Firecrawl MCP for AI. Turn any website into usable data for your agent.
Works with every AI agent you already use
…and any MCP-compatible client








Connect to your AI in seconds.
Firecrawl turns websites into clean, structured markdown data for your AI agent. It lets you scrape single pages or run multi-page crawls across entire sites without touching a dashboard.
You can map out a site's full structure and monitor job progress using this MCP.
What your AI can do
Crawl url
Initiates a multi-page crawl job for an entire website.
Delete crawl job
Removes a specified, completed or failed crawl job from the system log.
Get crawl status
Checks and reports the current progress status of an ongoing crawl job.
Scrape any single URL and return the main text content as clean, machine-readable markdown.
Generate a map showing all the key pages and how they connect on a website's hierarchy.
Start large, multi-page crawling jobs across an entire site and monitor them until completion.
List all crawl jobs (active or finished) and delete old jobs to keep your job log clean.
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Firecrawl Alternative: 6 Tools
These tools allow you to manage every aspect of web data extraction, from mapping site structure to deleting old crawl jobs.
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 Url
Initiates a multi-page crawl job for an entire website.
Delete Crawl Job
Removes a specified, completed or failed crawl job from the system log.
Get Crawl Status
Checks and reports the current progress status of an ongoing crawl job.
List Crawl Jobs
Retrieves a list detailing all past and present crawl jobs managed by the MCP.
Map Website
Generates a structural map showing how a website's various pages are linked together.
Scrape Url
Retrieves and cleans the main content from a single specified URL, returning markdown text.
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Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
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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
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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.
Manual Web Data Collection
Right now, extracting data feels like manual labor. You open the source page, right-click, copy the text block; then you switch tabs to check a competitor's site and repeat the process. If you need 50 pages, that means 50 cycles of clicking, copying, pasting into a spreadsheet, and hoping you didn't miss any key sections.
With this MCP connected to your agent, those steps vanish. Your agent runs the command, and it automatically handles the web complexity—the scraping, the cleaning, the structuring. You get back pure markdown text that's ready for analysis.
Structured Web Data Output
You don't just get raw HTML; you get structured data. The MCP converts complex website code into clean, predictable formats—markdown or HTML—that your agent can read without error.
This means the AI doesn't waste time filtering out navigation menus or ad scripts. It gives you exactly what matters: the core content. Period.
What your AI can actually do with this
Web content is messy. When you pull data off a website, it rarely comes in a neat format your AI agent can use immediately; it's usually buried in HTML tags, navigation menus, and random scripts. This MCP changes that. It lets your agent treat any complex website like a clean source file.
You can direct it to scrape specific pages for immediate results or launch huge crawl jobs across multiple site sections. Furthermore, if you need to understand the architecture before pulling data, you can map out the entire site's hierarchy first. Because this MCP connects through Vinkius, your agent gains access to a powerful web curation engine that keeps all your extracted information structured and ready for whatever task comes next.
019d843b-5f94-7222-bd04-ca3f780b1ef2 Here's how it actually works
The bottom line is, you tell your agent what web content you need, and this MCP handles the complex extraction process for you.
Subscribe to this MCP and enter the required API key.
Instruct your AI agent to run a specific action, like scraping a URL or mapping a site structure.
The MCP executes the task and returns structured data—be it clean markdown text or a crawl job status.
Who is this actually for?
Anyone dealing with unstructured data from websites needs this. Content Marketers who run competitor audits; Data Scientists building knowledge bases; or SEO Specialists needing to map site depth—if your job involves reading a lot of web pages, you'll use this.
Runs rapid audits on landing pages and competitor blogs by scraping URLs to gather raw text for content generation.
Uses the MCP to crawl entire knowledge base sections, ensuring all data is converted into a consistent markdown format before ingestion.
Maps out website hierarchies using the site mapping tool and checks job statuses to verify page coverage without manual testing.
What Changes When You Connect
Stop wrestling with messy HTML. Instead, the scrape_url tool pulls out only the core text, giving you clean markdown that's ready to feed into a prompt.
Don't guess how big a site is. Use map_website to instantly see the entire page hierarchy and identify missing or critical sections before you start crawling.
Need data from dozens of pages? The MCP lets you initiate large jobs using crawl_url. You then use get_crawl_status to monitor progress without manually checking a dashboard.
Keep your workspace tidy. After a massive crawl job is done, use list_crawl_jobs and delete_crawl_job to archive the record, preventing log clutter.
