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Join Coverage Analytics: Record Quality Audit, String Normalisation & Numeric Distribution
x402
Join Coverage Analytics: Record Quality Audit, String Normalisation & Numeric Distribution 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 independent analytical reports on join coverage data (matched_rows, source_rows): a record quality audit, string normalisation, and numeric distribution analysis — all locally, on up to 200 supplied rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Join coverage — Record quality audit + String normalisation + Numeric distribution: Record quality audit + String normalisation + Numeric distribution 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 covering: (1) a record quality audit flagging completeness and validity issues across the supplied rows, (2) string normalisation analysis of text fields like dataset_group, and (3) a numeric distribution summary of the selected field (matched_rows or source_rows). All analysis is performed locally on the supplied data with no external lookups.
Example use cases
ETL pipeline join quality check
“I just ran a data pipeline and got join coverage results across 12 dataset groups for October 2026 — can you audit the record quality, normalise the dataset_group strings, and show me the numeric distribution for matched_rows? I'll give you all the row data with their dates and counts.”
Source row completeness audit
“Can you analyse these 50 join coverage records from our data warehouse — I need a quality audit plus numeric distribution on the source_rows field to see how evenly matched our data is across groups.”
Multi-group string normalisation review
“We have join coverage rows from 8 different dataset groups with inconsistent naming — can you run string normalisation on them alongside a record quality audit and distribution check? I'm feeding you up to 80 rows with matched_rows and source_rows values by date.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-09-193?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 join coverage records and need three simultaneous analytical perspectives — quality audit, string normalisation, and numeric distribution — in a single call without any external lookups or live data enrichment. It is ideal for offline ETL validation workflows, batch data quality reviews, and situations where all data is already in hand and needs structured local analysis for a flat $0.01 USDC fee.
Side effects
- More than 200 rows submitted — exceeds the per-call row limit
- Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) on any row
- Invalid date format (must match YYYY-MM-DD pattern)
- Numeric values out of the allowed range (±1,000,000,000,000)
- Empty rows array — minItems:1 not satisfied
- Invalid field enum value (must be 'matched_rows' or 'source_rows')
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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