McpRAG

Step 1: Start the Qdrant container

Start the QDrant container

docker run -p 6333:6333 -p 6334:6334 -v qdrant_storage:/qdrant/storage:z qdrant/qdrant

Step 2: Set up Bright data account.

Open a free account in brightdata and setup a user-email and password. You will need this inside the server2.py.

Step 3: Start the MCP server.

Clone the repo and open it in cursor IDE. Then go to settings > Cursor settings > MCP Servers. Click on 'Add new MCP server' and add the following code (assuming you have no other server running) to mcp.json.

To know the location of 'uv'

• For Mac / Linux: Use which uv or where uv
• For windows: It is usually present in %USERPROFILE%/.local/bin/uv, where %USERPROFILE% resolves to something like c:\Users\username.

{
  "mcpServers": {
    "mcpRAG": {
      "command": "path/to/uv",
      "args": [
        "--directory",
        "absolute/path/to/projectdir",
        "run",
        "server2.py"
      ]
    }
  }
}

It should show the status in green and display the tools: f1_faq_search_tool and bright_data_web_search_tool.

You can now open the chat in cursor (Ctrl + L) and ask questions.

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How to test your RAG app with MCP

Prerequisites

1. Qdrant – Start the container (Step 1 above).
2. F1 FAQ collection – Create it once by running the notebook rag2.ipynb (run the cell that creates f1_faq_collection and stores embeddings), or run the test script below.
3. MCP server – Add the server in Cursor settings (Step 3 above) and ensure it shows green status with tools faq_retrieval_tool and bright_data_web_search_tool.

Test 1: In Cursor chat (recommended)

1. Open Cursor chat: Ctrl + L (or Cmd + L on Mac).
2. Ask an F1 question, e.g.:
   • "Who governs F1 racing?"
   • "What is the halo device?"
   • "How many points for winning an F1 race?"
3. The AI will use faq_retrieval_tool to get context from your RAG and answer. For non‑F1 topics it may use bright_data_web_search_tool (requires Bright Data credentials in .env).

Test 2: Local script (no Cursor)

From the project directory run:

uv run test_rag_mcp.py

This creates f1_faq_collection if needed, then runs a sample FAQ query and prints the retrieved context so you can verify the RAG pipeline without opening Cursor.