Summary
A dataset you have not opened yet, explained.
Two tools are enough because the second is a script runner: once the CSV is a dataframe, any analysis is code the assistant writes and executes, and what comes back is the result rather than the code. The prompt template gives it a starting direction so the first question is not yours to invent.
What it is
An MCP server for interactive data exploration. One tool loads a local CSV into a named dataframe, the other runs analysis scripts against it, and a prompt template starts the session from a file path and a topic.
What you get
- A local CSV loaded into a dataframe you name
- Analysis scripts run against the loaded data
- An explore-data prompt template that takes a CSV path and a topic and drives the exploration
- Datasets far larger than a spreadsheet will open, held as a dataframe rather than read into the conversation
Requirements
Python, and a local setup — the repository ships a setup script for macOS. Files are read from local paths, so the data stays on your machine.
Setup effort
Build from source — clone the repository and build it, then point your client at the binary