Sethbang MCP Screenshot Server

The MCP Screenshot Server is a lightweight, protocol‑compliant service that exposes screenshot capabilities to AI assistants through the Model Context Protocol. By wrapping Puppeteer behind a simple MCP tool interface, it removes the need for developers to embed headless‑browser logic directly in their AI workflows. Instead, an assistant can request a visual snapshot of any web page or local HTML file with a single tool invocation, letting the server handle navigation, rendering, and image generation.

This MCP server solves a common pain point in AI‑powered development: obtaining up‑to‑date visual representations of web content for analysis, debugging, or documentation. Traditional approaches require a separate headless‑browser process, manual screenshot commands, and complex error handling. The MCP server consolidates these responsibilities into a single, stateless endpoint that accepts declarative parameters—URL, viewport size, full‑page flag, and optional output path—and returns a PNG file. This abstraction allows assistants to reason about visual states without worrying about browser configuration, making it easier to generate UI reports, compare design changes, or validate rendering across environments.

Key features are intentionally straightforward yet powerful:

• Universal URL support – capture any HTTP, HTTPS, or local resource.
• Configurable viewport – set width and height within realistic ranges to mimic different devices or test scenarios.
• Full‑page capture – scrollable pages can be rendered in a single image, useful for regression testing or content audits.
• Custom output paths – specify where the PNG should be stored, enabling organized artifact management.
• Automatic directory handling – the server creates necessary folders on demand, eliminating manual setup steps.

Typical use cases include:

• UI regression testing – an AI assistant can request screenshots before and after code changes, compare pixel differences, and flag regressions.
• Documentation generation – automatically embed current page snapshots into technical docs or changelogs.
• Accessibility analysis – capture visual states for screen‑reader simulation tools or color contrast checkers.
• Web scraping validation – verify that a scraper correctly renders dynamic content by comparing screenshots.

Integration is seamless with any MCP‑compliant client. The assistant simply calls the tool, passing the desired options; the server returns a PNG path or data URI that can be embedded in responses, stored for later reference, or passed to downstream services. Because the server is stateless and relies on a proven headless‑browser library, it scales horizontally with minimal overhead.

What sets this MCP Screenshot Server apart is its protocol‑first design coupled with a minimal API surface. Developers can plug it into existing AI pipelines without rewriting rendering logic, while the server itself remains agnostic to specific front‑end frameworks or deployment environments. Its focus on ease of use, configurability, and robust file handling makes it a practical addition to any AI‑enabled development workflow that requires reliable visual capture.