Before you can run analytics or reporting inside your AI agent, you need a MCP.
At the most basic level, MCPs connect your data to your AI agent. But not all MCPs are built the same.
The best ones don’t just bring in your data—they also layer it with context that improves accuracy, create guardrails to ensure cost efficiency, maintain your existing security standards, and much more.
So how do you know which MCP is right for you?
We know many Amazon agencies and brands that are currently shopping around for the best MCP for their data.
They want to run analytics and reporting inside their AI agent—but they don’t want to compromise the sophistication that their teams bring to those tasks.
With the right MCP, it’s possible to have it all: security, sophistication, and data accuracy.
Here’s our checklist on what to look for when choosing a MCP.
Why use a MCP for Amazon data in the first place?
Some brands and agencies think they can just upload CSV files of their data to an AI agent, and run analytics that way.
But directly uploading files to, say, Claude presents a number of challenges.
Not only will you need to continue to redownload and re-upload these files every time you want to re-run your analytics, but you’re also going to get worse-quality responses.
Your data sets will be fragmented, and it will be hard for your AI agent to answer analytics questions that pull from multiple data sets. (For instance, questions that involve both ad spend and inventory levels.)
Plus, your AI agent won’t have any sense of your business context, so it will struggle to interpret the questions you ask about, say, advertising efficiency and missing match types.
What you should look for in a MCP for Amazon
Here’s the checklist we think you should follow when choosing a MCP.
Data completeness. MCPs are still a new category of software, and engineers are working out how to bring all of your Amazon data into an AI agent.
AMC data, for instance, might take a little while longer before it comes to a MCP.
But you want to be sure you have nearly all of your key data sets available within your MCP.
You probably want a MCP that includes data across ads, sales, inventory, finance, Search Query Performance, and more.
So much of analytics on Amazon requires pulling from multiple data sets. You need a very complete data picture to unlock the full value of a MCP.
Of course, just bringing in the data alone is only half the battle.
You also want to be sure all of these data sets are interconnected, so your AI agent pulls out smart answers without getting confused in the process.
Data accuracy. We all know AI is prone to hallucinations. That’s why any old connection between your Amazon data and your AI agent won’t do.
You need to ensure you’re relying on a high-quality connection between the two.
A good MCP will include accuracy metrics, to make sure you’re seeing consistent results every time.
Inevitably, mistakes will still happen—just as they would if you analyzed your data manually.
You should be sure your MCP’s accuracy rate is greater than, or on par with, your own.
Another key component here is whether your AI agent will understand your business context when answering questions.
If you ask questions about your days of cover, will Claude be able to reason through the fact that this refers to a combination of your inventory and your sales velocity?
Will it understand the meaning of terms like ACOS?
For what it’s worth, Intentwise’s AI Gateway provides all of this critical business context to your AI agent as an additional semantic layer.
Your AI agent should be able to work through even complex e-commerce concepts, so you ensure you get the answers and dashboards you actually want.
Cost optimization. Running an AI agent isn’t free. Claude, for instance, charges users based on the number of “tokens,” or strings of text, that it processes to answer your query.
A relatively brief output won’t cost you much at all. But if your MCP isn’t savvy about limiting the tokens Claude needs, you risk getting back gigantic data sets from Claude.
And these data sets will cost you.
Intentwise’s AI Gateway is designed for efficiency, limiting the number of rows that are sent back in individual requests.
We only return the most relevant info for every query. If you ask for the top keywords, for example, we will only return 50 rows, unless you ask for more. That helps you optimize your Claude-specific costs.
Data security. Leading brands and agencies have strict privacy and security procedures. The last thing you want is a MCP that forces you to compromise those standards.
We suggest you look for a MCP server that is built in a contained environment. MCPs that work with Microsoft Azure and AWS Bedrock let you run these LLM analytics inside a contained environment, where they don’t have access to Claude server or the internet.
With Azure and Bedrock, no data leaves your server and goes back to Claude. Instead, these servers run a localized version of the model. You maintain full control.
