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Interactive Feedback MCP - 交互式反馈收集器

@bulice

リポジトリ

基本情報

カテゴリ

その他

ライセンス

MIT

トランスポート

stdio

作者

bulice

Interactive Feedback MCP - 交互式反馈收集器 について

Interactive Feedback MCP Server - A tool for collecting user feedback with PySide6 interface

#interactive-feedback#feedback-collector#ai-assistant

Interactive Feedback MCP - 交互式反馈收集器 の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "interactive-feedback-mcp": {
      "command": "uv",
      "args": [
        "sync"
      ]
    }
  }
}

Interactive Feedback MCP - 交互式反馈收集器 のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

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

よくある質問

1

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`.

2

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`.

3

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.

4

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.

5

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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