Codemesh MCP Server

@pyalwin

代码仓库

基本信息

分类

开发工具

传输方式

stdio

作者

pyalwin

提交者

pyalwin

关于 Codemesh MCP Server

Intelligent code knowledge graph for AI coding agents. Gives agents a persistent, queryable knowledge graph with search, call tracing, and agent write-back. 71% cheaper, 72%

#grep #read #knowledge-graph #code-search#call-trace

Codemesh MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "codemesh": {
      "command": "npx",
      "args": [
        "-y",
        "@pyalwin/codemesh"
      ],
      "env": {
        "CODEMESH_PROJECT_ROOT": "/path/to/your/project"
      }
    }
  }
}

Codemesh MCP Server 工具

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点击上方按钮,在临时沙箱中运行此服务器并实时获取工具列表。

概览

What is Codemesh?

Codemesh is an MCP server that gives AI coding agents a persistent, queryable knowledge graph. It indexes codebases into a graph of symbols, edges, and embeddings, enabling agents to explore, trace, and ask questions without wasting tokens on discovery. It is for developers using AI coding agents on real-world codebases.

How to use Codemesh?

Install the npm package globally (npm install -g @pyalwin/codemesh), then index your project (codemesh index --with-embeddings). Configure your MCP client (e.g., Claude Code, Claude Desktop, Cursor) with the codemesh MCP server pointing to your project root. The agent gets six codemesh_* tools: codemesh_answer, codemesh_explore, codemesh_trace, codemesh_enrich, codemesh_workflow, codemesh_status. Alternatively, use CLI mode by exporting CODEMESH_PROJECT_ROOT and calling codemesh explore ... directly via Bash.

Key features of Codemesh

Persistent, queryable code knowledge graph
71% cheaper and 72% faster than baseline Grep+Read
82% fewer tool calls (8 vs 45 on average)
Agent write-back: enrich graph with summaries and workflows
MCP and CLI integration modes
Supports semantic search, impact analysis, call tracing

Use cases of Codemesh

Reducing token waste when agents explore large codebases
Quickly answering complex code questions without reading all files
Tracing call chains across many files in a single tool call
Persisting agent learning across sessions via write-back enrichment

常见问题

How do I install Codemesh?

Run `npm install -g @pyalwin/codemesh` or run directly with `npx -y @pyalwin/codemesh`.

What is the difference between MCP Server and CLI mode?

MCP Server works via structured tool calls and is best for Opus and complex workflows. CLI mode has zero MCP overhead, calls codemesh directly via Bash, and is recommended for Sonnet/Haiku and speed-sensitive workflows.

Does Codemesh require any special runtime or dependencies?

It requires Node.js and npm. It has no other external dependencies. The codebase must be a local project directory.

How does the agent write-back work?

The agent can call `codemesh_enrich` and `codemesh_workflow` tools to persist summaries, concepts, and multi-file flows into the graph. These enrichments survive re-indexing and are available across sessions.

What is the recommended way to run the index?

Run `codemesh index --with-embeddings` from the project root. This creates symbols, edges, and embeddings for all files. The index step takes seconds to minutes depending on codebase size.

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