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Null Coverage Analytics — Grouped Numeric Summary & Keyed Dataset Comparison
x402
Null Coverage Analytics — Grouped Numeric Summary & Keyed Dataset Comparison is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-16).
Runs two analytical reports on null coverage data: a grouped numeric summary by category and a row-keyed comparison between two datasets, analyzing missing_values and total_values fields across up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Null coverage — Grouped numeric summary + Keyed dataset comparison: Grouped numeric summary + Keyed dataset comparison for supplied null coverage records (missing_values, total_values). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.
Output
Returns two analytical reports: (1) a grouped numeric summary aggregating missing_values or total_values statistics (e.g. count, mean, min, max, sum) broken down by dataset_group, and (2) a keyed dataset comparison matching rows between the primary and secondary (other) datasets by row_id to surface differences in the chosen field.
Example use cases
Monthly null rate audit by pipeline group
“Here are my null coverage records from October for two pipeline groups — group-a and group-b — each row has a row_id, date, missing_values, and total_values. Can you give me a grouped numeric summary of missing_values by dataset_group and also compare this month's records against last month's baseline dataset?”
Comparing data quality snapshots before and after ETL fix
“I have two snapshots of null coverage data — one before our ETL patch and one after — each with up to 150 rows including row_id, dataset_group, date, missing_values, and total_values. Run a grouped numeric summary on the post-patch data and compare it row-by-row against the pre-patch snapshot to see which rows changed.”
Cross-team dataset completeness comparison
“Our team A and team B both submitted null coverage reports with the same schema — row_id, dataset_group, date, missing_values, total_values. Can you summarize total_values by group for team A's data and then compare team A's records against team B's records to spot discrepancies?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-03-098?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both a grouped statistical summary AND a row-keyed comparison between two null coverage datasets in a single call, all computed locally without any external lookups. Ideal for data quality audits where you want to compare two snapshots of the same dataset schema side by side while also getting group-level aggregate statistics on missing or total value counts.
Side effects
- Missing required fields (row_id, dataset_group, date, missing_values, total_values) causes validation error
- Exceeding 200 rows in either dataset array returns an error
- Invalid date format (not YYYY-MM-DD) causes rejection
- Null or out-of-range numeric values may be excluded from aggregation
- Empty rows array returns an error (minItems: 1)
- Payment failure or insufficient USDC balance blocks processing
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| other | array |
Tags
x402
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