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Join Coverage Analytics: Record Quality Audit, String Normalisation & Date Pivot
x402
Join Coverage Analytics: Record Quality Audit, String Normalisation & Date Pivot 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 three local analytical reports on join coverage data (matched_rows, source_rows): record quality audit, string normalisation, and date-by-category pivot — up to 200 rows per call.
Price / call$0.01
Success—
StatusUnknown
How this service works
Join coverage — Record quality audit + String normalisation + Date by category pivot: Record quality audit + String normalisation + Date by category pivot for supplied join coverage records (matched_rows, source_rows). 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 computed locally: (1) a record quality audit highlighting missing, null, or out-of-range values in matched_rows and source_rows; (2) a string normalisation report identifying inconsistencies in text fields like dataset_group; and (3) a date-by-category pivot summarising the selected numeric field across dates and dataset groups. No external lookups or predictions are included.
Example use cases
ETL pipeline join quality check
“I've got join results from our ETL pipeline — can you run a record quality audit and date pivot on these rows? row-1, 2024-03-01, group-alpha, matched=450, source=500; row-2, 2024-03-02, group-beta, matched=380, source=400. Analyse matched_rows.”
Dataset group string normalisation audit
“Please run the string normalisation and record quality reports on my join coverage data: row-A on 2024-06-15 group-sales matched_rows=1200 source_rows=1300, row-B on 2024-06-16 group-Sales matched_rows=900 source_rows=1000 — I think there might be casing inconsistencies in the group names.”
Monthly join coverage category pivot
“Can you pivot our monthly join coverage metrics by date and dataset_group? Here's the data: row-1 2024-07-01 group-north matched=5000 source=5200, row-2 2024-07-02 group-south matched=4800 source=5100, row-3 2024-07-01 group-north matched=4900 source=5050. Field is source_rows.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-09-206?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 join coverage records with matched_rows and source_rows and need all three analytical outputs (quality audit, string normalisation, date pivot) in a single cost-efficient call ($0.01 USDC). Prefer it over separate tools when you want bundled local analytics with no external data dependencies or side effects.
Side effects
- Fewer than 1 row or more than 200 rows submitted — request rejected
- Invalid date format (not YYYY-MM-DD) — validation error
- matched_rows or source_rows outside allowed numeric range — error
- Missing required fields (row_id, dataset_group, date) — schema validation failure
- Invalid field enum value (not matched_rows or source_rows) — rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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