Using SEO MCP Servers: Complete Guide for Marketers

SEO MCP servers offer a new way of doing things. Instead of trying to sort tables and charts to make sense of your content’s performance metrics, you can just ask.

Data is the backbone of a solid SEO strategy. I’ve spent a lot of time building reports and sorting through spreadsheets to understand content performance trends so I can make better marketing decisions.

But in order to see a complete picture of your SEO standings, you need to bring together information from multiple charts and tables and sift through thousands of data points. Even with custom reports I built myself, I’ve often found myself squinting at my screen, trying to see the bigger picture.

Model Context Protocol (MCP) enables LLMs like Claude to securely access and analyze your SEO data, then communicate the findings to you in simple, accessible language.

You can use the Semrush API to bring its massive database right into your AI conversations: real SEO data on domains, keywords, and more.

Incorporating MCP into your tech stack could revolutionize your SEO data analysis workflow. I’m here to walk you through the basics of understanding MCP and how to prompt for the content insights you need.

What Is an MCP Server?

Put simply, an MCP server is a program that allows LLMs (like Claude or ChatGPT) and other AI applications to connect to third-party data sources.

For the technologically curious, here’s a more detailed breakdown.

Model Context Protocol (MCP) defines the standards and rules for communication between LLMs and other applications. It facilitates AI integrations by:

• Securing connections between AI applications and third-party tools
• Translating information from data sources into a standardized format AI models can understand

An MCP server is the program that connects to the third-party data source you want to integrate with your preferred LLM. It handles retrieving the data for the AI model, often through an API.

Put it all together and an MCP server SEO workflow looks something like this:

Another way to think of it is to imagine you’re eating at a restaurant, but you and the kitchen staff don’t speak the same language.

You know what’s on the menu, but to place your order, you have to give it to a waiter who speaks your language and the language of the kitchen staff.

The waiter takes your order to the kitchen and tells them what you want, then the kitchen staff fetches the ingredients for your meal and combines them on your plate. The waiter then brings the plate to you.

In this analogy, you represent the LLM. The waiter is Model Context Protocol, the kitchen staff is the MCP server, and the ingredients are the SEO data you want to analyze.

What Are the Benefits of MCP Servers for SEO?

MCP servers can be a huge efficiency booster for SEO data analysis. Even seasoned content marketers have to spend a lot of time combing through analytics reports to spot trends.

LLMs can help with this without an MCP server through context engineering, but this requires you to download your data and upload it to the LLM. Depending on the context window of the model you’re using, you might have to upload your data in multiple batches to process it all.

Integrating your preferred LLM with Semrush, Google Analytics, Search Console, and other SEO data sources lets you prompt for real-time SEO data analysis whenever you like, without extra steps.

MCP also lets you interact with your SEO data using conversational language. This gives you the chance to ask follow up questions to discover new insights and easily assess complex data relationships.

What Are the Drawbacks of MCP Servers for SEO?

The biggest potential pitfall of using MCP for SEO is the same problem that comes with using AI for anything — hallucination.

You can’t take everything an LLM tells you at face value, even when it has access to your SEO data. Verifying the LLM has access to the data it says it does is one sanity check that can help prevent mistakes.

For example, if you’ve connected Claude to Search Console and prompted for keyword volume data, you’ll know that any information Claude returns is hallucinated, because Search Console doesn’t provide keyword volumes.

One other thing to keep in mind is that in most cases, your MCP server is accessing your SEO data using API calls.

Depending on the platform you’re connecting to, there may be costs associated with those API calls, and/or limits to API usage. Check the terms for your SEO tools before connecting to them with MCP to avoid unexpected expenses.

If you’re using a free LLM for SEO data analysis via MCP, you might also hit usage limits when using complex prompts to sift through large amounts of data.

I experienced this when testing some of the prompts in this article with the free version of Claude.

Finally, it’s best to be aware that using MCP carries the risk of prompt injection, a cybersecurity attack that uses malicious prompts to cause unintended responses in LLMs.

Following cybersecurity best practices and using tools like Microsoft’s Prompt Shields help mitigate this risk.

