Interactive Feedback MCP - 交互式反馈收集器

Name: Interactive Feedback MCP - 交互式反馈收集器
Author: bulice

@bulice

リポジトリ

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

基本情報

カテゴリ

その他

ライセンス

MIT

ランタイム

python

トランスポート

stdio

公開者

bulice

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"
      ]
    }
  }
}

ツール

ツールは検出されませんでした

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

FAQ from Interactive Feedback MCP - 交互式反馈收集器

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.

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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