Firecrawl MCP for AI. Web Scraping Made Simple: Crawl, Scrape, Index.
Works with every AI agent you already use
…and any MCP-compatible client








Connect to your AI in seconds.
Firecrawl crawls entire websites, turning complex web pages into clean, structured Markdown ready for your AI agent. It handles JavaScript rendering and recursive crawling automatically.
You can map site structures, scrape individual URLs, or monitor large data jobs—all through natural conversation with your preferred AI client.
What your AI can do
Cancel active crawl
Stops any ongoing website crawl job immediately.
Get crawl status
Retrieves the current status of a specific crawl job ID.
Start crawl
Initiates a recursive crawl of a website and returns a unique job ID for tracking.
Turn any specific URL into clean Markdown text in one go.
Start a job that discovers and extracts content from every subpage, building deep knowledge bases.
Find all the internal links on a domain without actually downloading any full page content.
Monitor ongoing crawls to see exactly where the process stands.
Track your remaining API credits and current usage in real time.
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Firecrawl: Web Data Extraction (6 Tools)
Use these tools to manage web data acquisition workflows. You can scrape single URLs, run large recursive crawls, or map site structures from within 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 VinkiusCancel Active Crawl
Stops any ongoing website crawl job immediately.
Get Crawl Status
Retrieves the current status of a specific crawl job ID.
Start Crawl
Initiates a recursive crawl of a website and returns a unique job ID for tracking.
Map Website Structure
Discovers and lists every reachable link on a domain without scraping any content.
Scrape Url
Extracts the full, cleaned Markdown content from a single URL.
Get Api Usage
Checks how many API credits you have left and tracks your usage.
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
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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
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- 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 6 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Dealing with messy web data feels like archaeology.
Today, pulling information from the web means manual clicking through dozens of pages. You copy content into one document, then paste it into another tool. If the site uses JavaScript or has annoying sidebars and footers, you spend half your time cleaning up the garbage tags just to get usable text.
With this MCP, you simply ask your agent for the data. It handles all the heavy lifting: rendering the JS, filtering out the noise, and delivering only clean Markdown. You get structured insights instead of a pile of messy HTML.
Structured Web Data with Firecrawl
You no longer need to worry about writing custom BeautifulSoup scripts or debugging complex scraping logic. The tools handle the job: `map_website_structure` finds all links, and then `start_crawl` systematically pulls content from them.
Your agent treats the internet like a predictable filing cabinet. It's reliable data acquisition, period.
What your AI can actually do with this
Need to build a knowledge base from the web? Firecrawl lets you treat websites like structured databases. Instead of wrestling with messy HTML tags or writing complex scraping scripts, you just tell your agent what site you need. It handles everything else: rendering JavaScript, filtering out headers and footers, and converting deep structures into clean Markdown.
If you connect this MCP via Vinkius, your AI client manages the whole process—from mapping a domain's entire link structure to crawling hundreds of pages recursively. You maintain control over data acquisition using natural language prompts, making web scraping feel like talking to an expert data engineer who never gets tired.
019dd0f1-1cfd-731d-bbff-c84b7b8f732b Here's how it actually works
The bottom line is, you talk to your agent, and it handles the whole web scraping pipeline for you.
Subscribe to this MCP, then grab your API key from the Firecrawl dashboard.
Your AI client uses natural language to initiate a task—like mapping links or starting a crawl job.
The system returns structured data (Markdown or status updates) that your agent can use immediately.
Who is this actually for?
Anyone who has to gather data from multiple web pages manually. This is for the research team bogged down in competitor websites, the developer building a RAG application, or the content creator drowning in unformatted articles.
They need clean, structured data inputs to build Retrieval-Augmented Generation (RAG) pipelines without writing custom web scrapers.
They automate gathering competitor information across multiple sites for comparison and analysis.
They instantly convert long-form articles into clean Markdown or need full-page screenshots without leaving their workspace.
What Changes When You Connect
Stop copy-pasting messy HTML. Use scrape_url to instantly convert any webpage into high-fidelity Markdown that your AI agent can read cleanly.
Build massive knowledge bases by running a recursive crawl using start_crawl. This tool maps and extracts content from an entire site, not just one page.
