detect_quality_issues
Run a data-quality audit to detect common issues like duplicates, missing values, data type problems, and whitespace, then group findings by severity.
Instructions
Run a focused data-quality audit and return issues grouped by severity.
Detects duplicate rows, all-missing and high-missing columns, constant
columns, likely identifier columns, numbers stored as text, dates stored as
text, columns mixing numeric and text values, leading/trailing whitespace,
and empty (whitespace-only) strings. Each issue carries a column (or null for
table-level), an issue code, a severity (high/warning/info), and
a plain-language explanation.
Use this when the user cares specifically about cleanliness, is preparing data for modeling, or asks "is anything wrong with this data?".
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": {
"path": "<path>"
},
"name": "detect_quality_issues"
}
}Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | ||
| max_rows | No |