How to Use MCP for SEO (Real-Life Use Cases with Prompts)

We’ve talked about a lot of theoretical concepts so far. I’m a hands-on learner, so I want to show you some actual ways you can use MCP for SEO with a few concrete examples.

1. Install and Configure an SEO MCP Server

To keep things simple, I’m going to start out with a very straightforward MCP server setup with a handful of beginner-friendly SEO prompts. Then I’ll share how you can start building out your MCP workflow to include multiple SEO data sources for more comprehensive insights.

I’ve connected the Claude Desktop app with Semrush using an MCP server from MCP.so, a community-based directory of MCP servers.

To use the Semrush API, you’ll need the Semrush Business plan. You’ll want to purchase credits for the Standard API.

There are a few things to keep in mind when choosing an MCP server from a directory like this:

• Programming language: You will need to download one or more programming languages to install an MCP server. If there’s a language you already use or are more familiar with, you might want to prioritize a server that uses that language.
• Credibility: Check for user ratings or GitHub stars to evaluate the quality of the MCP server.
• Documentation: The MCP server should include detailed instructions on how to install and configure it, as well as troubleshooting guidelines.

The exact installation and configuration instructions will depend on which server you choose. Here are the basic steps you’ll most likely need to take:

1. Obtain the API credentials for the SEO data source you want to connect to Claude (or another LLM). In this case, you’ll need a Semrush Standard API key.
2. Install any dependencies, such as Node.js or Python.
3. Create a configuration file for Claude and add your MCP server configuration. The servers I’ve used in this guide (and all the other ones I looked into) provide the exact code you need to use, so it’s a simple copy/paste job. This tutorial walks you through the easiest way to create your Claude configuration file without having to use the command line.

Make sure to restart Claude after saving your configuration file. If everything is set up properly, you’ll see the MCP servers you’ve installed when you click on the “Search and tools” icon below the search bar.

Once you have it up and running, you can start prompting for SEO recommendations.

2. Allow Claude to Use MCP Tools

Whenever you submit a prompt referencing data accessed via MCP, Claude will ask you for permission to perform each action it takes.

You can click “Allow always” to avoid seeing this modal popup every time you prompt for SEO data analysis, but I recommend using the “Allow once” button.

Especially if you’re connecting to a third-party tool that has API limits or costs, these reminders will help you avoid accidentally overusing your MCP server.

3. Prompt Claude for SEO Insights Using Semrush Data

There are tons of different SEO-related prompts you can use with an MCP server. These are some examples of prompts I’ve tested for analyzing data from Semrush.

Find Low-Competition Keywords

Choosing the right keywords is the first step to creating content that ranks. I like looking for terms that have a nice balance of search volume and keyword difficulty.

Low-competition keywords are easier to rank for, meaning if you publish quality content, you’re more likely to end up on page one than if you go after a high-competition keyword.

Here’s the prompt I used to ask Claude for a list of keywords that balances difficulty and search volume:

Note that I went with a pretty general keyword to start. If your topic is much more niche, you might need to adjust the numbers in the prompt to turn up enough options for consideration.

Claude returned a list of ten keywords just as I asked, along with their Semrush data: search volume, keyword difficulty, and cost per click. It also provided a summary, highlighting some of the top keywords it recommends:

Analyze Your Domain Against Competitors

Knowing how your content stacks up against competitors helps you identify opportunities to pull ahead and stand out to potential customers. Semrush is a great tool for competitor analysis, and with MCP, you can have Claude give you a leg up.

Here’s the prompt:

This lets you easily see all the relevant data for your top competitors side by side in seconds, saving you valuable time.

Claude’s detailed response lets me see everything I need to know about how Exploding Topics performs compared to similar sites without having to manually look up those domains in Semrush.

It also gave me some actionable steps to improve performance, like targeting more commercial intent keywords, building high-value keyword clusters, and improving backlinking strategy.

4. Connect Multiple MCP Servers

Most content marketers use several SEO tools to plan and create content. You can connect multiple MCP servers to your LLM to bring all your SEO data into a central location where you can analyze it with the help of AI.

I connected a Search Console MCP server to Claude in addition to the Semrush MCP server I demonstrated in the previous step. I can now use data from both of the sources in the same conversation to conduct a more comprehensive SEO analysis.

