Used to download and install the uv package installer, which is recommended for dependency management.
Enables cloning repositories from GitHub to access code for the RAGFlow MCP server.
Provides the runtime environment for the RAGFlow MCP server, requiring Python 3.12 specifically.
Supports configuration management through pyproject.toml for dependency resolution and installation.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@RAGFlow MCPsearch for recent AI research papers about RAG systems"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
ragflow-mcp
Simple RAGFlow MCP. Only useful until the RAGFlow team releases the official MCP server
Installation
We provide two installation methods. Method 2 (using uv) is recommended for faster installation and better dependency management.
Method 1: Using conda
Create a new conda environment:
Clone the repository:
Install dependencies:
Method 2: Using uv (Recommended)
Install uv (A fast Python package installer and resolver):
Clone the repository:
Create a new virtual environment and activate it:
Install dependencies:
Run MCP Server Inspector for debugging
Start the MCP server
Start the inspector using the following command: