How to use MCP to get more data from the tools you already use

Model Context Protocol (MCP) makes it simpler to work with the information already sitting inside your search engine optimization and advertising instruments. As a substitute of digging via reviews, exporting information, and stitching spreadsheets collectively, you’ll be able to ask an AI assistant questions that might in any other case take hours to reply.

That makes an MCP server helpful for evaluation that requires discovering patterns throughout pages, key phrases, visitors, rankings, and different information factors.

Right here’s how I’ve been utilizing MCP servers to get extra out of instruments like Ahrefs, Google Analytics, and Google Search Console.

Utilizing MCP to uncover what’s driving competitor development

Just a few months in the past, I got down to be taught why a shopper’s competitor was rising so quick. Ahrefs confirmed me their profitable pages and key phrases, however not the underlying development or how the items match collectively.

• Had these pages climbed steadily for months or spiked all of a sudden?
• Did the expansion observe to a web page sort or subdirectory, or did the entire web site profit from an algorithm replace?

I linked Claude to the Ahrefs MCP server and requested it to dig in. Minutes later, I had a breakdown of which pages have been new within the final six months, their estimated visitors, the key phrases driving it, and the way key pages had grown month over month.

Right here’s what I discovered about this competitor:

• They constructed a brand new part of their web site with extremely centered service pages.
• Worldwide content material they’ve been constructing for the final three years began to take off.
• They flipped the change on area redirects for 10+ companies they acquired years in the past.

Ahrefs doesn’t conceal this information, but it surely’s scattered throughout reviews and totally different filters. Usually, you’d export dozens of reviews and mix the information with pivot tables. For month-to-month, weekly, or day by day comparisons, that course of is gradual and irritating.

MCP helps you to pull and reshape information that already lives within the instruments you pay for, however is a ache to achieve via the traditional interface. Builders who work with APIs gained’t discover this new. For everybody else, it opens up a complete new world of knowledge evaluation.

MCP is an open customary that lets AI assistants connect with exterior instruments and information sources. Anthropic open-sourced it in November 2024, and assist has since expanded throughout main AI platforms.

An MCP server connects an AI assistant to a selected instrument or service, letting you question its information in plain language. For instance, Semrush offers several MCP servers.

Many different AI platforms and instruments assist MCP as effectively, together with N8N and Copilot Studio.

Dig deeper: WebMCP explained: Inside Chrome 146’s agent-ready web preview

The questions your advertising dashboards can’t simply reply

That is the place MCP can earn its place in your workflow. The on a regular basis stuff — what a web page ranks for, what number of backlinks it has — is straightforward to lookup anyplace. The exhausting questions are those the place the reply is hidden within the information, and you need to dig for it.

Listed here are the sorts of prompts I’ve used:

• “Utilizing the Ahrefs MCP server, assist me perceive why this web site is doing so effectively, particularly within the final 12 months. Is it particular pages or key phrases?”
• “Utilizing Ahrefs information, are you able to inform which pages are new within the final six months and what their estimated visitors is?”
• “Examine the backlink profiles and development tendencies for these 5 websites, and inform me which one is choosing up referring domains the quickest and the way.”
• “Throughout these 20 key phrases in my area of interest, which websites rank most frequently and in the most effective positions?”

These are the questions that usually come up in evaluation work, equivalent to competitor analysis and post-algorithm critiques. You are able to do this manually, but it surely takes hours of busywork to piece all the pieces collectively.

MCP works throughout your advertising stack

Many advertising platforms now ship MCP servers, together with Semrush, DataForSEO, Serpstat, Buffer, and VidIQ.

What you’ll be able to pull depends upon what every instrument exposes via its API, however as soon as it’s linked, all you need to do is ask it questions.

Google Analytics is among the most helpful MCP servers I’ve used. GA4 is highly effective however exhausting to navigate. Half the time, you already know the reply is in there, however don’t wish to construct one other exploration report to search out it.

The Google Analytics MCP server connects to the GA4 Information API, so you’ll be able to simply ask. Just a few examples:

• Diagnosing a drop:
  • “Natural visitors fell about 20% final week. Which pages misplaced essentially the most, and is it concentrated in a selected nation or gadget sort?”
• Discovering the mismatch:
  • “Which pages have excessive engagement time however a low conversion charge?”
  • “Are you able to give me visitors for the final half-hour, by the minute?”

Should you handle a number of accounts, you’ll be able to run queries throughout a number of properties directly:

• “Throughout all my properties, what are their respective information retention settings (2 months vs. 14 months)?”
• “What’s the mixed visitors for [site 1] and [site 2] for the final week, damaged down by Natural, Direct, and Referral?”
• “Which net information streams in my account wouldn’t have Enhanced Measurement enabled?”

