# 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
```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
```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Development/Unpublished Servers Configuration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:
```bash
uv sync
```
2. Build package distributions:
```bash
uv build
```
This will create source and wheel distributions in the `dist/` directory.
3. Publish to PyPI:
```bash
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](https://github.com/modelcontextprotocol/inspector).
You can launch the MCP Inspector via [`npm`](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm) with this command:
```bash
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.
