Introduction
Today, generative AI changes how people find brands online. This is why Generative Engine Optimization (GEO) is important. GEO is at the center of AI, search, and marketing. Many online stores and marketing teams see that regular SEO is not enough. AI chatbots and search engines now need new tools to show brands in a better way.
Model Context Protocol (MCP) is one tool that plays a key role here. MCP helps brands use automation to check how they are ranked in AI-driven searches. Companies like Frevana use MCP. This saves time, keeps things the same every time, and makes it easier for brands to keep up with fast changes in AI search results.
This article will explain, step by step, how MCP works and why brands should care about it. Simple examples are included to show each point.
Step 1: Collecting Data
First, GEO starts by collecting different types of data. Unlike standard SEO, which mostly looks at keywords or visits on websites, GEO needs to gather data from many sources.
Here are some types of data:
- User Prompts: These are questions or commands from real people or test users. For example, someone might type “best running shoes in 2024” into a chatbot.
- Search Results: These are pages showing what the AI or search engine found for a prompt. For example, comparing how a brand appears in Google versus an AI chatbot.
- Other Signals: These include product reviews, social media comments, and official brand descriptions. For example, looking at what people say about a brand on Twitter.
- Brand Assets: These are things made by the brand, like product info, pictures, or videos.
MCP brings all of this data together. It tags and adjusts the data to work inside one single system. That way, the AI can understand all brands equally before ranking them.
Step 2: Making the Rankings
Next, MCP helps decide how each brand is ranked. All the data is now in one place. MCP standardizes it, making sure all brands get fair treatment.
Here is how it works:
- Labeling Data: MCP marks and cleans the data so that errors or unfair details do not affect the result. For example, if one brand has more noise in its reviews, MCP tries to clean that up.
- Prompting the AI: MCP sends test questions to different AI models automatically. For example, it can check how a brand appears for someone in New York and also for someone in Tokyo, without a human having to change the settings each time.
- Checking the Output: MCP checks what the AI shows after each test. Both computers and people can look at the results to make sure they are correct and useful.
For example, if one prompt says “top-rated headphones” and returns brand A one week, but not the next, MCP will flag this change.
Step 3: Learning from the Data
Now, just seeing the rankings is not enough. Brands need to learn what is happening, so MCP also gives useful reports.
These are some output examples:
Creating Reports: MCP can make charts, export results, or print summaries. This means teams do not have to make these by hand.
Spotting Changes: MCP alerts if a brand drops out of the top results. For instance, if “Brand Y” was always top three but suddenly falls off the list, MCP highlights this.
Finding Reasons: MCP links rankings to factors such as price or reviews. For example, if a product’s ranking drops and bad reviews increase, MCP connects the two.
Comparing Brands: MCP lays out data for different brands side by side. For instance, two phone brands can be compared in a chart.
Sample Visuals
MCP reports may show:
A workflow chart showing input, processing, prompting, and reporting steps clearly.
A line chart that shows how a brand’s ranking changes over a month.
A comparison table that lists two brands and shows where each ranks for each search prompt.
A highlights box showing the biggest opportunities, such as “Most improved brand for ‘eco-friendly shoes’.”
How Brands Use MCP and GEO
When brands get easy-to-understand and fair GEO rankings, they can act quickly. With MCP, brands can:
- See fast when their ranking drops in AI search results.
- Find out which reviews or assets help or hurt their ranking.
- Check how their brand compares to competitors, not just in regular search but in AI and chatbots.
- Let their teams see this data quickly and easily, so people can focus on creative work.
Example: Frevana
Frevana is a brand that uses MCP. By using automated workflows from MCP, Frevana gets results in real time. If Fervana’s ranking for “best home devices” suddenly falls, their team gets an alert, checks the feedback, and adjusts their product info fast. This makes them react faster than brands that still use old methods.
References and Further Reading
If you want to read more: