About ragflow-mcp
ragflow-mcp is a lightweight MCP server for RAGFlow, intended as a temporary option before RAGFlow releases its official MCP server. Its README documents setup with conda or uv and debugging with the MCP Inspector.
Key features
- 01RAGFlow MCP server
- 02Installation with conda or uv
- 03MCP Inspector debugging
Use cases
- 01Connect an MCP client to RAGFlow
- 02Debug the server with MCP Inspector
MCP support
It implements an MCP server for RAGFlow. The README provides instructions for launching MCP Inspector to debug it.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEragflow-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:
conda create -n ragflow_mcp python=3.12
conda activate ragflow_mcp
- Clone the repository:
git clone https://github.com/oraichain/ragflow-mcp.git
cd ragflow-mcp
- Install dependencies:
pip install -r requirements.txt
Method 2: Using uv (Recommended)
- Install uv (A fast Python package installer and resolver):
curl -LsSf https://astral.sh/uv/install.sh | sh
- Clone the repository:
git clone https://github.com/oraichain/ragflow-mcp.git
cd ragflow-mcp
- Create a new virtual environment and activate it:
uv venv --python 3.12
source .venv/bin/activate # On Unix/macOS
# Or on Windows:
# .venv\Scripts\activate
- Install dependencies:
uv pip install -r pyproject.toml
Run MCP Server Inspector for debugging
-
Start the MCP server
-
Start the inspector using the following command:
# you can choose a different server
SERVER_PORT=9000 npx @modelcontextprotocol/inspector
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.