🖼️ MCP Screenshot Server

A lightweight, MCP-compatible screenshot microservice built with FastAPI for Windows.
It allows AI agents and automation tools to capture full-screen, region-based, or window-specific screenshots via simple HTTP calls.

!MCP Compatible !License: MIT !Python

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🔧 Features

• 📸 Capture full-screen screenshots
• 🪟 Capture specific window by title
• 🔲 Capture custom regions [x, y, width, height]
• 🧠 MCP-compliant REST API
• 🖼️ Returns images as PNG or base64
• 🚀 Built with FastAPI, ready for production or LLM use

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🧠 Use Cases

• Integrating with LLMs using Model Context Protocol (MCP)
• QA test automation pipelines
• Monitoring and remote capture tools
• Visual logging/debugging tools for agents

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📦 Installation

git clone https://github.com/yourusername/mcp-screenshot-server.git
cd mcp-screenshot-server
python -m venv .venv
source .venv/bin/activate   # or .venv\Scripts\activate on Windows
pip install -r requirements.txt
uvicorn main:app --reload

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🔌 API Usage

POST /screenshot

Request JSON body:

{
  "region": [0, 0, 1280, 720],        // optional
  "window_title": "Untitled - Notepad", // optional
  "base64": true                      // optional (default: false)
}

Response (base64 mode):

{
  "status": "ok",
  "mode": "region",
  "image_format": "base64",
  "image": "<base64-encoded-image>"
}

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🛠️ Tech Stack

• Python 3.11+
• FastAPI
• mss or pyautogui for screenshot
• pillow for image processing
• pygetwindow for window matching (optional)

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📄 License

MIT License.
Feel free to use, fork, and integrate — commercial or personal.
See LICENSE for details.

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📬 Contributing

Pull requests and issues welcome!
Open a PR to add features or improve compatibility across platforms (e.g., Mac/Linux support).

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🙋 FAQ

• Does it work on Linux/macOS?
  Not yet. This version is Windows-focused, but you’re welcome to extend it.

• Is it MCP-certified?
  This project aims to follow the MCP spec as closely as possible for maximum compatibility with LLM agents.

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🧠 Inspired By

• Anthropic’s Model Context Protocol
• Real-world automation use cases powered by LLMs and Python

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