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Interactive MCP feedback server with Flutter Web UI for AI-assisted development. Collect user feedback, images, and manage sessions in real-time.

Project description

Leo Feedback MCP

A customized MCP (Model Context Protocol) server for interactive feedback collection during AI-assisted development. Built with a Flutter Web frontend and Python/FastAPI backend.

Features

  • Flutter Web UI with dark theme for interactive feedback
  • AI Work Summary panel with Markdown rendering
  • Image support - upload, drag & drop from IDE, paste (PNG, JPG, GIF, BMP, WebP)
  • Auto-submit countdown with configurable prompts
  • Audio & browser notifications when AI requests feedback
  • Session history - persists across browser reloads via backend storage
  • Drag & drop files from IDE (Cursor) into the feedback panel
  • Keyboard shortcuts - Ctrl+Enter / Cmd+Enter for quick submit
  • Smart browser detection (WSL, SSH, local)
  • Session management with auto-cleanup

Architecture

┌─────────────────────────────────────────────┐
│  Cursor / AI Client                         │
│  (calls interactive_feedback MCP tool)      │
└──────────────┬──────────────────────────────┘
               │ MCP Protocol (stdio)
┌──────────────▼──────────────────────────────┐
│  Python Backend                             │
│  ├── FastMCP server (MCP tool definitions)  │
│  ├── FastAPI (HTTP routes + WebSocket)      │
│  └── Session management & image processing  │
└──────────────┬──────────────────────────────┘
               │ HTTP + WebSocket
┌──────────────▼──────────────────────────────┐
│  Flutter Web Frontend                       │
│  ├── Workspace (AI Summary + Feedback)      │
│  ├── Sessions (chat history)                │
│  ├── Settings (auto-submit, timeout)        │
│  └── About                                  │
└─────────────────────────────────────────────┘

Requirements

  • Python 3.11+
  • uv (Python package manager)
  • Flutter 3.x+ (for frontend development/building)

Installation

Option A: Install from PyPI (recommended for users)

pip install leo-feedback-mcp

Or with uv:

uv pip install leo-feedback-mcp

MCP configuration (.cursor/mcp.json):

{
  "mcpServers": {
    "leo-feedback-mcp": {
      "command": "uvx",
      "args": ["leo-feedback-mcp"],
      "timeout": 600,
      "autoApprove": ["interactive_feedback"]
    }
  }
}

Option B: Install from source (for development)

git clone <your-repo-url> ~/Desktop/leo-feedback-mcp
cd ~/Desktop/leo-feedback-mcp
make init
make build

MCP configuration (.cursor/mcp.json):

{
  "mcpServers": {
    "leo-feedback-mcp": {
      "command": "uv",
      "args": ["run", "--directory", "<path-to-leo-feedback-mcp>", "leo-feedback-mcp"],
      "timeout": 600,
      "autoApprove": ["interactive_feedback"]
    }
  }
}

Replace <path-to-leo-feedback-mcp> with the actual path to the cloned repository.

Apply configuration

You can place the MCP config in:

  • Project-level: .cursor/mcp.json in your project root
  • Global: ~/.cursor/mcp.json to apply across all projects

After editing, restart Cursor or reload MCP servers (Cursor Settings > MCP > Reload).

Usage

Once configured, the AI assistant will automatically call interactive_feedback to collect your feedback during conversations. A Web UI will open in your browser where you can:

  1. Review the AI's work summary (Markdown rendered)
  2. Provide text feedback with keyboard shortcuts
  3. Attach images via upload button, drag & drop, or paste
  4. View session history of previous AI interactions
  5. Configure auto-submit prompts and timeout settings

Environment Variables

Variable Description Default
MCP_DEBUG Enable debug logging false
MCP_WEB_HOST Web UI host 127.0.0.1
MCP_WEB_PORT Web UI port 8765

Credits

  • Author: Leo Nguyen

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