Interactive Feedback MCP

by zengxiaolou

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About

An MCP server for AI-assisted development tools like Cursor and Claude, supporting interactive feedback workflows with AI.

Details

Author
zengxiaolou
Categories
Developer Tools, Automation, AI

Setup

Install Interactive Feedback MCP in your MCP client (Claude Desktop, Cursor, Windsurf, and others).

Repository: https://github.com/zengxiaolou/Interactive-Feedback-MCP

Follow the installation instructions in the repository README, then restart your MCP client.

Interactive Feedback MCP 是一个高性能的 MCP Server,专为CursorClaude DesktopWindsurf等AI开发工具设计。采用现代化三栏布局和毛玻璃效果UI,支持实时交互反馈、多媒体处理和智能项目分析。

- 🔄 实时双向对话- AI助手可暂停并请求用户澄清,避免猜测性开发
- 🎯 预定义选项- 快速选择常用操作,提升开发效率
- 📊 智能分析- 自动分析用户意图、紧急程度和项目上下文
- ⚡ 性能优化- 启动时间<2秒,UI响应<100ms,内存占用<100MB

- 🖼️ 三栏布局- 消息内容(40%) + 智能推荐(40%) + 项目信息(20%)
- ✨ 毛玻璃效果- 深色主题,强制模式,不受系统主题影响
- 🌏 中文优化- 完美支持中文字体和UTF-8编码
- 📱 响应式设计- 适配不同屏幕尺寸和DPI设置

- 🏗️ MCP协议- 基于FastMCP框架的标准化工具调用
- 🎯 PySide6 UI- 现代化Qt界面框架
- ⚡ 性能监控- 内置性能跟踪和优化系统
- ⚙️ 配置管理- 统一配置系统,支持主题切换和个性化设置

- 每次提示消耗API额度,成本高昂
- 基于猜测的开发导致错误代码
- 单向交互,无法及时澄清需求
- 迭代效率低,调试时间长

- 🔄 AI可在单次请求内多轮交互
- 💰 工具调用不计入API使用量
- ✅ 确认后再执行,减少错误
- 🚀 效率提升5倍以上

- Python: 3.10+
- 系统: Windows 10+, macOS 12+, Ubuntu 20.04+
- 内存: 建议4GB+
- 存储: 500MB可用空间

# macOS/Linux curl -LsSf https://astral.sh/uv/install.sh | sh # Windows (PowerShell) powershell -c "irm https://astral.sh/uv/install.ps1 | iex" # 或者使用 pip pip install uv
git clone https://github.com/your-username/interactive-feedback-mcp.git cd interactive-feedback-mcp
# 测试MCP服务器 uv run server.py # 测试UI界面 uv run enhanced_feedback_ui.py --prompt "测试消息" --output-file test.json
{ "mcpServers": { "interactive-feedback": { "command": "uv", "args": [ "--directory", "/absolute/path/to/interactive-feedback-mcp", "run", "server.py" ], "timeout": 600, "autoApprove": ["interactive_feedback"] } } }
{ "mcpServers": { "interactive-feedback": { "command": "uv", "args": [ "--directory", "/absolute/path/to/interactive-feedback-mcp", "run", "server.py" ], "timeout": 600 } } }

- 使用绝对路径确保正确找到项目
- Windows用户请使用正斜杠/或双反斜杠\\
- 配置后需要重启AI客户端

Cursor Settings > Rules for AI中添加:

# Interactive Feedback MCP 使用规则 ## 强制交互协议 - 收到用户消息后,必须先调用 interactive_feedback 工具进行智能分析 - 提供预定义选项供用户快速选择 - 执行操作前必须获得用户确认 - 完成任务后询问是否需要进一步操作 ## 使用场景 - 需求不明确时:询问澄清 - 有多种实现方案时:提供选项 - 重要操作前:请求确认 - 任务完成后:询问后续需求 ## 格式示例

创建~/.interactive_feedback_mcp/config.json

{ "ui": { "theme": "enhanced_glassmorphism", "language": "zh_CN", "font_family": "PingFang SC", "font_size": 14, "window_width": 1400, "window_height": 1200, "panel_ratios": [40, 40, 20] }, "performance": { "max_startup_time": 2.0, "max_response_time": 100.0, "max_memory_usage": 100.0, "enable_monitoring": true } }

