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Join Coverage Analytics — Record Quality Audit + Date by Category Pivot
x402
Join Coverage Analytics — Record Quality Audit + Date by Category Pivot is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-15).
Runs two local analytical reports on join coverage data (matched_rows, source_rows): a record quality audit and a date-by-category pivot, for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Join coverage — Record quality audit + Date by category pivot: Record quality audit + Date by category pivot for supplied join coverage records (matched_rows, source_rows). 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
Two analytical reports: (1) a record quality audit highlighting issues such as nulls, out-of-range values, and anomalies in the selected numeric field (matched_rows or source_rows); and (2) a date-by-category pivot table summarizing the chosen metric across dataset_group and date dimensions — all computed locally on the supplied data with no external calls.
Example use cases
ETL pipeline join quality check
“I have output from my ETL join process — about 150 rows with row IDs, dataset groups, dates, and matched_rows vs source_rows counts. Can you run a record quality audit and a date-by-category pivot on the matched_rows field so I can see where coverage is dropping?”
Daily dataset match rate review
“I want to audit today's join results across my two dataset groups — group-a and group-b — over the past month. I've got ~80 rows with dates and matched/source row counts. Please give me a quality audit and pivot the source_rows by date and group.”
Data reconciliation anomaly detection
“Before I sign off on this data reconciliation, can you run a quality audit and a date-by-category pivot on my join coverage records? I have 60 rows from three groups with matched_rows and source_rows so I can spot any mismatched dates or groups with bad coverage.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-09-017?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have join coverage records with matched_rows and source_rows and need two simultaneous analytical views — a quality audit plus a date-by-category pivot — in a single cheap call ($0.01 USDC). Ideal for data engineers validating ETL join outputs, reconciling dataset coverage, or spot-checking match rates across groups and dates without needing live lookups or external data enrichment.
Side effects
- Input array is empty or exceeds 200 rows — validation error returned
- Required fields (row_id, dataset_group, date, matched_rows, source_rows) missing from any row — schema validation failure
- Date field does not match YYYY-MM-DD pattern — rejected with format error
- Numeric values outside allowed range (±1,000,000,000,000) — validation error
- 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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