MCP-powered Agentic RAG using Firecrawl and Qdrant

This project implements Agentic RAG using Firecrawl and Qdrant.

• Firecrawl is used to scrape data from the web
• Qdrant as the local vector database.
• Cursor IDE as the MCP client.

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Setup and installations

Get Firecrawl API Key:

• Go to Firecrawl and sign up for an account.
• You will find your API key there.
• Store it in the .env file.

FIRECRAWL_API_KEY="..."

Install Dependencies: Ensure you have Python 3.11 or later installed.

pip install firecrawl-py mcp qdrant-client

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Run the project

First, start a Qdrant docker container as follows (make sure you have downloaded Docker):

docker run -p 6333:6333 -p 6334:6334 \
-v $(pwd)/qdrant_storage:/qdrant/storage:z \
qdrant/qdrant

Next, go to the notebook.ipynb file, run the code to create a collection in your vector database.

Finally, set up your local MCP server as follows:

• Go to Cursor settings
• Select MCP
• Add new global MCP server.

In the JSON file, add this:

{
  "mcpServers": {
      "mcp-rag-app": {
          "command": "python",
          "args": ["/absolute/path/to/server.py"],
          "host": "127.0.0.1",
          "port": 8080,
          "timeout": 30000
      }
  }
}

Done! You can now interact with your vector database and fallback to web search if needed.

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Contribution