Using MCP with Google Search Console requires you to create API credentials using Google Cloud Platform. The Google Search Console MCP server I’m using has a helpful video tutorial that walks you through this process, as well as additional installation instructions.

When connecting a second MCP server to Claude, make sure to check its dependencies. For example, the two servers I’ve used in this post use different programming languages, so I had to make sure both were installed on my computer.

You can use one Claude desktop configuration file for multiple MCP servers, but you won’t be able to just copy and paste the code like you did for the first one. The configurations for both servers should be nested under "mcpServers": {

Here’s how my configuration file looks after connecting a second MCP server:

{
  "mcpServers": {
  "semrush-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "github:mrkooblu/semrush-mcp"
      ],
      "env": {
        "SEMRUSH_API_KEY": "your-api-key-here",
        "LOG_LEVEL": "info"
      }
  },
  "gscServer": {
      "command": "/FILE/PATH/HERE",
      "args": ["/FILE/PATH/HERE"],
      "env": {
        "GSC_CREDENTIALS_PATH": "/FILE/PATH/HERE",
        "GSC_SKIP_OAUTH": "true"
      }
    }
  }
}

With that out of the way, here are some prompts you can use to analyze Semrush and Search Console data together in Claude.

Compare Page Performance Over Time

Seeing how a specific page’s performance has changed over a certain period can help you determine if it’s time to update and re-optimize it.

For instance, if a previously top-performing blog post has a sudden decline in rankings and impressions, you may need to do some SEO housekeeping and re-optimize for your target keywords.

Likewise, a drop in traffic and clicks might indicate that competitors’ articles are more attractive to searchers. You might want to try a new title to grab potential readers’ attention and stand out from the other search results.

Here’s the prompt I used to compare blog post search performance over time, using metrics from both Semrush and Search Console. I used Typing Mind, which works the same way as Claude:

The response was a highly detailed analysis that included:

• Growth metrics for clicks, impressions, average position, and click-through-rate from January to July 2025
• SERP feature analysis and indexing check
• Top performing queries for this blog post, as well as new queries surfacing in July
• Traffic sources and click distribution
• Recommendations for continued growth, such as optimizing for related queries and building topical authority

Identify Keyword Query Trends and AI Overview Opportunities

Finding trends in the queries driving traffic to your site is useful for making decisions about future topics to pursue.

Knowing how users are finding your site can help you build out topic clusters around those queries. On the other hand, understanding which keywords are declining can help you identify topics to steer away from, or content you might need to re-optimize for search or clicks.

Here’s the prompt I wrote to identify keyword trends:

Claude’s response was very detailed. It went beyond my request to identify increasing and decreasing traffic trends and also provided analysis for seasonal traffic fluctuations.

I then followed up with this prompt:

This gave me a list of keywords with high potential to trigger AI Overviews, along with their keyword volumes and difficulties. With this list, I have a starting point for topic ideas I can write about to try to maximize my content’s visibility.

Find Secondary Keywords

Keyword clustering enables you to target multiple queries in a single piece of content. It’s a foundational SEO technique, and there are plenty of tools out there to help you with it.

But with an MCP server, you can use AI to build keyword clusters around your existing content to expand the reach of posts that are already performing well.

Here’s the prompt I used to generate secondary keywords for top content:

For each of the top three posts it identified, Claude recommended primary and long-tail keywords, and also highlighted content gaps and other related terms I might want to optimize for.

I then asked:

Claude’s response was a table with each keyword’s search volume and difficulty. It also highlighted the most promising secondary keywords with the best balance between volume and difficulty.

Enhance Your SEO Workflow With AI-Powered Insights

By connecting Semrush and Search Console to Claude with MCP, I was able to pinpoint content weaknesses, enhance my content strategy, and better understand my audience — all with a few simple prompts.

Using an MCP server makes SEO data analysis fast and simple. It saved me time combing through tables and charts in two separate platforms and also quickly showed me connections between separate data types.

If you want to start digging into your Semrush data with help from Claude, the first step is to sign up for Semrush Business and purchase some API credits. Then see for yourself how MCP can revolutionize your SEO workflow!