- 📊 多级别日志记录(DEBUG, INFO, WARNING, ERROR, CRITICAL)
- 🔄 文件轮转和大小控制(默认10MB轮转,保留5个备份)
- ⚡ 性能监控(记录操作耗时,识别慢操作)
- 📋 项目上下文记录(自动记录项目信息和Git状态)
- 🐛 错误详情追踪(包含堆栈信息和上下文)
- 👁️ 实时日志监控

logs/ ├── interactive_feedback_mcp.log # 主日志文件 ├── errors.log # 错误日志 ├── performance.log # 性能日志 └── project_context.log # 项目上下文日志
# 查看日志摘要 python manage_logs.py summary # 查看最近50行主日志 python manage_logs.py view --type main --lines 50 # 查看错误日志 python manage_logs.py view --type error # 搜索日志内容 python manage_logs.py search "错误关键词" --type all # 分析错误统计 python manage_logs.py analyze # 实时监控日志 python manage_logs.py monitor # 清理30天前的日志 python manage_logs.py cleanup --days 30 # 导出日志文件 python manage_logs.py export --output logs_backup.zip # 配置日志级别 python manage_logs.py config --level DEBUG

- 📝 日志级别和格式
- 📦 文件大小和轮转设置
- 🖥️ 控制台输出控制
- ⏱️ 性能监控阈值
- 🧹 自动清理策略

python manage_logs.py view --type performance
python manage_logs.py view --type context
python manage_logs.py search "UI启动失败" --type all
请使用 interactive_feedback 询问我想要什么类型的API设计

- 左栏(40%):消息内容和用户输入
- 中栏(40%):AI智能推荐和选项
- 右栏(20%):项目信息和Git状态

- Ctrl+Enter:提交反馈
- Escape:取消操作
- Ctrl+1-5:快速选择预定义选项
- Ctrl+/:显示帮助信息

interactive-feedback-mcp/ ├── server.py # MCP服务器入口 ├── enhanced_feedback_ui.py # UI主程序 ├── rules.md # 开发规范文档 ├── pyproject.toml # 项目配置 ├── ui/ # UI组件模块 │ ├── components/ # 核心组件 │ │ ├── three_column_layout.py # 三栏布局 │ │ ├── enhanced_markdown_renderer.py # 渲染引擎 │ │ └── main_window.py # 主窗口 │ ├── styles/ # 样式主题 │ │ └── enhanced_glassmorphism.py # 毛玻璃主题 │ ├── utils/ # 工具模块 │ │ ├── performance.py # 性能监控 │ │ └── config_manager.py # 配置管理 │ └── widgets/ # 自定义控件 └── tests/ # 测试文件
# 安装开发依赖 uv sync --dev # 运行测试 uv run python -m pytest tests/ # 代码格式化 uv run python -m black . # 类型检查 uv run python -m mypy .

- 启动时间: < 2秒
- UI响应: < 100毫秒
- 内存使用: < 100MB (空闲状态)
- CPU占用: < 5% (空闲状态)

# 检查编码设置 python -c "import locale; print(locale.getpreferredencoding())" # 强制UTF-8 export PYTHONIOENCODING=utf-8
# 重置配置 rm ~/.interactive_feedback_mcp/config.json # 重新启动应用 uv run enhanced_feedback_ui.py --prompt "测试" --output-file test.json
# 检查性能指标 uv run python -c " from ui.utils.performance import global_performance_monitor monitor = global_performance_monitor monitor.start_monitoring() print(monitor.get_current_metrics()) "

- 检查路径配置是否正确
- 确认uv命令可用
- 查看客户端日志错误信息
- 验证server.py可正常运行
- Fork 项目
- 创建功能分支:git checkout -b feature/new-feature
- 遵循开发规范
- 编写测试用例
- 提交PR

- 遵循rules.md中的开发规则
- 使用Black进行代码格式化
- 保持测试覆盖率 > 80%
- 编写清晰的提交消息

- FastMCP- MCP框架支持
-
PySide6- UI框架
-
Cursor- AI开发工具
-
Claude- AI助手

- 问题反馈:GitHub Issues
- 功能建议:
GitHub Discussions

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