Keeping a website current takes more than fixing broken links. A tutorial can load perfectly while pointing readers to an old installer, an abandoned project, or instructions that no longer work.

I used Firecrawl to help review older articles on this site. The useful part was getting readable source material into an AI agent’s hands so we could compare it with what I had written. You can use the same approach on a blog, business website, or documentation site without building a complicated updater.

What Firecrawl does

Firecrawl retrieves web content and turns it into Markdown or structured data. Markdown is plain text with headings, links, and other basic formatting, which makes it convenient to read and give to an AI assistant.

For website maintenance, these are the operations to know:

Operation When to use it
Scrape Read a specific article or documentation page.
Map Discover URLs on your site so you can choose pages to review.
Crawl Collect content from a group of linked pages.
Search Find an official source when you do not know its current address.

Start with Scrape if you already have the URLs. A Map helps you find pages, but it is not a guaranteed list of everything on your site. A Crawl collects the pages themselves; set a page limit and restrict it to the section you actually need.

Firecrawl provides the material. You or your AI assistant still need to decide whether a claim is wrong and what should change.

Why use Firecrawl instead of letting an agent scrape?

An agent can often fetch a web page directly or open it in a browser. For one simple page, that may be all you need. Firecrawl becomes useful when collecting readable content starts taking more work than reviewing it.

Imagine checking ten older tutorials against several different documentation sites. One serves ordinary HTML. Another builds its content with JavaScript. A third surrounds the useful text with navigation, menus, and repeated footer links. With direct scraping, the agent needs suitable tools and extraction logic for each situation. You may end up maintaining browser scripts and page-specific cleanup before you get to the actual article review.

Firecrawl packages much of that collection work into a service. Its scraping tools handle JavaScript-rendered pages, offer Markdown and structured output, and manage fetching infrastructure such as proxies and caching. That can give the agent more consistent source material across different websites.

The benefit is less plumbing to build and maintain. Clean article text can also reduce the irrelevant material sent to a model compared with raw HTML. Whether that saves time or money depends on the pages, the agent’s existing tools, and the amount of cleanup you would otherwise need. An agent that already has a good browsing and extraction tool may gain less from adding another service.

Your task A sensible starting point
Read an article already in your repository Give the agent the local Markdown.
Read one public page that loads cleanly Use the agent’s existing fetch or browser tool.
Get data from an official API or feed Use that structured source directly.
Review pages across several different websites Try Firecrawl for consistent text extraction.
Collect a documentation section Use a bounded Firecrawl crawl and inspect its results.
Check how a page looks or whether a button works Use a browser; extracted text cannot establish visual or interactive behavior.

There are tradeoffs. Hosted Firecrawl adds another service, usage charges, and a data-handling decision. Check what you are sending, especially before including private material. It can still encounter blocked pages or miss important content, so compare a few captures with the originals before trusting a larger run.

Freshness matters too. Firecrawl can return cached content; its scrape API provides maxAge to control acceptable cache age, with maxAge: 0 requesting a fresh fetch. Use that when checking a recent change rather than assuming every capture was fetched live. Neither fresh content nor neatly formatted Markdown proves that a claim is correct.

For this workflow, use the article text you already own, collect the outside sources that need checking, and let the agent spend its effort comparing them. Add Firecrawl where it makes that collection easier.

Try it on one page first

The easiest starting point is the Firecrawl Playground. Choose Scrape, enter a public article URL, and request Markdown. Check that the result contains the article you expected rather than a login screen, error message, or empty page.

If you work with an AI assistant, follow Firecrawl’s MCP setup guide for your client. MCP is the connection that lets an assistant call services such as Firecrawl. Once connected, try this:

Use Firecrawl to read this public article: [paste the URL]. Summarize its main instructions and list the external sources it references. Do not edit anything yet.

You do not need to migrate your website or install anything in its frontend. The research happens separately from your publishing system.

Pick a small batch worth checking

Choose three to five articles that people still use. Installation guides, download pages, and documentation for actively changing software are good starting points.

Be specific about what you want checked. “Does this guide still link to the official installer?” is a more useful task than “Update my entire website.”

Use public pages for the first run. Leave unpublished drafts, customer information, and private admin pages out of the selection. If you already have the article text in your repository or CMS, give the assistant that text and use Firecrawl for the external sources.

Compare your article with the source

For a software tutorial, start with the project’s official documentation, release notes, or repository. Read the relevant source page instead of treating a search snippet as proof.

Here is a prompt you can adapt:

Review these articles: [paste three to five URLs]. Use Firecrawl to read them and check their installation requirements, download links, and support claims against official sources. Fetch each shared source once. Limit this review to 20 page fetches and tell me what remains unchecked when you reach that limit. For each issue, show the original passage, explain the problem, link to the evidence with the date checked, and suggest a small correction. Separate confirmed problems from historical instructions and things you could not verify. Return a report without editing or publishing anything.

The request limit in that prompt is an instruction to the agent, not an account spending cap. Set service limits where available and check usage during the run.

A useful report should let you make a decision without repeating all the research. For example:

Finding What to do
A documentation link redirects to the project’s new address Verify the destination and update the link.
Current documentation requires a newer operating system Add the requirement with its source and version context.
An old tutorial accurately describes an older release Keep that context; add a dated note if readers need current guidance.
A source times out or blocks access Mark it unverified and investigate before changing the article.

A page returning successfully does not prove its advice is correct. A changed page does not automatically make your article wrong, either.

Make small edits you can review

Read the findings and choose the corrections that help your readers. Ask the assistant to apply those specific changes, or make them yourself in your CMS.

For a site stored in Git, put the changes in a pull request so you can compare the old and new text. For WordPress or another CMS, use its revision and preview tools. Keep existing article URLs and preserve the original publication date. If the change needs a date, add a visible update note.

Preview the edited page, open the changed links, and check any commands in an appropriate test environment. Documentation research alone does not prove that a command works. Run your site’s normal checks before publishing.

What this caught on my site

The first pilot checked selected claims in ten articles and produced seven corrections across six articles. Some changes were small but useful: the Docker Guide got a current documentation link, and WSL 2 got a note separating the historical walkthrough from current installation guidance.

Other articles needed their history preserved. The Linux Gaming Guide kept its old XanMod commands, with a notice pointing readers to current instructions. A later archive review also found moved repositories and unavailable video embeds. The detailed archive audit records the coverage and unresolved checks.

These reviews combined Firecrawl research, other checks, and editorial decisions. They did not establish that every claim on the site was current.

Keep the work small and useful

Save the source URLs, retrieval dates, and reports so you know what was checked. Reuse a source capture within a review instead of fetching it for every article. Check your account’s usage and current limits before a larger crawl; different operations and output formats can have different costs.

For a larger documentation section, Firecrawl’s crawl guide explains page limits and result retrieval. Wait for the job to finish and inspect errors as well as returned pages before describing the coverage.

You can also use this approach to compare your FAQ with your public product documentation or prepare a content inventory before a redesign. Start with one concrete question, gather the relevant pages, and review the result before changing your site.

A handful of well-supported corrections is a useful outcome. You do not need an automatic publishing system to get there.