Qdrant Docs Rag MCP server

This MCP server implementation provides tools for retrieving and processing documentation through vector search, allowing AI assistants to enhance their responses with relevant documentation context. It offers semantic search capabilities across multiple documentation sources for real-time context augmentation.

Installation and Configuration

To use the RAG Documentation MCP server, you'll need to configure it with Claude Desktop by adding the following to your claude_desktop_config.json:

{
  "mcpServers": {
    "rag-docs": {
      "command": "npx",
      "args": [
        "-y",
        "@hannesrudolph/mcp-ragdocs"
      ],
      "env": {
        "OPENAI_API_KEY": "",
        "QDRANT_URL": "",
        "QDRANT_API_KEY": ""
      }
    }
  }
}

You must provide values for these environment variables:

• OPENAI_API_KEY: Your OpenAI API key for generating embeddings
• QDRANT_URL: URL of your Qdrant vector database instance
• QDRANT_API_KEY: API key for Qdrant authentication

Available Tools

Search Documentation

The search_documentation tool lets you search through stored documentation using natural language queries.

Inputs:

• query (string): Text to search for in the documentation (natural language query, specific terms, or code snippets)
• limit (number, optional): Maximum results to return (1-20, default: 5)

List Sources

Use list_sources to view all documentation sources currently stored in the system. This returns a comprehensive list of indexed documentation including source URLs, titles, and last update times.

Extract URLs

The extract_urls tool crawls a specified webpage and identifies all hyperlinks.

Inputs:

• url (string): Complete URL of the webpage to analyze (must include protocol)
• add_to_queue (boolean, optional): If true, automatically adds extracted URLs to the processing queue

Remove Documentation

To remove specific documentation sources from the system:

Inputs:

• urls (string[]): Array of URLs to remove from the database (must exactly match the URL used when adding documentation)

Queue Management

Three tools help you manage the documentation processing queue:

• list_queue: Shows all URLs waiting in the processing queue
• run_queue: Processes and indexes all URLs in the queue sequentially
• clear_queue: Removes all pending URLs from the queue

Practical Applications

The RAG Documentation tool can be effectively used for:

• Enhancing AI responses with relevant documentation
• Building documentation-aware AI assistants
• Creating context-aware tooling for developers
• Implementing semantic documentation search
• Augmenting existing knowledge bases

How to add this MCP server to Cursor

There are two ways to add an MCP server to Cursor. The most common way is to add the server globally in the ~/.cursor/mcp.json file so that it is available in all of your projects.

If you only need the server in a single project, you can add it to the project instead by creating or adding it to the .cursor/mcp.json file.

Adding an MCP server to Cursor globally

To add a global MCP server go to Cursor Settings > MCP and click "Add new global MCP server".

When you click that button the ~/.cursor/mcp.json file will be opened and you can add your server like this:

{
    "mcpServers": {
        "cursor-rules-mcp": {
            "command": "npx",
            "args": [
                "-y",
                "cursor-rules-mcp"
            ]
        }
    }
}

Adding an MCP server to a project

To add an MCP server to a project you can create a new .cursor/mcp.json file or add it to the existing one. This will look exactly the same as the global MCP server example above.

How to use the MCP server

Once the server is installed, you might need to head back to Settings > MCP and click the refresh button.

The Cursor agent will then be able to see the available tools the added MCP server has available and will call them when it needs to.

You can also explictly ask the agent to use the tool by mentioning the tool name and describing what the function does.