What Changed on Your Website Last Week?

Most teams can't answer that question. Getting answers from your website feels like it should be instant. For some people, it is.

You've probably heard them say it: just ask the AI, and it hands you whatever you need in under a minute. Sure, if your site is small and every page is public. But if you're reading this, chances are you've tried it yourself. Either the assistant churned for 45 minutes and ate through your usage limits, or it simply missed what you were looking for.

The overnight-report workaround

For larger sites, a common fix is a scheduled routine: have the AI check the site every day or week and send a notification when something changes. The job might run for a while, but nobody notices because it runs overnight. Problem solved, for some teams. For everyone else, three problems remain:

• Token burn. Premium AI plans can run $100–$200 a month, and not every team can justify that. Even if yours can, do you want half your usage spent crawling every page on a menial task?
• No on-demand answers. An overnight report doesn't help when a stakeholder needs to know what changed in the last hour.
• Gated content. A crawling AI sees your site the way an anonymous visitor does. Anything behind a login is invisible to it.

Going deeper: structured data

What we actually want is for the AI to read real, structured data instead of scraping rendered pages. So how do we get there?

One option is custom code: an internal process that regularly pulls your data and feeds it to a model, plus a reusable skill that gets more efficient with each run and sends reports or alerts based on what it finds. But that means running a custom agent wherever your data lives and figuring out how to secure it. On-demand answers are still hard, and it's one more system you have to maintain indefinitely.

The better path: MCP

The Model Context Protocol (MCP) is an open standard that lets AI assistants connect directly to external systems and data sources. If your site runs on a CMS, this is likely your best option. Your content is already structured, and your CMS probably exposes an API protected by its own built-in permissions.

An MCP server that understands your site's content model gives your AI access through the right credentials (including to pages the public can't see) without compromising security. Instead of spinning up a browser agent to make slow, expensive page-by-page requests, the AI queries the data directly. Searches across even large, complex sites become fast and manageable.

Want a report on new products? You don't wait all night for a crawl. You wait a few seconds. And because the AI reads from an API rather than your page layouts, a redesign won't break it. The data structure only changes when your content model or CMS does.

For a fuller explainer of what MCP is and how it works with a CMS, see What Is an MCP, and Why Does Your CMS Need One?.

Where it falls short, and where it doesn't

This approach has limits. If there's no MCP server for your CMS, or its API doesn't expose what the AI needs to read, you're back to the other methods.

Our MCP server for Sitefinity handles all three problems. With it connected, all we have to do is ask. I use Claude: I ask it to find any content created in the last week, and within seconds I have the answer. From there, I can turn it into a repeatable skill with parameters that filter to what I care about: new products, changed pages, or blog posts from specific authors.

If your site runs on Sitefinity, we can connect it to Claude and walk you through what it finds in your own content, gated pages included. If you're on another CMS, we'll tell you whether an MCP server exists for it and whether it can see what you need.