Below are useful info to host this Agentic RAG app using MCP
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 uvorwhere uv - For windows: It is usually present in
%USERPROFILE%/.local/bin/uv, where%USERPROFILE%resolves to something likec:\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
- Qdrant – Start the container (Step 1 above).
- F1 FAQ collection – Create it once by running the notebook
rag2.ipynb(run the cell that createsf1_faq_collectionand stores embeddings), or run the test script below. - MCP server – Add the server in Cursor settings (Step 3 above) and ensure it shows green status with tools
faq_retrieval_toolandbright_data_web_search_tool.
Test 1: In Cursor chat (recommended)
- Open Cursor chat: Ctrl + L (or Cmd + L on Mac).
- Ask an F1 question, e.g.:
- "Who governs F1 racing?"
- "What is the halo device?"
- "How many points for winning an F1 race?"
- The AI will use
faq_retrieval_toolto get context from your RAG and answer. For non‑F1 topics it may usebright_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.pyThis 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.
