Memory MCP is a high-performance code intelligence server built on the Model Context Protocol. It indexes entire repositories in milliseconds using tree-sitter AST parsing and supports 66 programming languages. AI agents query the indexed graph with sub-millisecond response times.

It targets developers using AI coding assistants like Claude Code, Cursor, or Windsurf who need their agent to understand the full structure of a codebase — functions, classes, imports, and call graphs — without manually feeding context.

How it saves time or tokens

Without a code intelligence server, AI agents must read files sequentially to understand project structure, consuming tokens on boilerplate and irrelevant code. Codebase Memory MCP pre-indexes the AST so the agent queries only what it needs. This reduces context window usage and speeds up responses.

How to use

1. Add the MCP server to your .mcp.json configuration:

{
  "mcpServers": {
    "codebase-memory-mcp": {
      "command": "npx",
      "args": ["-y", "codebase-memory-mcp@latest"]
    }
  }
}

1. Restart your AI editor (Claude Code, Cursor, or Windsurf).
2. The server indexes your project automatically and responds to structural queries from the agent.

Example

{
  "mcpServers": {
    "codebase-memory-mcp": {
      "command": "npx",
      "args": ["-y", "codebase-memory-mcp@latest"]
    }
  }
}

Once configured, your AI agent can query the codebase graph for function definitions, class hierarchies, and import chains without reading every file.

Related on TokRepo

• MCP Integrations — More MCP servers for AI-assisted development
• AI Memory Providers — Memory solutions that complement code intelligence

Key considerations

When evaluating Codebase Memory MCP for your workflow, consider the following factors. First, assess whether your team has the technical prerequisites to adopt this tool effectively. Second, evaluate the maintenance burden against the productivity gains. Third, check community activity and documentation quality to ensure long-term viability. Integration with your existing toolchain matters more than feature count alone. Start with a small pilot project before rolling out across the organization. Monitor resource usage during the initial adoption phase to identify bottlenecks early. Document your configuration decisions so team members can onboard independently.

Common pitfalls

• Very large monorepos (100K+ files) may take longer for the initial index; subsequent queries remain fast.
• Languages not in the 66 supported by tree-sitter will be skipped during indexing.
• The MCP server runs as a local process; it does not send code to external servers, but it does consume local CPU and memory during indexing.