mcp_analyze_quality
分析自己的性能数据
Documentation
🚀 MCP性能分析服务器
> 移动应用性能监控数据智能分析工具,专注于严重问题检测
📋 快速开始
1. 获取项目
git clone git@github.com:DaSheng1994/mcp_analyze_quality.git
cd mcp_analyze_quality2. 安装依赖
# 创建虚拟环境
python3 -m venv .venv
source .venv/bin/activate # Linux/macOS
# 安装依赖
pip install -r requirements.txt3. 配置Cursor MCP
编辑 `~/.cursor/mcp.json`:
{
"mcpServers": {
"performance-analyzer": {
"command": "/path/to/your/project/.venv/bin/python",
"args": ["/path/to/your/project/main.py"],
"cwd": "/path/to/your/project"
}
}
}4. 重启Cursor并使用
完全退出并重新启动Cursor,然后在对话中输入:
分析这个性能数据:http://localhost:8000/meminfo.csv🌐 远程部署
服务器端部署
# 在服务器上部署
git clone git@github.com:DaSheng1994/mcp_analyze_quality.git
cd mcp_analyze_quality
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
# 启动服务(后台运行)
nohup python main.py > mcp.log 2>&1 &
nohup python3 -m http.server 8000 > http.log 2>&1 &客户端配置
团队成员在各自的Cursor中配置:
{
"mcpServers": {
"performance-analyzer": {
"command": "ssh",
"args": ["your-server", "cd /path/to/mcp_analyze_quality && .venv/bin/python main.py"],
"env": {}
}
}
}使用远程服务
分析这个性能数据:http://your-server-ip:8000/meminfo.csv📊 功能特性
- 严重问题检测: 专注于识别需要立即处理的严重性能问题
- 简洁输出: 只返回严重警告信息,避免信息过载
- 智能分析: 基于预定义规则进行精准判断
- 易于集成: 轻量级MCP服务器,快速部署
🚨 严重警告规则
当前支持的严重警告检测:
- 物理内存警告: VmRSS超过1.3GB时触发
- Views数量警告: Views增长超过700个时触发
📝 自定义规则
可以通过修改 `.cursor/rules/quality-rules.mdc` 文件来自定义分析规则。
🚀 现在就开始使用MCP性能分析工具吧!
Frequently asked questions
What is mcp_analyze_quality?
mcp_analyze_quality is 分析自己的性能数据
How do I install mcp_analyze_quality?
Open the GitHub repository and follow its README. Most MCP servers are added to your client's MCP config, then called by your agent.
Is mcp_analyze_quality open source?
Yes — it is hosted on GitHub at https://github.com/DaSheng1994/mcp_analyze_quality.
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