Screenshot MCP Server

Screenshot MCP Server

The Screenshot MCP Server solves a common pain point for AI assistants that need visual context: capturing what the user is actually seeing on their screen. Traditional chatbot interfaces lack the ability to “look” at a desktop, which limits applications such as remote support, automated UI testing, or visual analytics. By exposing a simple take_screenshot tool over the Model Context Protocol, this server allows an AI assistant to request a snapshot of the user's display and receive it in a ready‑to‑process format, bridging the gap between textual prompts and visual data.

At its core, the server performs three tasks: capture, compress, and transmit. It uses pyautogui to grab the full screen, then leverages Pillow to encode the image as JPEG with configurable quality settings. The resulting binary is base64‑encoded, ensuring safe transport over JSON or SSE streams without corruption. This lightweight payload can be decoded by the client and fed directly into image‑analysis models or displayed in a UI, enabling workflows where the assistant can describe screen contents, spot anomalies, or guide users through complex interfaces.

Key features include:

• Full‑screen capture with a single command, no need for manual screenshot tools.
• Automatic JPEG compression, balancing image fidelity and bandwidth usage.
• Base64 encoding for reliable transmission across all MCP transports (stdio, SSE).
• Dual transport modes: the default stdio interface is ideal for local testing, while an SSE endpoint allows web‑based clients to stream screenshots in real time.
• Configurable quality so developers can trade off sharpness against payload size based on network constraints.

Typical use cases span several domains. In remote support, an AI agent can request a screenshot to identify UI elements or error dialogs, then provide step‑by‑step instructions. In automated testing, a test harness can capture the screen after each test step and feed the image to visual regression tools. Developers building AI‑augmented IDEs can use screenshots to let assistants inspect code editors or debugging windows. Because the server is a pure MCP service, it integrates seamlessly into any existing AI workflow that already consumes tools via the Model Context Protocol.

The standout advantage of this server is its simplicity and portability. With no heavy dependencies beyond standard Python imaging libraries, it runs on Linux, macOS, and Windows. The SSE mode opens the door to browser‑based assistants without requiring a separate WebSocket setup, while the stdio mode keeps local experimentation straightforward. By providing an out‑of‑the‑box screenshot capability, the Screenshot MCP Server empowers developers to create richer, more perceptive AI assistants that can see and act upon the user’s visual environment.