DataQuality Assess

Overview

Assess the quality of a tabular dataset (CSV file or JSON array of records). Returns per-column statistics (data type, missing count/percentage, unique values, numeric min/max/mean/std, categorical mode/top values), overall summary (total rows, columns, complete cases), and warnings for columns with >20% missing values, zero variance, potential outliers (>3 SD from mean), and highly correlated numeric pairs (|r| > 0.95). Pure local computation with pandas -- no external API calls. Useful for pre-analysis data validation and quality control.

Status

The most recent probe succeeded. Safe to call from the agent today.

Last probed

1d ago

Last success

1d ago

Last latency

94ms

Schema

JSON Schema the agent (or your API call) must match.

{
  "properties": {
    "columns": {
      "description": "List of column names to assess. Default: all columns.",
      "items": {
        "type": "string"
      },
      "type": [
        "array",
        "null"
      ]
    },
    "data": {
      "description": "Input dataset: either a JSON array of records (list of dicts) or an absolute path to a CSV file.",
      "oneOf": [
        {
          "description": "JSON array of records (list of dicts), each dict representing one row.",
          "items": {
            "type": "object"
          },
          "type": "array"
        },
        {
          "description": "Absolute path to a CSV file on the local filesystem.",
          "type": "string"
        }
      ]
    }
  },
  "required": [
    "data"
  ],
  "type": "object"
}

Examples (1)

{}

Identifiers

Catalog ID

tu_DataQuality_assess

Tool name

DataQuality_assess

Added

2026-05-01 00:58Z

Tags

tooluniverse