What runtime does CodeWeave Mcp require?
Node.js 20 or later. Ollama must also be installed locally to run the embedding model.
@semihkayan
Basic information
Save tokens while coding — your AI agent gets structured code context, not file dumps.
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"codeweave-mcp": {
"command": "npx",
"args": [
"@codeweave/mcp"
]
}
}
}Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
Fetch the live tool list by running this server in a temporary sandbox using the button above.
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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