What runtime does CodeWeave Mcp require?
Node.js 20 or later. Ollama must also be installed locally to run the embedding model.
@semihkayan
基本情報
Save tokens while coding — your AI agent gets structured code context, not file dumps.
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": {
"codeweave-mcp": {
"command": "npx",
"args": [
"@codeweave/mcp"
]
}
}
}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。
CodeWeave Mcp is an MCP server that gives AI agents structured code intelligence by querying local indexes — AST, call graph, type graph, and hybrid semantic search — instead of dumping entire files into context. It is designed for developers using AI coding assistants like Claude Code and VS Code who want cheaper, more precise code understanding.
Run npx @codeweave/mcp inside your project directory. The setup wizard automatically installs dependencies, configures your MCP client, and indexes your project. After setup, open your project in Claude Code or VS Code and ask questions using the 8 available tools (e.g., semantic_search, get_function_source, get_impact_analysis).
Node.js 20 or later. Ollama must also be installed locally to run the embedding model.
Indexes live on disk in a `.code-context` directory. LanceDB stores vector embeddings; call and type graphs are stored as JSON.
The system degrades gracefully: search falls back to full-text only, and AST-based tools (dependencies, impact analysis, module summary) work without any embedding infrastructure.
It uses the MCP protocol over stdio transport. It works with any MCP-compatible client, including Claude Code, VS Code, and Cursor.
CodeWeave Mcp is under active development. The first run requires a one-time download of Ollama and the embedding model (takes a few minutes).
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