just-every/mcp-screenshot-website-fast

Captures full-page website screenshots and automatically tiles them into 1072x1072 pixel chunks (1.15 megapixels) using Sharp image processing, optimizing for Claude Vision API's token efficiency and visual processing constraints. The system constrains all viewport dimensions to maximum 1072x1072 to ensure each tile fits within optimal vision model input boundaries without requiring external image resizing or post-processing.

Implements multiple wait strategies (networkIdle, domContentLoaded, custom JavaScript conditions) to ensure dynamic content has fully loaded before capture, with configurable timeouts and retry logic. The system injects JavaScript probes to detect application-specific readiness conditions (e.g., React hydration, data fetch completion) rather than relying solely on browser network events.

Uses the Sharp image processing library to efficiently tile full-page screenshots into 1072x1072 chunks, handling image format conversion, compression, and metadata extraction. The tiling pipeline processes captured PNG images through Sharp's streaming API, splitting large images into overlapping or non-overlapping tiles based on configuration, and returning tile metadata with coordinate information.

Manages Chromium browser process lifecycle with automatic restart on crash, graceful shutdown on signals (SIGTERM, SIGINT), and connection pooling to reuse browser instances across multiple screenshot operations. The system implements a serve-restart wrapper that monitors the main MCP server process and automatically restarts it if it crashes, maintaining availability for long-running AI agent workflows.

Records time-series screenshots of page interactions as WebP animations with adaptive frame rate selection based on content change detection. The system captures PNG frames at configurable intervals, deduplicates identical frames to reduce file size, and encodes the sequence into WebP animations using Sharp, enabling efficient video-like capture of dynamic page behavior without full video codec overhead.

Captures console output (log, error, warn, info) during page execution with full execution context, including message content, severity level, and timestamp. The system injects a JavaScript listener that intercepts console methods and collects messages over a specified duration, returning structured JSON with all captured messages for analysis by AI models.

Exposes screenshot and screencast capabilities as MCP tools via stdio-based JSON-RPC transport, enabling integration with Claude Code, VS Code, Cursor, and JetBrains IDEs. The system implements the Model Context Protocol specification, serializing tool requests/responses as JSON-RPC messages over stdin/stdout, allowing AI assistants to invoke screenshot operations as native tools.

Provides a command-line interface (bin/mcp-screenshot-website.js) for direct screenshot capture without MCP server overhead, enabling scripting, testing, and manual screenshot operations. The CLI accepts URL, viewport, wait strategy, and output format parameters, executing the screenshot capture engine directly and returning results as files or base64-encoded output.

Allows configuration of browser viewport dimensions (width, height) with automatic constraint enforcement to ensure all viewports are clamped to maximum 1072x1072 pixels. The system validates viewport parameters at request time and rejects or clamps oversized viewports to maintain compatibility with Claude Vision API tiling constraints.

Supports multiple output formats for screenshot results: base64-encoded PNG for embedding in JSON responses (suitable for MCP protocol), and file-based PNG output for CLI and direct file storage. The system handles encoding/decoding transparently based on output format selection, enabling flexible integration with different transport mechanisms.

Implements robust page navigation with automatic retry on transient failures, timeout handling, and detailed error reporting. The system attempts to navigate to the target URL with configurable retry counts and backoff strategies, capturing detailed error information (network errors, timeouts, navigation failures) for debugging and fallback handling.