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Null Coverage Audit: Record Quality + String Normalisation + Grouped Numeric Summary
x402
Null Coverage Audit: Record Quality + String Normalisation + Grouped Numeric Summary is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-14).
Audits data quality by computing null/missing value coverage, normalises strings, and produces grouped numeric summaries for up to 200 rows across three independent reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Null coverage — Record quality audit + String normalisation + Grouped numeric summary: Record quality audit + String normalisation + Grouped numeric summary for supplied null coverage records (missing_values, total_values). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.
Output
Returns three independent analytical reports: (1) a record quality audit flagging null coverage issues per row, (2) a string normalisation report for text fields like row_id and dataset_group, and (3) a grouped numeric summary aggregating missing_values and total_values statistics by dataset_group — all computed locally with no external lookups.
Example use cases
Dataset completeness check before ETL pipeline
“I need to audit data quality before loading into our warehouse — here are 120 rows each with row_id, dataset_group, date, missing_values, and total_values. Run the null coverage audit on the missing_values field and give me all three reports: record quality, string normalisation, and grouped numeric summary.”
Group-level null rate comparison across teams
“We have data from three different teams labelled in the dataset_group field — can you analyse these 80 rows for null coverage on total_values, and give me a grouped numeric summary so I can compare each team's data completeness?”
Monthly data quality review for reporting
“I want to do our monthly data quality review — here are 200 rows dated across October with missing_values and total_values filled in. Run all three analytical reports on the missing_values field so I can see where our null coverage problems are by group.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-03-194?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a combined, cost-efficient data quality analysis covering null coverage auditing, string normalisation, and grouped numeric summarisation in a single call for up to 200 rows of tabular data with missing/total value fields. It is ideal for lightweight pre-ETL data validation, periodic data quality reviews, or comparing null rates across dataset groups — especially when no live lookups, predictions, or external actions are required and all analysis can be performed locally on supplied data.
Side effects
- Input array exceeds 200 rows — request will be rejected
- Missing required fields (row_id, dataset_group, date, missing_values, total_values) cause validation errors
- Invalid date format (not YYYY-MM-DD) triggers pattern validation failure
- Numeric values outside the allowed range cause schema rejection
- Empty rows array returns an error
- Payment of 0.01 USDC not completed results in 402 response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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