A Model Context Protocol (MCP) server implementation that brings Hyperbrowser’s web-scraping, crawling and browser-agent tooling to any MCP-compatible client.
https://github.com/hyperbrowserai/mcpYou're probably spending too much time wrestling with web scraping code. Dealing with dynamic content, handling JavaScript-heavy sites, extracting structured data from messy HTML, and maintaining browser automation scripts that break every time a site updates.
The Hyperbrowser MCP server changes this entirely. It plugs directly into your existing AI coding assistant (Claude Desktop, Cursor, Windsurf) and gives you professional-grade web automation capabilities without writing a single line of scraping code.
Instead of building custom scrapers, you get nine production-ready tools that your AI assistant can use directly:
# One command, then your AI can scrape any site
npx hyperbrowser-mcp YOUR-API-KEY
Now your coding assistant can scrape documentation sites, extract product data, crawl competitor pages, automate form submissions, and handle complex multi-step browser workflows - all through natural language requests.
Data extraction that actually works: The extract_structured_data tool converts any messy HTML into clean JSON schemas. No more regex parsing or BeautifulSoup wrestling - just describe what data you want and get it structured.
Multi-page crawling without the headaches: crawl_webpages follows links intelligently and extracts content from entire site sections. Perfect for documentation sites, product catalogs, or any multi-page data collection.
Three levels of browser automation:
browser_use_agent for fast, lightweight tasksopenai_computer_use_agent for general automationclaude_computer_use_agent for complex, multi-step workflowsSearch integration: search_with_bing brings web search directly into your development context - no more switching tabs to research APIs or find examples.
Research and Documentation: Your AI can crawl entire documentation sites, extract API endpoints, gather code examples, and build comprehensive project knowledge bases.
Competitive Analysis: Automated product data extraction, pricing comparisons, feature analysis across multiple sites without manual data entry.
Content Migration: Moving from one platform to another? Your AI can extract structured content from the old system and prepare it for import.
QA and Testing: Automated user flows, form testing, and site monitoring through natural language instructions rather than brittle test scripts.
Data Pipeline Building: Instead of building custom ETL processes for web data, describe what you need and let the AI handle the extraction and transformation.
The server integrates seamlessly with your existing setup:
Claude Desktop:
{
"mcpServers": {
"hyperbrowser": {
"command": "npx",
"args": ["--yes", "hyperbrowser-mcp"],
"env": {"HYPERBROWSER_API_KEY": "your-key"}
}
}
}
Cursor/Windsurf: Drop the same config into your MCP settings and you're running.
One-line install: npx -y @smithery/cli install @hyperbrowserai/mcp --client claude
You don't need to maintain browser automation code anymore. No Selenium setup, no Playwright configuration, no handling of dynamic content loading, no dealing with anti-bot measures. The server handles all the complexity while your AI assistant gets clean, structured data.
The persistent profile system means your automation tasks maintain session state and cookies across interactions. Your AI can build complex workflows that span multiple requests without losing context.
With 318+ GitHub stars and active development, this isn't a side project - it's a production-ready tool that other developers are already using to eliminate web scraping busywork.
The next time you need to extract data from a website, don't reach for BeautifulSoup. Ask your AI to handle it through Hyperbrowser, and focus on what you actually want to build with that data.