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Join Coverage Analytics — Record Quality Audit, String Normalisation & Keyed Dataset Comparison
x402
Join Coverage Analytics — Record Quality Audit, String Normalisation & 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 2026-10-20).
Runs three local analytical reports on join coverage data (matched_rows, source_rows): a record quality audit, string normalisation, and keyed dataset comparison for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Join coverage — Record quality audit + String normalisation + Keyed dataset comparison: Record quality audit + String normalisation + Keyed dataset comparison 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
Three independent analytical reports generated from the supplied join coverage data: (1) a record quality audit flagging missing values, nulls, and out-of-range figures in matched_rows and source_rows; (2) a string normalisation report on dataset_group and row_id fields; (3) a keyed dataset comparison between the primary and secondary row arrays, highlighting value differences by row_id and dataset_group. All computed locally with no external lookups.
Example use cases
ETL pipeline join coverage validation
“I've got two snapshots of my ETL join results — the original run has matched_rows of 500, 620, 410 and source_rows of 550, 650, 450 for groups alpha, beta, gamma on 2025-01-10 to 2025-01-12, and the reprocessed run has matched_rows of 510, 618, 412 for the same groups and dates. Can you run the full quality audit, string normalisation, and keyed comparison on both datasets to flag any discrepancies?”
Data warehouse match rate anomaly check
“Run a record quality audit on my warehouse join coverage data — I have 8 rows across two dataset groups called 'orders_v2' and 'returns_v2' dated from 2024-06-01 to 2024-06-08, with matched_rows ranging from 1000 to 8500 and source_rows from 1100 to 9000. I want to see if there are any quality issues or string normalisation problems in the group labels.”
Before-and-after join coverage comparison
“Compare my before and after join coverage datasets from last quarter — the 'before' set has 4 rows for dataset_group 'sales' with matched_rows 300, 450, 200, 380 and source_rows 320, 470, 210, 400, and the 'after' set has the same structure but matched_rows are 310, 448, 205, 390. Give me all three analytical reports including the keyed dataset comparison.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-09-207?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a lightweight, low-cost ($0.01 USDC) batch analytical pass on join coverage data without relying on external lookups or live data connections. It is ideal when you have two versions of the same keyed dataset and want simultaneous quality auditing, string normalisation, and value-level comparison in one call. Prefer it over bespoke scripts when you need repeatable, structured reports for up to 200 rows of join coverage metrics across dataset groups and dates.
Side effects
- More than 200 rows submitted — endpoint rejects with validation error
- Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) — schema validation failure
- Invalid date format (not YYYY-MM-DD) — pattern validation error
- matched_rows or source_rows outside allowed numeric range — validation rejection
- Empty rows array — minItems constraint violation
- Malformed JSON body — parse error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| other | array |
Tags
x402
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