mcp-screenshot-server

# 🖼️ 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.    --- ## 🔧 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 --- ## 🧠 Use Cases - Integrating with LLMs using [Model Context Protocol (MCP)](https://github.com/anthropic-ai/model-context-protocol) - QA test automation pipelines - Monitoring and remote capture tools - Visual logging/debugging tools for agents --- ## 📦 Installation ```bash 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 ``` --- ## 🔌 API Usage ### `POST /screenshot` **Request JSON body:** ```json { "region": [0, 0, 1280, 720], // optional "window_title": "Untitled - Notepad", // optional "base64": true // optional (default: false) } ``` **Response (base64 mode):** ```json { "status": "ok", "mode": "region", "image_format": "base64", "image": "<base64-encoded-image>" } ``` --- ## 🛠️ Tech Stack - Python 3.11+ - FastAPI - `mss` or `pyautogui` for screenshot - `pillow` for image processing - `pygetwindow` for window matching (optional) --- ## 📄 License MIT License. Feel free to use, fork, and integrate — commercial or personal. See [LICENSE](LICENSE) for details. --- ## 📬 Contributing Pull requests and issues welcome! Open a PR to add features or improve compatibility across platforms (e.g., Mac/Linux support). --- ## 🙋 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. --- ## 🧠 Inspired By - Anthropic’s [Model Context Protocol](https://github.com/anthropic-ai/model-context-protocol) - Real-world automation use cases powered by LLMs and Python --- ``` Would you like me to tailor a specific section to emphasize AI agent use (e.g., “how to use with o1 or GPT-4o via plugin”)?