Need to know what links exist before scraping? Use map_website_structure first. It gives you the full blueprint of a website without wasting credits on unnecessary downloads.
Maintain total control over your process using get_crawl_status. You can monitor long jobs and use cancel_active_crawl if something goes wrong or takes too long.
Know your limits before running big jobs. The get_api_usage tool lets you check your credit count so you never run out mid-project.
See it in action
Building a competitor analysis database
A market analyst needs to track 15 competitors' documentation sites. They use map_website_structure first to confirm all relevant subdomains, then run start_crawl on each one to build an indexed knowledge base for comparison.
Creating a site index for a manual
A developer needs to document every single page of their internal wiki. They use map_website_structure to get the full list, and then loop through that list using scrape_url to pull clean Markdown from each link.
Researching a niche topic quickly
A researcher needs data on a specific university department. They use start_crawl on the main departmental URL, letting it recursively gather content and index all pages for later review.
Checking web content integrity
Before committing to a large crawl job, a user calls get_api_usage. This confirms they have enough credits and can start the process with confidence.
The honest tradeoffs
Trying to scrape everything at once
Just calling 'scrape this whole website' without thinking about the scope, which might fail or hit rate limits.
Don't try to do it all in one go. First, use map_website_structure to get a list of URLs, then process them using scrape_url, or if you want full coverage, start with start_crawl.
Getting lost in long jobs
Starting a crawl job and forgetting about it until hours later, unsure if it finished, failed, or is still running.
Always track your work. Use start_crawl to get the ID, then use get_crawl_status periodically until you confirm completion.
Running into credit limits
The agent starts scraping and suddenly stops with an error because the user didn't know their usage was high.
Check your budget first. Call get_api_usage before launching any major crawl job to confirm you have sufficient credits.
When It Fits, When It Doesn't
Use this MCP if your goal is structured data extraction from the web, particularly when dealing with complex sites that require multiple steps (like mapping links or crawling recursively). You'll use map_website_structure for site blueprints; use scrape_url when you only care about one page. Only run a full crawl using start_crawl if you need deep knowledge bases across many pages. Don't use this MCP if your data is already in a structured database format, or if you just need to scrape a simple API endpoint—there are better tools for that.
Questions you might have
How do I scrape just one page using Firecrawl? (scrape_url) +
Call scrape_url and provide the exact URL. This tool is designed for single-page extraction, giving you clean Markdown without starting a full crawl job.
What's the difference between map_website_structure and start_crawl? +
map_website_structure only finds links (the blueprint). start_crawl actually visits those links and extracts content to build your knowledge base.
Can I stop a crawl job if it fails or takes too long? (cancel_active_crawl) +
Yes. If you initiate a crawl via start_crawl and need to halt the process, use cancel_active_crawl with the job ID.
How do I check if my credits are okay before crawling? (get_api_usage) +
Use get_api_usage. This tool immediately reports your remaining credit balance and usage history, letting you manage costs upfront.
Using `get_crawl_status`, how do I confirm that a recursive job has completed indexing all pages? +
The status endpoint reports the final completion state. You need to poll this tool repeatedly until it returns 'completed' or 'failed'. This confirms the crawl finished processing, not just started.
If a recursive job initiated by `start_crawl` encounters an error, how do I debug the issue? +
The system captures detailed error logs associated with the specific job ID. Check these logs using your agent to see the exact page or link that caused the failure. You can then retry only the problematic segment.
How does `map_website_structure` handle links pointing outside of the main domain? +
The tool is designed to understand internal site architecture, so it only discovers and lists reachable URLs within the specified root domain. It ignores external links entirely.
When managing multiple large jobs, do I need a specific job ID for `cancel_active_crawl`? +
Yes, you must provide the unique Job ID for each crawl instance you intend to stop. The tool operates on IDs; it can't cancel an entire category of running jobs.
How do I find my Firecrawl API Key? +
Log in to your Firecrawl dashboard, and navigate to the API Keys section to copy your unique token.
Can I scrape content excluding headers and footers? +
Yes! The scrape_url tool includes an onlyMainContent parameter. When set to true, Firecrawl uses AI to extract only the core article or page content.
How long does a recursive crawl take? +
Crawl time depends on the site size and depth. Use the get_crawl_status tool to monitor progress and retrieve results once the job is complete.
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