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Join Coverage Analytics: Record Quality Audit, String Normalisation & Numeric Row Ordering
x402
Join Coverage Analytics: Record Quality Audit, String Normalisation & Numeric Row Ordering is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-23).
Runs three local analytical reports on user-supplied join coverage data (matched_rows, source_rows): a record quality audit, string normalisation, and numeric row ordering — for up to 200 rows per call.
Price / call$0.01
Success—
StatusUnknown
How this service works
Join coverage — Record quality audit + String normalisation + Numeric row ordering: Record quality audit + String normalisation + Numeric row ordering 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 structured analytical reports for the supplied join coverage rows: (1) a record quality audit flagging nulls, out-of-range values, and format issues; (2) a string normalisation report for dataset_group and other string fields; and (3) a numeric row ordering report ranking rows by the selected field (matched_rows or source_rows). All computed locally with no external lookups.
Example use cases
ETL pipeline join quality check
“I just ran an ETL join and have these 5 rows of coverage data — row-1 through row-5 across groups alpha and beta, dates from 2025-01-01 to 2025-01-05, with source_rows ranging 10–50 and matched_rows ranging 8–45. Can you audit their record quality, normalise the group name strings, and order them by source_rows?”
Data team coverage report review
“We need to validate our weekly join coverage batch before the Monday review — here are 12 rows with row IDs, dataset groups, dates in 2025-03, and matched_rows plus source_rows values. Run the quality audit, string normalisation, and numeric ordering on matched_rows so the team can see any issues.”
Spotting nulls and bad values in join metrics
“Some of our join coverage records might have null or out-of-range values in matched_rows — can you audit the record quality and also give me string normalisation and numeric ordering for these 8 rows, sorted by matched_rows, so I can find the problematic ones?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-09-196?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled local analytical reports (quality audit, string normalisation, numeric ordering) on join coverage data in a single low-cost call, without any external lookups or live data enrichment. It is best suited for ETL validation workflows, data engineering pipelines, and pre-load data quality checks where matched_rows and source_rows are the core metrics.
Side effects
- Fewer than 1 or more than 200 rows submitted — validation error
- Row missing required fields (row_id, dataset_group, date, matched_rows, source_rows) — schema rejection
- Date field not in YYYY-MM-DD format — pattern validation failure
- Numeric field exceeds ±1,000,000,000,000 — range error
- Invalid field enum value (not matched_rows or source_rows) — enum validation error
- Payment of 0.01 USDC not received — 402 Payment Required
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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