広告掲載
MCP.so
ログイン
C

Codemesh MCP Server

@pyalwin

リポジトリ

基本情報

カテゴリ

開発者ツール

トランスポート

stdio

作者

pyalwin

投稿者

pyalwin

Codemesh MCP Server について

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 の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

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

Codemesh MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

上のボタンで一時的なサンドボックスでこのサーバーを実行し、ツール一覧を取得できます。

概要

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

よくある質問

1

How do I install Codemesh?

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

2

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.

3

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.

4

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.

5

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.

コメント

「開発者ツール」の他のコンテンツ

Casefile

azimov777

One case file per task — shared by every session, agent and person that touches it. Self-hosted MCP server and live board. MIT.

3

Mcp-Warden

ermoluk

Local-first credential broker for MCP. AI coding agents (Claude Code, Cursor, Codex…) call APIs, SSH and databases through scoped presets; secrets stay in your OS keychain and never enter the agent's context.

4

Perfex CRM

themesic

Turn Perfex CRM into an AI-ready workspace. This MCP server exposes your full REST API to Claude, ChatGPT and any AI agent, so leads, invoices and tasks are one prompt away.

1

JiCo for Jira & Confluence – PII-redacting MCP server with Human-in-the-Loop

drkv-com

JiCo for Jira & Confluence: local MCP server for Jira Data Center - redacts personal data before the AI model, holds every write for approval

OrangePro

Aamir Siddiqui

OrangePro local-first CLI + MCP server for behavior mapping, grounded test generation, and dynamic proof.

12
広告掲載