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Read Website Fast

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Fast, token-efficient web content extraction that converts websites to clean Markdown. Features Mozilla Readability, smart caching, polite crawling with robots.txt support, and concurrent fetching with minimal dependencies.

πŸ”₯πŸ”₯πŸ”₯πŸ”₯βœ“ VerifiedFreeQuick setup

@just-every/mcp-read-website-fast

Fast, token-efficient web content extraction for AI agents - converts websites to clean Markdown.

npm version GitHub Actions

<a href="https://glama.ai/mcp/servers/@just-every/mcp-read-website-fast"> <img width="380" height="200" src="https://glama.ai/mcp/servers/@just-every/mcp-read-website-fast/badge" alt="read-website-fast MCP server" /> </a>

Overview

Existing MCP web crawlers are slow and consume large quantities of tokens. This pauses the development process and provides incomplete results as LLMs need to parse whole web pages.

This MCP package fetches web pages locally, strips noise, and converts content to clean Markdown while preserving links. Designed for Claude Code, IDEs and LLM pipelines with minimal token footprint. Crawl sites locally with minimal dependencies.

Note: This package now uses @just-every/crawl for its core crawling and markdown conversion functionality.

Features

  • Fast startup using official MCP SDK with lazy loading for optimal performance
  • Content extraction using Mozilla Readability (same as Firefox Reader View)
  • HTML to Markdown conversion with Turndown + GFM support
  • Smart caching with SHA-256 hashed URLs
  • Polite crawling with robots.txt support and rate limiting
  • Concurrent fetching with configurable depth crawling
  • Stream-first design for low memory usage
  • Link preservation for knowledge graphs
  • Optional chunking for downstream processing

Features

  • Fast startup using official MCP SDK with lazy loading for optimal performance
  • Content extraction using Mozilla Readability (same as Firefox Reader View)
  • HTML to Markdown conversion with Turndown + GFM support
  • Smart caching with SHA-256 hashed URLs
  • Polite crawling with robots.txt support and rate limiting
  • Concurrent fetching with configurable depth crawling
  • Stream-first design for low memory usage
  • Link preservation for knowledge graphs
  • Optional chunking for downstream processing

Available Tools

  • read_website - Fetches a webpage and converts it to clean markdown
    • Parameters:
      • url (required): The HTTP/HTTPS URL to fetch
      • pages (optional): Maximum number of pages to crawl (default: 1, max: 100)

Available Resources

  • read-website-fast://status - Get cache statistics
  • read-website-fast://clear-cache - Clear the cache directory

Auto-Restart Feature

The MCP server includes automatic restart capability by default for improved reliability:

  • Automatically restarts the server if it crashes
  • Handles unhandled exceptions and promise rejections
  • Implements exponential backoff (max 10 attempts in 1 minute)
  • Logs all restart attempts for monitoring
  • Gracefully handles shutdown signals (SIGINT, SIGTERM)

For development/debugging without auto-restart:

Copy & paste β€” that's it
# Run directly without restart wrapper
npm run serve:dev

Architecture

Copy & paste β€” that's it
mcp/
β”œβ”€β”€ src/
β”‚   β”œβ”€β”€ crawler/        # URL fetching, queue management, robots.txt
β”‚   β”œβ”€β”€ parser/         # DOM parsing, Readability, Turndown conversion
β”‚   β”œβ”€β”€ cache/          # Disk-based caching with SHA-256 keys
β”‚   β”œβ”€β”€ utils/          # Logger, chunker utilities
β”‚   β”œβ”€β”€ index.ts        # CLI entry point
β”‚   β”œβ”€β”€ serve.ts        # MCP server entry point
β”‚   └── serve-restart.ts # Auto-restart wrapper

Development

Copy & paste β€” that's it
# Run in development mode
npm run dev fetch https://example.com

# Build for production
npm run build

# Run tests
npm test

# Type checking
npm run typecheck

# Linting
npm run lint

Contributing

Contributions are welcome! Please:

  1. Fork the repository
  2. Create a feature branch
  3. Add tests for new functionality
  4. Submit a pull request

License

MIT