MCP / AI Assistant Integration

CodeRag ships an MCP (Model Context Protocol) server as an npm package. It exposes the following tools to any MCP-compatible AI assistant (Copilot, Claude, Cursor, etc.):

| Tool | Description | |------|-------------| | coderag_list_workspaces | List all indexed workspaces and their chunk/edge counts. Call this first to discover workspace names. | | coderag_bulk_query | Run 1–10 hybrid searches in parallel (vector + lexical + symbol, RRF-fused). Returns LLM-ready text blocks including call-graph neighbors and external library XML docs. Prefer this over a single query. | | coderag_bulk_file_chunks | Fetch chunk outlines (all functions, classes, methods) for 1–20 files in parallel. | | coderag_bulk_type_members | Fetch all members of 1–20 types in parallel. Useful after coderag_type_implementors to drill into each implementation. | | coderag_type_implementors | Find all types that directly implement or inherit a given signature. | | coderag_chunk_edges | Get incoming and outgoing call-graph edges for a chunk ID. Answers "who calls this?" and "what does this call?" |

Install

npm install -g @jayarrowz/mcp-coderag

Or run without installing:

npx @jayarrowz/mcp-coderag

Configure

The server connects to the CodeRag dashboard API. Set CODERAG_URL to point at your running dashboard (defaults to http://localhost:5180 or port 7180 via docker):

VS Code (settings.json):

"mcp": {
  "servers": {
    "coderag": {
      "command": "npx",
      "args": ["-y", "@jayarrowz/mcp-coderag"],
      "env": { "CODERAG_URL": "http://localhost:7180" }
    }
  }
}

Claude Desktop (claude_desktop_config.json):

"mcpServers": {
  "coderag": {
    "command": "npx",
    "args": ["-y", "@jayarrowz/mcp-coderag"],
    "env": { "CODERAG_URL": "http://localhost:7180" }
  }
}

The source lives in src/CodeRag.Mcp/. See the npm package for the latest release.