Ask your AI assistant real questions about your data and get answers you can rely on — an MCP server with 45 analysis tools that pick the right statistical method themselves and tell you how much to t
Ask your AI assistant real questions about your data — and get answers you can actually rely on.
You have a spreadsheet. You want to know what's in it, what's driving a number, who your best customers are, where things are heading. So you ask Claude.
Today it will write a little program on the spot and give you an answer. Usually that's fine. When it isn't, you can't tell: it might have used the wrong kind of test for your data, or graded its own prediction on the same rows it learned from, or told you one thing causes another when it only happens alongside it. The answer looks equally confident either way.
This tool sits between your assistant and your data and takes those judgement calls out of its hands. You ask the question in plain English; a proper analysis engine works out the right method from your actual data, runs it, and tells you how much to trust the result — or says the data can't answer the question, rather than making something up.
You asked a question. You didn't have to know that comparing four groups against a revenue figure calls for a particular statistical test, or that this data broke the assumptions of the obvious one and needed a different test instead. That was worked out from the data, and written down in the answer — so if someone asks six months from now how you got that number, it's on the record.
There are 45 tools under the hood, but you never call them. You ask; your assistant picks. In practice that means questions like:
From the project README.
Add the radar badge to your README — it shows your project was picked up by MCP Radar and links to this page:
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