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Join Coverage Analytics: Record Quality Audit, String Normalisation & Weighted Mean
x402
Join Coverage Analytics: Record Quality Audit, String Normalisation & Weighted Mean 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 analytical reports on join coverage data (matched_rows, source_rows): a record quality audit, string normalisation, and a secondary-measure weighted mean — all offline, up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Join coverage — Record quality audit + String normalisation + Secondary-measure weighted mean: Record quality audit + String normalisation + Secondary-measure weighted mean 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: (1) a record quality audit flagging issues in the supplied join coverage records, (2) string normalisation results for text fields such as dataset_group, and (3) a weighted mean computed over the chosen secondary measure (matched_rows or source_rows). All results are derived locally from the submitted data with no external lookups.
Example use cases
ETL pipeline join quality check
“I have 80 rows from my nightly ETL run with matched_rows and source_rows counts per dataset group — can you run a record quality audit, string normalisation, and weighted mean for matched_rows on all of them so I can spot any data issues?”
Dataset group string cleanup audit
“My join coverage export has inconsistent dataset_group labels across 120 rows — can you run the string normalisation report alongside the quality audit and weighted mean for source_rows to help me clean things up?”
Join coverage weighted mean for reporting
“For my weekly data pipeline report, can you compute the secondary-measure weighted mean for matched_rows across these 40 join coverage records and also run the quality audit and string normalisation at the same time?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-09-205?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three simultaneous analytical reports — quality audit, string normalisation, and weighted mean — on join coverage data in a single cost-efficient call. Ideal for ETL monitoring, data pipeline QA, or batch auditing of matched vs source row metrics where no live lookups or external predictions are required. Best for datasets up to 200 rows processed in one shot.
Side effects
- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) causes row rejection
- Invalid date format (not YYYY-MM-DD) fails pattern validation
- Numeric values outside ±1,000,000,000,000 are rejected
- Empty rows array is not accepted (minItems: 1)
- Payment of 0.01 USDC must be provided; missing payment returns 402
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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