What is Ragflow Mcp Server

ragflow-mcp-server is an API server that enables knowledge base search and chat functionalities, allowing users to interact with a chat assistant based on a dataset.

Use cases

Use cases include building automated customer service bots, educational tutoring systems, and interactive knowledge bases for organizations.

How to use

To use ragflow-mcp-server, you need to install it and configure it with your API key and base URL. You can create a chat assistant using the ‘create_chat’ tool and interact with it using the ‘chat’ tool.

Key features

Key features include listing datasets, creating chat assistants, and enabling interactive conversations with the chat assistant.

Where to use

ragflow-mcp-server can be used in various fields such as customer support, educational platforms, and any application requiring knowledge retrieval and conversational AI.

RAGFlow MCP Server

RAGFlow API MCP Server，可以查找知识库和聊天。

下载 MCP 开发文档和 RAGFlow API 参考：

wget https://modelcontextprotocol.io/llms-full.txt -O docs/mcp-llms-full.txt
wget https://github.com/infiniflow/ragflow/raw/refs/heads/main/docs/references/python_api_reference.md -O docs/ragflow-python_api_reference.md

Components

Tools

1. list_datasets

   • 列出所有数据集
   • 返回数据集的 ID 和名称

2. create_chat

   • 创建一个新的聊天助手
   • 输入：
     • name: 聊天助手的名称
     • dataset_id: 数据集的 ID
   • 返回创建的聊天助手的 ID、名称和会话 ID

3. chat

   • 与聊天助手进行对话
   • 输入：
     • session_id: 聊天助手的会话 ID
     • question: 提问内容
   • 返回聊天助手的回答

Configuration

[TODO: Add configuration details specific to your implementation]

Quickstart

Install

GitHub Copilot

.vscode/mcp.json

{
  "servers": {
    "ragflow-mcp-server": {
      "command": "uvx",
      "args": [
        "ragflow-mcp-server",
        "--api-key=ragflow-dhMzViYzJlMTM1NjExZjBiNWU5MDI0Mm",
        "--base-url=http://172.16.33.66:8060"
      ]
    }
  }
}

Continue

config.yaml

mcpServers:
  - name: RAGFlow Server
    command: uvx
    args:
      - ragflow-mcp-server
      - --api-key
      - ragflow-dhMzViYzJlMTM1NjExZjBiNWU5MDI0Mm
      - --base-url
      - http://172.16.33.66:8060

Claude Desktop

On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json
On Windows: %APPDATA%/Claude/claude_desktop_config.json

Development

Building and Publishing

To prepare the package for distribution:

1. Sync dependencies and update lockfile:

uv sync

2. Build package distributions:

uv build

This will create source and wheel distributions in the dist/ directory.

3. Publish to PyPI:

uv publish

Note: You’ll need to set PyPI credentials via environment variables or command flags:

• Token: --token or UV_PUBLISH_TOKEN
• Or username/password: --username/UV_PUBLISH_USERNAME and --password/UV_PUBLISH_PASSWORD

Debugging

Since MCP servers run over stdio, debugging can be challenging. For the best debugging
experience, we strongly recommend using the MCP Inspector.

You can launch the MCP Inspector via npm with this command:

npx @modelcontextprotocol/inspector \
  uv --directory /Users/junjian/GitHub/wang-junjian/ragflow-mcp-server \
  run ragflow-mcp-server \
  --api-key ragflow-dhMzViYzJlMTM1NjExZjBiNWU5MDI0Mm \
  --base-url http://172.16.33.66:8060

Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.