Hyperbrowser MCP Server – Connect AI agents to the web through browsers
Hi HN! Excited to share our MCP Server at Hyperbrowser - something we’ve been working on for a few days. We think it’s a pretty neat way to connect LLMs and IDEs like Cursor / Windsurf to the internet. Our MCP server exposes seven tools for data collection and browsing: 1. `scrape_webpage` - Extract formatted (markdown, screenshot etc) content from any webpage 2. `crawl_webpages` - Navigate through multiple linked pages and extract LLM-friendly formatted content 3. `extract_structured_data` - Convert messy HTML into structured JSON 4. `search_with_bing` - Query the web and get results…
Hyperbrowser MCP Server – Connect AI agents to the web through browsers launched on March 20, 2025 with 63 votes, #490 of 7,215 launches that month and more than 95% of that year's launches. The site was still online in September 2026, 1 year on.
In plain words
Hyperbrowser MCP Server is a tool that connects AI language models and code editors like Cursor and Windsurf to the internet through browser automation. It provides seven functions for web interaction, including webpage scraping, multi-page crawling, structured data extraction, web search via Bing, and browser automation using different AI agents. The server converts web content into formats that AI systems can understand and work with.
written from the facts on this page · September 2026
From the sources
A MCP server implementation for hyperbrowser. Contribute to hyperbrowserai/mcp development by creating an account on GitHub.
This is Hyperbrowser's Model Context Protocol (MCP) Server. It provides various tools to scrape, extract structured data, and crawl webpages. It also provides easy access to general purpose browser agents like OpenAI's CUA, Anthropic's Claude Computer Use, and Browser Use. More information about the Hyperbrowser can be found here . The hyperbrowser API supports a superset of features present in the mcp server. More information about the Model Context Protocol can be found here . npx hyperbrowser-mcp < YOUR-HYPERBROWSER-API-KEY > Running on Cursor Add to ~/.cursor/mcp.json like this: { "mcpServers" : { "hyperbrowser" : { "command" : " npx " , "args" : [ " -y " , " hyperbrowser-mcp " ], "env"…from github.com
In the maker’s words, at launch
Hi HN! Excited to share our MCP Server at Hyperbrowser - something we’ve been working on for a few days. We think it’s a pretty neat way to connect LLMs and IDEs like Cursor / Windsurf to the internet. Our MCP server exposes seven tools for data collection and browsing: 1. `scrape_webpage` - Extract formatted…
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Large Language Models (LLMs) are powerful, but they’re limited by fixed context windows and outdated knowledge. What if your AI could access live search, structured data extraction, OCR, and more—all through a standardized interface? We built the JigsawStack MCP Server, an open-source implementation of the Model Context Protocol (MCP) that lets any AI model call external tools effortlessly. Here’s what it unlocks: - Web Search & Scraping: Fetch live information and extract structured data from web pages. - OCR & Structured Data Extraction: Process images, receipts, invoices, and handwritten…

Hyperbrowser – Scalable Browser Infrastructure for AI Apps2024 · hyperbrowser.ai · ▲57Hey HN! Excited to share a project we've been working on called Hyperbrowser. It’s a tool that makes scaling headless browsers ridiculously easy. It allows you to spin up hundreds of browser sessions in secure, isolated environments, with sub-second launch times. We automatically solve captchas, use residential proxies and manage concurrent sessions so that you can focus on your own business. The idea for Hyperbrowser came from our own struggles building AI apps and agents like sales tools, automations, and AI editors. Every project seemed to hit the same roadblock: interacting with the web.…
OpenBrowser-AIApr 2026 · openbrowser.me · ▲76Your AI agent is burning 6x more tokens than it needs to just to browse the web. We built OpenBrowser MCP to fix that. Most browser MCPs give the LLM dozens of tools: click, scroll, type, extract, navigate. Each call dumps the entire page accessibility tree into the context window. One Wikipedia page? 124K+ tokens. Every. Single. Call. OpenBrowser works differently. It exposes one tool. Your agent writes Python code, and OpenBrowser executes it in a persistent runtime with full browser access. The agent controls what comes back. No bloated page dumps. No wasted tokens. Just the data your…
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