For most analysts & marketers, a quick data grab often spirals into figuring out what the best dimensions for this question are. Are these dimensions and metrics even compatible, and then, the most important question: what does this actually tell me? 

Here’s where the Analytics Model Context Protocol (MCP) Server comes into play. It acts as a bridge between Gemini and GA4, enabling conversational analytics via natural language querying of GA data without the need for manual data handling or custom API code.

By combining conversational AI with GA4 data, the MCP server lowers the technical barriers to analytics and accelerates decision-making across teams. Instead of digging through GA4 menus, you can now get an answer to solve business questions within a fraction of the time.

The tool is a great driver for data democratisation; the ability to make data & insights available to all in a business through a conversational layer can result in better-informed and faster decisions across teams. 

This can have an impact for every type of user.

You’ve launched a campaign and are waiting to understand the performance. Asking

‘How is my landing page for campaign X performing?’ will result in a breakdown of sessions, users and conversion rates within seconds.

You already know exactly what data you need, it’s a matter of providing the specifics. Provide the breakdown dimensions, filters and receive an output without the load times and back and forth of a GA4 exploration.

Reduce time spent navigating dashboards and get fast answers to high-level questions such as “What’s the biggest selling product this quarter? ”

Query

Response

We can take this a step further. 

A common use of GA4 is post-campaign analysis, but as mentioned earlier, it takes time to roam through the UI, add the right dimensions, and manually analyse results.

Say we wanted to utilise the MCP server to fast-track this process? How might that look?

Fast campaign performance metrics:

In this example, the response didn’t just return top-line numbers—it also provided a short analysis, a framework for improvement, and even a hypothesis to explore further.

Let’s assume I’m a new user of GA, I ask, “I don’t understand the GA4 menus, just tell me if my summer sale performed well?”.

Within seconds, I get a simple, digestible summary including total revenue, purchases, conversion rate, and AOV. No setup, no filters, just the insight I needed. It’s a glimpse of how accessible analytics becomes when powered by conversational AI.

There is additional value in using this tool outside of fast & easy retrieval: it has the conversational layer of Gemini! For any of these outputs you can ask any follow up questions that enable you to understand the data more or retrieve more data to obtain a conclusion. 

For those less familiar with GA4, who find the various dimensions & metrics somewhat intimidating, it's a fantastic tool for realtime learning. You can get the answer to your business questions and observe which dimensions and metrics are deemed best to use, building confidence whilst gathering data. 

For example, if you wanted to understand how people are finding your site but don’t know where to start:

Rather than just providing a result, the MCP Server explains how to approach the question -  suggesting relevant GA4 dimensions like source, medium, and campaign