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Join Coverage Analytics: Record Quality Audit, String Normalisation & Adjacent Row Changes
x402
Join Coverage Analytics: Record Quality Audit, String Normalisation & Adjacent Row Changes is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).
Runs three local analytical reports (record quality audit, string normalisation, and adjacent row changes) on supplied join coverage data (matched_rows, source_rows) for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Join coverage — Record quality audit + String normalisation + Adjacent row changes: Record quality audit + String normalisation + Adjacent row changes 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: a record quality audit (flagging nulls, outliers, and invalid values), a string normalisation report (identifying inconsistencies in text fields like dataset_group), and an adjacent row changes report (detecting row-to-row deltas in the selected numeric field). All analysis is performed locally on the supplied data with no external lookups.
Example use cases
ETL pipeline join quality audit
“I've got about 50 rows of join coverage data with matched_rows and source_rows across two dataset groups from last month — can you run a full record quality audit, string normalisation check, and adjacent row change detection on them, focusing on the matched_rows field?”
Detect anomalous row-to-row changes
“Can you check my join coverage records for unexpected adjacent row changes in the source_rows field? I have 120 rows across dataset groups 'sales' and 'returns' dated from 2025-01-01 to 2025-04-30.”
String normalisation for dataset group labels
“My join coverage table has inconsistent dataset_group strings across about 80 rows — can you run string normalisation plus a record quality audit on the matched_rows field to flag any issues?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-09-200?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled analytical passes — quality audit, string normalisation, and adjacent row change detection — on join coverage data (matched_rows/source_rows) in a single $0.01 call for up to 200 rows, without needing live data lookups or external enrichment. Ideal for lightweight, self-contained data quality checks in ETL or data pipeline workflows.
Side effects
- More than 200 rows submitted — request will be rejected or truncated
- Invalid date format (not YYYY-MM-DD) causes validation failure
- Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) causes schema rejection
- Non-numeric values in matched_rows or source_rows fields fail type validation
- Empty rows array returns an error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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