Summary
From is there data on this to a downloaded folder and a first analysis, without leaving the chat.
Search returns the fields that decide whether a dataset is worth using, download count, last updated and usability rating, so the choice happens before anything is downloaded. The EDA piece is a prompt rather than an analysis: it hands your model a structured starting point and the model writes the code.
What it is
A server over the Kaggle API that searches datasets, downloads and unzips them locally, and generates an exploratory-analysis prompt for one.
What you get
- Dataset search by keyword, returning up to 10 results as JSON
- Each result carrying ref, title, subtitle, download count, last updated and usability rating
- Download and unzip of a dataset by its owner/dataset-slug reference, for example kaggle/titanic
- A destination path of your choosing, defaulting to ./datasets/<dataset_slug>/
- A generated EDA prompt covering data loading, missing-value checks, visualisations and summary statistics
Requirements
Python 3.10 or newer, a Kaggle account and an API token. Credentials come either from KAGGLE_USERNAME and KAGGLE_KEY or from a kaggle.json in the standard location. Runs locally, in Docker, or through Smithery.
Setup effort
Build from source — clone the repository and build it, then point your client at the binary