MCP Screenshot Server

Overview

The MCP Screenshot Server is a lightweight, Windows‑only microservice that exposes a Model Context Protocol (MCP) compliant REST API for capturing screenshots. It solves the common pain point of programmatically grabbing visual information from a desktop environment—whether for debugging, monitoring, or enabling AI agents to “see” their surroundings. By abstracting the complexity of screen capture into a single HTTP endpoint, developers can quickly integrate visual data into LLM workflows without writing platform‑specific code.

At its core, the server offers three intuitive capture modes: full screen, a named window, or an arbitrary rectangular region specified by pixel coordinates. The output can be delivered as a raw PNG file or base64‑encoded string, allowing seamless embedding in JSON payloads that AI assistants consume. Because it follows the MCP spec, any LLM or agent that understands MCP can invoke the service using standard tool calls, making it a drop‑in solution for Claude, GPT‑4o, or custom agents built on Anthropic’s framework.

Key capabilities include:

• FastAPI foundation for low‑latency, production‑ready deployment.
• Cross‑platform image handling via or , with optional window matching through .
• Flexible response formats (PNG or base64) to accommodate diverse integration scenarios.
• Simple JSON request schema, enabling straightforward tooling and automation scripts.

Typical use cases span from automated QA pipelines that need visual regression checks, to remote monitoring dashboards where an AI assistant can fetch and analyze screenshots on demand. In LLM‑driven workflows, agents can request a screenshot of a specific application window (e.g., “capture the current state of Notepad”) and then process or describe the image, enriching conversational context with real‑time visual data.

The server’s design prioritizes minimalism and MCP compliance, giving developers a reliable, well‑documented interface that plugs directly into existing AI toolchains. Its modular architecture also invites extensions—adding macOS/Linux support or advanced image analysis hooks—making it a versatile component in any AI‑enhanced automation stack.