🖥️ Desktop MCP

A Model Context Protocol (MCP) server for desktop operations, providing AI assistants with the ability to capture and analyze screen content across multi-monitor setups.

Features

• 📸 Multi-Monitor Screenshot Support: Capture screenshots from any region across all connected displays

• 🖥️ Screen Information: Get detailed information about all connected monitors (resolution, position, dimensions)

• 🎨 Smart Image Optimization: Automatic compression and resizing for AI context efficiency

• 🔄 Dual Mode Operation: Run as an MCP server or as a standalone web API

• ⚡ FastAPI Powered: Built on modern, fast, and well-documented FastAPI framework

Installation

Prerequisites

• Python 3.8 or higher

• Windows, macOS, or Linux

Setup

1. Clone the repository:

git clone https://github.com/yourusername/desktop-mcp.git
cd desktop-mcp

2. Install dependencies:

pip install -r requirements.txt

Usage

MCP Mode (Default)

Run as an MCP server for use with AI assistants like Claude Desktop:

python -m app.main

Web Mode

Run as a standalone web API with interactive documentation:

python -m app.main --web

This will:

• Start the server at http://localhost:8000

• Automatically open the interactive API docs in your browser

• Enable live reload for development

Configuration

Adding to Claude Desktop

Add this configuration to your Claude Desktop MCP settings file (typically at ~/.cursor/mcp.json or %APPDATA%/.cursor/mcp.json):

{
  "mcpServers": {
    "Desktop MCP": {
      "command": "python",
      "args": ["-m", "app.main"],
      "cwd": "/path/to/desktop-mcp"
    }
  }
}

API Reference

Endpoints

GET /desktop/screens

Get information about all connected monitors.

Response:

[
  {
    "x": 0,
    "y": 0,
    "width": 1920,
    "height": 1080,
    "name": "\\\\.\\DISPLAY1",
    "is_primary": true,
    "width_mm": 527,
    "height_mm": 296
  }
]

POST /desktop/screenshot

Capture a screenshot of a specific region.

Parameters:

• x (int): X coordinate of top-left corner

• y (int): Y coordinate of top-left corner

• width (int): Width of capture region

• height (int): Height of capture region

• context_mode (string, optional): Image quality mode

  • minimal (default): 600px max, 30% quality - for basic UI detection

  • normal: 800px max, 50% quality - for detailed UI inspection

  • detailed: 1200px max, 70% quality - for pixel-perfect UI analysis

Request Body:

{
  "x": 0,
  "y": 0,
  "width": 1920,
  "height": 1080
}

Response:

{
  "context": [
    {
      "type": "image",
      "source": {
        "type": "base64",
        "media_type": "image/webp",
        "data": "UklGRi..."
      }
    }
  ]
}

Usage Examples

Example 1: Capture Primary Monitor

import requests

# Get screen info
screens = requests.get("http://localhost:8000/desktop/screens").json()
primary = next(s for s in screens if s["is_primary"])

# Capture primary screen
screenshot = requests.post(
    "http://localhost:8000/desktop/screenshot",
    params={"context_mode": "normal"},
    json={
        "x": primary["x"],
        "y": primary["y"],
        "width": primary["width"],
        "height": primary["height"]
    }
).json()

Example 2: Capture Specific Region

# Capture a 800x600 region starting at position (100, 100)
screenshot = requests.post(
    "http://localhost:8000/desktop/screenshot",
    params={"context_mode": "minimal"},
    json={
        "x": 100,
        "y": 100,
        "width": 800,
        "height": 600
    }
).json()

Example 3: Multi-Monitor Setup

# For a 3-monitor horizontal setup (each 1920x1080):
# Left monitor: x=0, y=0
# Center monitor: x=1920, y=0
# Right monitor: x=3840, y=0

# Capture right monitor
screenshot = requests.post(
    "http://localhost:8000/desktop/screenshot",
    params={"context_mode": "detailed"},
    json={
        "x": 3840,
        "y": 0,
        "width": 1920,
        "height": 1080
    }
).json()

Use Cases with AI Assistants

When integrated with AI assistants like Claude:

• Visual Debugging: "Can you see what error message is on my screen?"

• UI/UX Analysis: "What do you think of this design layout?"

• Tutorial Assistance: "I'm stuck on this step, can you see what I'm doing wrong?"

• Code Review: "Can you review the code visible on my screen?"

• Accessibility Testing: "Is this UI accessible and well-organized?"

Development

Project Structure

desktop-mcp/
├── app/
│   ├── __init__.py
│   ├── main.py              # Application entry point
│   ├── api/
│   │   ├── __init__.py
│   │   └── desktop.py       # Desktop API routes
│   └── schemas/
│       ├── __init__.py
│       ├── enums.py         # Context mode enums
│       ├── rect.py          # Rectangle schema
│       └── screeninfo.py    # Screen info schema
├── requirements.txt
└── README.md

Running Tests

# Run the server in web mode for testing
python -m app.main --web

# Visit http://localhost:8000/docs to test endpoints

Requirements

• fastapi - Modern web framework

• fastmcp - MCP protocol implementation

• uvicorn - ASGI server

• screeninfo - Monitor information retrieval

• pyautogui - Screenshot capture

• pillow - Image processing

• pydantic - Data validation

Security Considerations

⚠️ Important: This tool provides direct access to screen content. When deploying:

• Only expose to trusted networks

• Consider authentication mechanisms for production use

• Be mindful of sensitive information in screenshots

• Use appropriate context modes to minimize data transfer

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

MIT License - feel free to use this project for personal or commercial purposes.

Troubleshooting

Screenshot Capture Fails

• Linux: Ensure you have the required X11 libraries installed

• macOS: Grant screen recording permissions in System Preferences

• Windows: Run with appropriate privileges if capturing protected content

Multi-Monitor Issues

• Use GET /desktop/screens first to verify monitor coordinates

• Remember that coordinates are based on virtual desktop layout

• Monitors may be arranged horizontally, vertically, or in custom configurations

Performance Optimization

• Use minimal context mode for frequent captures

• Capture only the necessary region instead of full screens

• Consider caching screen information instead of querying repeatedly

Support

For issues, questions, or suggestions, please open an issue on GitHub.

Made with ❤️ for enhancing AI assistant capabilities