Qinglong MCP Server
# 青龙面板 MCP Server
[](https://badge.fury.io/py/qinglong-mcp-server)
这是一个 Model Context Protocol (MCP) server,用于查询和执行青龙面板中的定时任务。
> **说明**:本仓库包含源代码,供开发者参考。普通用户请直接通过 pip 或 uvx 安装使用。
## 功能
- `list_qinglong_tasks`: 查询青龙面板中的所有定时任务列表
- `run_task`: 执行任务并等待完成,自动返回执行日志(最多等待30秒)
- `run_task_async`: 异步启动任务,不等待执行完成
- `get_task_logs`: 获取青龙面板中指定任务的执行日志
- `get_task_status`: 获取青龙面板中指定任务的执行状态
- `list_subscriptions`: 查询青龙面板中的所有订阅列表
- `run_subscription`: 运行指定的订阅
## 安装
使用 pip 安装:
```bash
pip install qinglong-mcp-server
```
或使用 uvx(推荐,无需安装):
```bash
uvx qinglong-mcp-server
```
## 配置
首次运行时会自动创建配置文件模板。
编辑配置文件:
**macOS/Linux:**
```bash
nano ~/.qinglong-mcp/.env
```
**Windows:**
```cmd
notepad %USERPROFILE%\.qinglong-mcp\.env
```
填入你的青龙面板信息:
```
QINGLONG_URL=https://your-qinglong-url.com
CLIENT_ID=your_client_id
CLIENT_SECRET=your_client_secret
```
## 使用
### 在 MCP 客户端中使用
编辑 MCP 配置文件,添加以下内容:
```json
{
"mcpServers": {
"qinglong": {
"command": "uvx",
"args": ["qinglong-mcp-server"]
}
}
}
```
配置文件位置(以 Kiro CLI 为例):
- `~/.kiro/settings/mcp.json`
### 开发测试
运行测试脚本:
```bash
./test_query_tasks.py
```
## 升级
```bash
pip install -U qinglong-mcp-server
```
## 项目地址
- PyPI: https://pypi.org/project/qinglong-mcp-server/
- GitHub: https://github.com/pholex/qinglong-mcp-server
## 联系方式
Email: pholex@gmail.com
TDQS
Scored across 7 tools
Each tool has a clearly distinct purpose with no ambiguity. Tools target specific resources (tasks, subscriptions) and actions (get logs, get status, list, run), ensuring agents can easily differentiate between them.
All tool names follow a consistent verb_noun pattern (e.g., get_task_logs, list_qinglong_tasks, run_subscription). The naming is uniform and predictable, making the set easy to navigate.
With 7 tools, the server is well-scoped for managing Qinglong tasks and subscriptions. Each tool serves a clear function, covering core operations without being overly sparse or bloated.
The tool set provides strong coverage for task and subscription management, including listing, running, and monitoring. A minor gap exists in update or delete operations for tasks and subscriptions, but agents can still handle most workflows effectively.