The MCP handles all the web complexity so you don't have to. You focus on the data; we handle the scraping.
See it in action
Monitoring a Competitor’s Blog
A marketing manager wants to audit five competitor blogs. Instead of opening five tabs and copying content, they ask their agent to run crawl_url across the main feed URLs. The MCP handles the job execution and delivers clean markdown summaries for direct comparison.
Building a Knowledge Base
A data scientist needs structured documentation from 50 pages of internal guides. They first run map_website to ensure full coverage, then use crawl_url to start the multi-page job, monitoring it with get_crawl_status until every page is collected.
Analyzing Site Architecture
An SEO specialist needs to know if a client's site has deep linking issues. They ask their agent to run map_website. The MCP immediately returns the structure, allowing them to spot missing or orphaned directories instantly.
Cleaning Up Old Data Runs
After several weeks of testing, a team has dozens of old crawl jobs cluttering their history. They use list_crawl_jobs to see everything and then execute delete_crawl_job on the irrelevant entries.
The honest tradeoffs
Trying to scrape a whole site at once
Asking your agent, 'Scrape every page from this massive corporate website.' This is vague and will likely fail or only grab the homepage.
First, use map_website to get the full list of endpoints. Then, create a focused job using crawl_url with specific parameters. Finally, monitor it via get_crawl_status.
Forgetting about job management
Running five large crawls over weeks and letting the job list grow endlessly, making it impossible to find a recent run.
After you're done with a crawl, use list_crawl_jobs to confirm its completion. Once confirmed, run delete_crawl_job on that entry so your log stays clean.
Using scrape_url for large data sets
Running scrape_url repeatedly for 50 pages is slow and inefficient.
For bulk content extraction, always use crawl_url. This starts a background job that can run continuously and efficiently collects all the necessary URLs.
When It Fits, When It Doesn't
Use this MCP if your core task involves turning unstructured web pages into clean, structured text data for an agent. If you need to audit site structure (use map_website) or process many pages over time (use crawl_url), this is the right tool. Don't use it if you just need to browse a website interactively—it won't simulate a user clicking buttons. Also, don't rely on scrape_url for anything bigger than one page; it's an extraction utility, not a workflow manager. If your goal is simply finding API keys or calling external services that aren't web-related, you need a different type of MCP.
Questions you might have
How do I start a crawl job using Firecrawl MCP? +
You initiate this using crawl_url. You provide the starting URL and any necessary parameters, and the MCP handles launching the background collection process.
Can I check if my crawl job is done with Firecrawl MCP? +
Yes. Use get_crawl_status to check the current progress of a running or paused crawl job, giving you real-time feedback on its status.
What's the difference between scrape_url and crawl_url in Firecrawl MCP? +
Scrape is for one URL; it delivers immediate text. Crawl is for multiple URLs across a site; it launches a background, multi-page job.
How do I delete old jobs using Firecrawl MCP? +
First, use list_crawl_jobs to get the Job ID. Then, pass that specific ID into the delete_crawl_job tool to remove it from your history.
What does the `map_website` function do, and how can it help me plan a crawl? +
It generates an immediate map of a website's structure. Instead of scraping pages, you get a clear hierarchy showing site depth and page distribution. This is perfect for planning exactly which areas to target next.
I want to audit my past work; how do I use `list_crawl_jobs`? +
The function returns a comprehensive list of all your crawl jobs, whether they succeeded or failed. You can review this history to track data collection over time and ensure you haven't missed any key sites.
When I use `scrape_url`, can I control the output format? +
Yes, you tell the MCP whether you need markdown or HTML. Picking the right format ensures that the content is structured perfectly for whatever LLM client or data pipeline you're feeding it.
If a crawl job fails using `crawl_url`, how do I remove its record? +
You first use list_crawl_jobs to find the specific faulty Job ID. Then, you execute delete_crawl_job with that ID. This removes junk records and keeps your MCP logs clean.
How do I find my Firecrawl API Key? +
Log in to your Firecrawl.dev dashboard, and you will find your API Key under the settings. Copy and paste it below.
Can the agent crawl multiple pages at once? +
Yes. Use the crawl_url tool providing the base URL. Firecrawl will start a job to extract all subpages, and you can monitor the status via get_crawl_status.
Is it possible to see the website structure before scraping? +
Yes. The map_website tool allows your agent to retrieve a hierarchy of the site, giving you an audit of the structure before performing a full scrape or crawl.
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