ragflow-mcp-server-continue MCP server
RAGFlow API MCP Server，可以查找知识库和聊天。
Components
Tools
-
list_datasets
- 列出所有数据集
- 返回数据集的 ID 和名称
-
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
-
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
-
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
Development/Unpublished Servers Configuration
``` "mcpServers": { "ragflow-mcp-server-continue": { "command": "uv", "args": [ "--directory", "D:\AIGC\Projects\ragflow-mcp-server-continue", "run", "ragflow-mcp-server-continue" ] } } ```Published Servers Configuration
``` "mcpServers": { "ragflow-mcp-server-continue": { "command": "uvx", "args": [ "ragflow-mcp-server-continue" ] } } ```Development
Building and Publishing
To prepare the package for distribution:
- Sync dependencies and update lockfile:
uv sync
- Build package distributions:
uv build
This will create source and wheel distributions in the dist/ directory.
- Publish to PyPI:
uv publish
Note: You'll need to set PyPI credentials via environment variables or command flags:
- Token:
--tokenorUV_PUBLISH_TOKEN - Or username/password:
--username/UV_PUBLISH_USERNAMEand--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.