交互式用户反馈MCP
概览
由Fábio Ferreira（@fabiomlferreira年）开发。更多AI开发增强内容请关注 dotcursorrules.com。 简单的MCP服务器，支持AI辅助开发工具如Cursor中的人工流程。该服务器允许你执行命令、查看输出，并直接向AI提供文本反馈。它还兼容Cline和Windsurf。 为了获得最佳效果，可以在你的AI助手中添加以下内容，你应该在规则上或直接在提示词中添加（例如光标）： 每当你想提问时，总是interactive_feedback地拨打MCP。每当你准备完成用户请求时，直接拨打MCP的interactive_feedback，而不是简单地结束流程。不断调用MCP，直到用户反馈为空，然后结束请求。 这样可以确保你的AI助手在标记任务完成前，利用该MCP服务器请求用户反馈。
概述（AI 翻译）
由Fábio Ferreira（@fabiomlferreira年）开发。 更多AI开发改进内容请查看 dotcursorrules.com。
简单的MCP服务器，支持AI辅助开发工具如Cursor中的人工流程。该服务器允许你执行命令、查看输出，并直接向AI提供文本反馈。它还兼容Cline和Windsurf。
提示工程
为了获得最佳效果，可以在你的AI助手中添加以下内容，你应该在规则上或直接在提示词中添加（例如光标）：
每当你想提问时，务必致电MCP
interactive_feedback。 每当你准备完成用户请求时，呼叫MCPinteractive_feedback，而不是简单地结束流程。 不断调用 MCP，直到用户反馈为空，然后结束请求。
这样可以确保你的AI助手在标记任务完成前，利用该MCP服务器请求用户反馈。
💡 为什么要用这个？
通过引导助手与用户联机，而不是扩展到投机性、高成本的工具调用，该模块可以大幅减少高级请求数量（例如：
本页由 AI 翻译，技术细节请以原文为准。
README
以下为原文 README 节选，完整内容请查看原文。
Interactive Feedback MCP
Developed by Fábio Ferreira (@fabiomlferreira). Check out dotcursorrules.com for more AI development enhancements.
Simple MCP Server to enable a human-in-the-loop workflow in AI-assisted development tools like Cursor. This server allows you to run commands, view their output, and provide textual feedback directly to the AI. It is also compatible with Cline and Windsurf.
Prompt Engineering
For the best results, add the following to your custom prompt in your AI assistant, you should add it on a rule or directly in the prompt (e.g., Cursor):
Whenever you want to ask a question, always call the MCP
interactive_feedback.
Whenever you’re about to complete a user request, call the MCPinteractive_feedbackinstead of simply ending the process. Keep calling MCP until the user’s feedback is empty, then end the request.
This will ensure your AI assistant uses this MCP server to request user feedback before marking the task as completed.
💡 Why Use This?
By guiding the assistant to check in with the user instead of branching out into speculative, high-cost tool calls, this module can drastically reduce the number of premium requests (e.g., OpenAI tool invocations) on platforms like Cursor. In some cases, it helps consolidate what would be up to 25 tool calls into a single, feedback-aware request — saving resources and improving performance.
Configuration
This MCP server uses Qt’s QSettings to store configuration on a per-project basis. This includes:
- The command to run.
- Whether to execute the command automatically on the next startup for that project (see “Execute automatically on next run” checkbox).
- The visibility state (shown/hidden) of the command section (this is saved immediately when toggled).
- Window geometry and state (general UI preferences).
These settings are typically stored in platform-specific locations (e.g., registry on Windows, plist files on macOS, configuration files in ~/.config or ~/.local/share on Linux) under an organization name “FabioFerreira” and application name “InteractiveFeedbackMCP”, with a unique group for each project directory.
The “Save Configuration” button in the UI primarily saves the current command typed into the command input field and the state of the “Execute automatically on next run” checkbox for the active project. The visibility of the command section is saved automatically when you toggle it. General window size and position are saved when the application closes.
安装
This server does not publish a one-line install command.
Open the repository installation guide 新手？先看 mcpskill.org 教程 →配置
{
"mcpServers": {
"interactive-feedback-mcp": {
"command": "uv",
"args": [
"--directory",
"/Users/fabioferreira/Dev/scripts/interactive-feedback-mcp",
"run",
"server.py"
],
"timeout": 600,
"autoApprove": [
"interactive_feedback"
]
}
}
}