describe
Profiles tabular and tensor datasets to summarize schema, statistics, missingness, correlations, rank, and dimensionality, returning bounded outputs and a next-step recommendation.
Instructions
Budgeted profile of a dataset. For tables: schema, label columns, identifier-like and label-like integer columns, per-feature stats, missingness, constant and duplicated features, plus high-dimensional geometry on a sample: strongest correlations, effective rank, condition number, PCA components needed for 50/80/90/95/99% variance (on the chosen scaling AND on the other one), the number of correlation eigenvalues above the Marchenko-Pastur noise edge (how many factors carry more than noise), intrinsic dimension (TwoNN and MLE), distance concentration and hubness. standardize: 'auto' (default) keeps raw scales for data in one shared unit such as pixels / counts / [0,1] intensities and z-scores heterogeneous columns; True/False force it. For tensors: dims, coordinate ranges, fill rate per axis, value distribution. Ends with one consistent hint on what to do next.
Tool call template
Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.
{
"id": 1,
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"arguments": {
"name": "<name>"
},
"name": "describe"
}
}Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | ||
| seed | No | ||
| sample | No | ||
| standardize | No | auto | |
| max_features | No |
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |