What are the system requirements?
Python 3.11 or higher, and the server runs on Windows, macOS, and Linux. The recommended package manager is `uv`.
Interactive Feedback MCP - 交互式反馈收集器
@bulice
基本信息
关于 Interactive Feedback MCP - 交互式反馈收集器
Interactive Feedback MCP Server - A tool for collecting user feedback with PySide6 interface
Interactive Feedback MCP - 交互式反馈收集器 配置
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"interactive-feedback-mcp": {
"command": "uv",
"args": [
"sync"
]
}
}
}Interactive Feedback MCP - 交互式反馈收集器 工具
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当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。
工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
概览
What is Interactive Feedback MCP - 交互式反馈收集器?
Interactive Feedback MCP - 交互式反馈收集器 is an MCP (Model Context Protocol) server that provides a graphical user interface for human-in-the-loop feedback during AI-assisted development. It allows users to submit text feedback, upload images, execute commands, and see real-time output, enabling collaborative development with AI tools like Cursor, Cline, and Windsurf.
How to use Interactive Feedback MCP - 交互式反馈收集器?
Clone the repository, install dependencies with uv sync or pip install -r requirements.txt, then run uv run server.py or python server.py. Configure the server in your AI tool’s MCP settings (e.g., Cursor’s mcpServers JSON) with the path to mcp_server.sh. The AI assistant can call the interactive_feedback tool to prompt the user for input.
Key features of Interactive Feedback MCP - 交互式反馈收集器
- Two-way text feedback with AI assistants via a GUI
- Multi-image upload or clipboard paste support
- Real-time command execution with live output and process monitoring
- Dark and light theme switching with responsive design
- Project-specific configuration persistence using Qt QSettings
- Command history and auto-execute on startup option
Use cases of Interactive Feedback MCP - 交互式反馈收集器
- Reducing speculative high-cost tool calls by confirming intent with the user before proceeding
- Collecting detailed human feedback during iterative code generation or debugging sessions
- Enabling visual feedback by attaching screenshots or diagrams to AI conversations
- Running and reviewing command outputs interactively within the AI development workflow
常见问题
How do I configure the server for Cursor?
Add a JSON entry under `mcpServers` in Cursor’s MCP configuration, specifying the command as the full path to `mcp_server.sh`, with `timeout` 600 and `autoApprove` for `interactive_feedback`.
What tools does the MCP server expose?
It provides the `interactive_feedback` tool for text/image feedback and the `get_image_info` tool for image details.
Where are configuration settings stored?
Settings are stored per project using Qt’s `QSettings` in platform‑specific locations (e.g., Windows registry, macOS plist, Linux config files). This includes command to run, auto‑execute flag, window geometry, and UI state.
Is there a diagnostic tool available?
Yes, a `diagnose_mcp.py` script is included to check MCP server connectivity, verify dependencies, and generate configuration suggestions.
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