Firecrawl MCP for AI. Give your AI agent full, structured web context.
Works with every AI agent you already use
…and any MCP-compatible client








Connect to your AI in seconds.
Firecrawl turns any website into clean, structured Markdown for your AI agent. It handles all the junk you usually fight: JavaScript rendering, cookie banners, and anti-bot measures.
Need to read a whole site? Crawl it recursively. Just want to know what pages exist? Map them first. Firecrawl gives your agent full web access in one API call.
What your AI can do
Crawl site
It crawls an entire website, extracting content from multiple linked pages in batches.
Map site
This tool discovers all URLs on a website so you can plan exactly what needs to be scraped later.
Scrape page
It pulls clean Markdown from any single web page, handling anti-bot protections automatically.
You give it a URL, and you get clean Markdown text ready for analysis.
It returns a list of all possible URLs on a domain without downloading any actual content.
The system follows internal links across a whole domain, returning the collected pages in batches.
It performs a search query and then scrapes the top results, including their full article text.
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Firecrawl: 4 Tools
These four tools let you scrape, map, crawl, or search any website, converting live content into structured data for your agent.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Firecrawl on VinkiusCrawl Site
It crawls an entire website, extracting content from multiple linked pages in batches.
Map Site
This tool discovers all URLs on a website so you can plan exactly what needs to be...
Scrape Page
It pulls clean Markdown from any single web page, handling anti-bot protections...
Search Web
It searches the web for a topic and returns scraped content from the resulting top...
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Firecrawl, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,100+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Firecrawl. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This connection provides 4 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Gathering web context used to be a nightmare of copies, tabs, and failed requests.
Today, if your agent needs information from the live web, you have to run multiple tools or complex APIs. You copy the main URL into one place, then manually feed that link into another service just to check its structure. If you need an entire site indexed, you're dealing with rate limits and tracking thousands of links across multiple tabs.
With this MCP, your agent gets a single point of access to web data. You tell it what domain or topic you care about; the system handles the messy details like JavaScript rendering, anti-bot protection, and link traversal automatically. You get clean Markdown—no junk, no effort.
Firecrawl MCP: Structured Web Data for Your Agents
The tedious parts are gone: No more writing boilerplate code just to discover URLs before scraping. No more stitching together search results with full-page content extraction.
You now run `map_site` or `search_web`, and the agent gets precisely what it needs—a clean, structured list of links or a fully extracted article—without any manual intervention.
What your AI can actually do with this
Your AI client needs more than just conversation; it needs context from the real world. This connector lets you give your agent direct access to the entire web, treating every URL like a readable document. You can scrape specific product pages for deep analysis. Or maybe you need to index an entire documentation site—the system handles crawling through all internal links until it’s done.
It even gives you Google-like search results, but with the full text extracted right there. Connecting this via Vinkius means your agent gets one gateway to read and understand anything on the internet, turning complex web data into simple Markdown for immediate use.
019d7599-9802-73fd-9e98-eba0bf01c694 Here's how it actually works
The bottom line is: you point your AI client at the data source, and it handles all the messy plumbing required to make that data readable Markdown.
Subscribe to this MCP and provide your API key.
Your agent sends a request specifying the target URL or domain scope.
The tool executes the web operation (e.g., scraping, crawling) and returns the structured content or job ID for tracking.
Who is this actually for?
Data engineers who are tired of building complex web-fetching pipelines. Research teams needing structured context from multiple sources. Content developers whose agents need up-to-date, scraped article content.
Needs to gather background information on a niche topic by running a search and scraping the full text of the top five academic articles.
Builds pipelines that systematically crawl an entire company documentation site, extracting every page's content into a database.
Must scrape the latest product pages from five different competitor sites to benchmark pricing and feature lists.
What Changes When You Connect
Scrape single articles perfectly. Use scrape_page to grab clean Markdown from any URL, ignoring cookie banners or JS rendering issues so the text is always usable.
Index entire sites reliably. Running crawl_site lets your agent recursively visit every page on a domain, perfect for building knowledge bases from documentation sets.
Understand the scope first. Before you write code to grab data, run map_site. This shows you all available URLs so you know exactly how big the target site is.
Fact-check with depth. Don't just search; use search_web to find information and then get the full article content from the top results for verification.
Save time on data prep. You don't have to worry about messy HTML or JavaScript execution; the MCP handles all the dirty work so you get structured text immediately.
See it in action
A research team needs a competitive analysis.
Instead of manually visiting competitor sites, the agent first uses map_site to list all product sections on three target domains. Then, it runs crawl_site across those domains to collect and compare full feature set descriptions.
You need a quick summary of a new white paper.
The agent uses search_web for the title of the white paper. Once found, it scrapes the main page using scrape_page, giving your agent enough context to summarize the key takeaways without needing manual reading.
A data team is building a knowledge base from an internal wiki.
