ragflow-mcp-server-continue MCP server

RAGFlow API MCP Server，可以查找知识库和聊天。

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

4. retrieve

   • 检索相关信息
   • 输入：
     • dataset_ids: 数据集的 ID
     • question: 提问内容
   • 返回从知识库检索到的内容

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

<details> <summary>Development/Unpublished Servers Configuration</summary>

"mcpServers": {
  "ragflow-mcp-server-continue": {
    "command": "uv",
    "args": [
      "--directory",
      "D:\AIGC\Projects\ragflow-mcp-server-continue",
      "run",
      "ragflow-mcp-server-continue"
    ]
  }
}

</details>

<details> <summary>Published Servers Configuration</summary>

"mcpServers": {
  "ragflow-mcp-server-continue": {
    "command": "uvx",
    "args": [
      "ragflow-mcp-server-continue"
    ]
  }
}

</details>

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 ragflow-mcp-server-continue run ragflow-mcp-server-continue

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