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Join Coverage Analytics: Record Quality Audit + String Normalisation + Equal-Width Histogram
x402
Join Coverage Analytics: Record Quality Audit + String Normalisation + Equal-Width Histogram is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-15).
Runs three local analytical reports on supplied join coverage records (matched_rows, source_rows): a record quality audit, string normalisation, and an equal-width histogram.
Price / call$0.01
Success—
StatusUnknown
How this service works
Join coverage — Record quality audit + String normalisation + Equal-width histogram: Record quality audit + String normalisation + Equal-width histogram 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 for the supplied join coverage rows: (1) a record quality audit flagging nulls, out-of-range values, and data issues in matched_rows and source_rows; (2) a string normalisation report on text fields like dataset_group; and (3) an equal-width histogram binning the chosen numeric field (matched_rows or source_rows) into the specified number of bins (1–30, default 5). All analysis is performed locally with no external lookups.
Example use cases
ETL pipeline join quality check
“I have 6 daily join coverage records from my ETL pipeline across two dataset groups — can you run a record quality audit and flag any nulls or anomalies in the matched_rows and source_rows? Groups are 'sales' and 'inventory', dates run from 2026-11-01 to 2026-11-06, matched rows range from 800 to 1200, source rows range from 1000 to 1500.”
Histogram of match rates by bin
“Take these 8 join coverage records and bin the source_rows field into 5 equal-width buckets so I can see the distribution — rows span groups 'alpha' and 'beta' from 2026-09-01 to 2026-09-08, with source_rows between 500 and 4000 and matched_rows between 400 and 3800.”
String normalisation on dataset group labels
“My join coverage data has inconsistent dataset_group labels and I want them normalised — can you run the string normalisation report on these 5 records? Rows are from 2026-12-01 to 2026-12-05, groups include ' Group A ', 'group-b', 'GROUP_C', and matched_rows and source_rows are all valid integers between 100 and 900.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-09-202?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a combined data quality audit, string normalisation, and histogram for join coverage data (matched_rows, source_rows) in a single low-cost call. It is ideal for data engineers profiling ETL join outputs, QA workflows checking match completeness, or analysts exploring the distribution of join rates across dataset groups. Best suited for small-to-medium batches (up to 200 rows) where all three report types are needed together without external enrichment or prediction.
Side effects
- Rows array empty or missing — validation error
- More than 200 rows supplied — rejected with max-items error
- bins value outside 1–30 range — validation error
- date field does not match YYYY-MM-DD pattern — rejected
- matched_rows or source_rows outside ±1,000,000,000,000 — rejected
- row_id missing or empty string — validation error
- Payment not completed — 402 response, no report generated
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| bins | integer | |
| rows | array | |
| field | string |
Tags
x402
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