The engineer uses crawl_site on the wiki domain. The MCP processes all the linked pages, turning thousands of articles into clean Markdown chunks ready to be indexed.
You need to validate if a site has documentation.
Before committing resources, you run map_site on the suspected domain. If it returns hundreds of links under a 'docs' directory, you know where to focus your scraping efforts.
The honest tradeoffs
Using search for site structure.
Trying to run search_web on an entire domain name thinking it will map out all the pages. This just gives you random, scraped articles from Google's results list.
If you want a complete inventory of URLs before scraping content, always use map_site. It provides a clean sitemap listing without wasting credits or bandwidth on actual article text.
Scraping complex sites with one tool.
Trying to scrape an entire documentation portal using only scrape_page on the homepage, assuming it will find everything. It won't; you'll just get the landing page text.
You need a multi-step process: First, use map_site to list all subdirectory links. Second, pass those URLs back into crawl_site to collect and index the content.
Missing context in research.
Asking your agent general questions about a topic without providing source material. The agent can't verify facts against specific web pages.
Always run search_web first to gather the top results, and then pass those links back into the system or use scrape_page on the most promising result for detailed context.
When It Fits, When It Doesn't
Use this MCP if your primary need is turning unformatted web text (HTML, JS) into predictable, clean Markdown. The key distinction is scope: If you just want a list of links, use map_site. If you want to collect content from every link on the site over time, run crawl_site. If you only care about one specific page, use scrape_page. Don't try to make these tools do everything; they are specialized primitives. If your task is simple—just reading a single article—don't bother with crawl_site; it’s overkill and slower.
Questions you might have
How does Firecrawl MCP handle JavaScript content? +
It automatically handles JavaScript rendering. This means if the website loads its data using JS (like many modern blogs do), your agent still gets to read it, not just the initial source code.
Can I use Firecrawl MCP to index an entire company wiki? +
Yes. You'd run map_site first to get all internal links, then pass those links into a job using crawl_site. This collects and indexes the full text from every linked page.
Is Firecrawl MCP better than just scraping one URL? +
Yes. While scrape_page works for single URLs, this MCP also gives you site-level tools like map_site, which help you understand the whole structure before you start extracting content.
What is the difference between crawl_site and map_site? +
Mapping only finds links; it returns a sitemap of URLs. Crawling, however, actually goes to those links, scrapes their content, and returns the text for every single page.
When I run `search_web`, does it provide full article content, or just snippets? +
It provides the extracted, full content from top search results. The tool combines web searching with automatic extraction, meaning you get more than just links; you get the actual text needed for your agent's context.
Does `scrape_page` consistently return Markdown, even if the source page is messy? +
Yes, it always returns clean Markdown regardless of how messy the original webpage was. This automatic conversion handles formatting issues and ensures your agent receives usable, structured text from any single URL.
How does the MCP handle rate limits when running `crawl_site` across many pages? +
The platform manages usage through credits assigned to your API key. You simply track your consumption; once you run out of free credits, you just top up your account for continued use.
Before using `scrape_page`, how can I check the entire site structure with `map_site`? +
You run map_site first to generate a complete sitemap of all possible URLs on a domain. This allows you to understand the site's architecture before deciding exactly which pages need scraping.
How does Firecrawl pricing work? +
Firecrawl uses a credit-based system. You get 500 free lifetime credits to start (no credit card required). Base cost is 1 credit per page scraped. Advanced features like JSON extraction (+4 credits) or enhanced mode (+4 credits) consume additional credits per page. Paid plans start at $16/month with 3,000 monthly credits.
Can Firecrawl handle JavaScript-heavy websites? +
Yes! Firecrawl renders pages in a full browser environment before extracting content — this means it handles React, Next.js, Angular, and any other JavaScript framework. It also automatically bypasses common anti-bot protections, removes cookie consent banners, and waits for dynamic content to load before extraction.
What formats does Firecrawl return? +
Firecrawl can return content in multiple formats: Markdown (default and most popular for LLM consumption), HTML, raw HTML, structured JSON (with LLM-powered extraction), screenshots, links, and page metadata. You can request multiple formats in a single call.
Powerful workflows you can unlock today
Build Serverless Data Warehouses Using MCP
You scrape data into CSV files that nobody queries , Firecrawl extracts structured web data, Neon stores it in serverless PostgreSQL you can query with SQL, and Sheets visualizes the results
MCP Servers for Self-Updating Research Bases
You spend 3 hours reading 40 articles to write one research brief , an AI agent with Firecrawl reads all 40 in 90 seconds, stores them semantically in Weaviate, and writes the brief in Notion with every source linked and every claim verified
We've already built the connector for Firecrawl. Just plug in your AI agents and start using Vinkius.
No hosting. No infrastructure. No complex setup.
All 4 tools are live and waiting.
You're up and running in seconds.
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Built, hosted, and secured by Vinkius. You just connect and go.