One among my go-to makes use of for this MCP server is analyzing visitors after algorithm updates and making an attempt to grasp what the influence could be, equivalent to which pages have declined or improved. I’ve additionally used it to evaluation visitors throughout a shopper’s total web site to search out alternatives for content material refreshes and determine precise anomalies that stand out in opposition to regular fluctuations.

Querying the API instantly additionally bypasses the 5,000-row export restrict of the GA4 interface. The largest disadvantage to the Analytics MCP is that it’s quite complicated to set up: it requires a Google Cloud challenge and an OAuth shopper.

Google Search Console MCP

Ahrefs offers you third-party estimates, and GA4 reveals what individuals did after they landed. To finish the stack, add Google Search Console to get queries, impressions, clicks, CTR, and common place.

Google has an official GA4 MCP server, however not an official Search Console MCP server. A number of community-built GSC MCP servers can be found on GitHub. Should you’re connecting to a shopper’s property, examine what entry the MCP server requests earlier than putting in it. 

I exploit one from Suganthan Mohanadasan, which is open-source and allows you to run the MCP server regionally from your individual pc. The setup is identical because the Google Cloud challenge and OAuth course of for the GA4 MCP server, so put aside a while to get it going.

As soon as it’s linked, it handles the questions the Search Console interface makes troublesome. Listed here are a few examples I like:

• “Give me a well being examine throughout my GSC properties.” One immediate covers each property directly, as a substitute of opening every account to examine.
• “What subjects is [site] lacking content material for primarily based on adjoining question information?” This has given me some glorious subjects that don’t exist in my GSC account however are associated to the question information.

Like the opposite MCP servers I’ve talked about, the information is there, however MCP helps you to get it out simply and in a manner the GSC dashboard can’t.

Monitoring how AI talks about your model

Should you care about GEO, MCP may monitor your AI search visibility. Each Ahrefs and Semrush provide AI metrics accessible via their MCP servers.

For instance, Ahrefs’ Model Radar (an costly paid improve, sadly) tracks how manufacturers seem in AI solutions throughout main surfaces like Google’s AI Overviews and AI Mode, ChatGPT, Perplexity, Gemini, and Copilot. It tracks share of voice in opposition to rivals, reveals model mentions and citations, and identifies which pages and domains these platforms cite most in your house.

Semrush affords an MCP server that features entry to helpful AI metrics, too. You’ll be able to ask it to summarize the place rivals are seeing shifts in AI visitors and the way your web site compares.

Many different AI instruments are constructing MCP servers, too. For instance, Waikay.io, an superior AI model monitoring instrument from Dixon Jones, simply launched an MCP server for patrons on Stage 2 accounts.

You’ll be able to velocity up evaluation by connecting a number of sources. As a result of the AI holds context throughout instruments, you’ll be able to ask a query that might usually imply three logins and a spreadsheet to reconcile.

For instance: Which weblog posts misplaced visitors final month, what key phrases do they rank for, and that are value refreshing first? One supply has the visitors, one other has the rankings, and the assistant pulls each right into a single reply.

Reporting is the apparent use. Individuals wire up MCP servers via connectors like N8N and platforms like HubSpot to drag the week’s information, summarize it, and drop it right into a doc or e-mail on a schedule. You continue to need a human studying the output, however the grunt work of gathering and formatting turns into a lot simpler.

Dig deeper: AI agents in SEO: A practical workflow walkthrough

What to know earlier than utilizing MCP

None of that is new to builders. Anybody snug with an API may pull and reshape this information lengthy earlier than MCP existed.

However the information is now extra accessible, and we will ask LLMs questions on it. MCP exposes capabilities that lived behind code and makes them obtainable to individuals with zero developer abilities.

Even for builders, having the ability to discuss to an LLM concerning the information will help them perceive its that means or encourage concepts they might by no means have provide you with on their very own.

Just a few limitations are value figuring out:

• These MCP servers run on the instrument’s API, so huge requests eat into your API credit and plan limits. Heavy queries add up.
• The AI solely sees what the API exposes. If an information level isn’t obtainable via the instrument’s API, you’ll be able to’t use it in evaluation.
• Confirm the output. The info is right, however the AI can nonetheless misinterpret it or oversimplify. Sanity-check something you’ll act on or put in entrance of a shopper.
• Inform it to make use of the MCP. Assistants don’t all the time know whether or not to name the linked instrument or simply search the net, so I begin prompts with “Utilizing the Analytics MCP” to make sure the suitable route is taken.
• Be affected person with huge requests. A challenge spanning many key phrases or a number of websites can take some time to come back again and, relying in your LLM plan, could burn via credit and require day by day pauses till they replenish.

The place to begin

Decide one instrument you already pay for and one query its dashboard makes annoying. Join the MCP server to your LLM of alternative, ask, and examine the reply to doing it by hand. 

That first question is more likely to open your eyes to the chances. After that, you’ll see what number of of your data-pull duties are a single immediate away.