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Join Coverage Analytics: String Normalisation + Date-by-Category Pivot
x402
Join Coverage Analytics: String Normalisation + Date-by-Category Pivot 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).
Analyzes join coverage records (matched_rows, source_rows) by running string normalisation and a date-by-category pivot report on up to 200 supplied rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Join coverage — String normalisation + Date by category pivot: String normalisation + Date by category pivot for supplied join coverage records (matched_rows, source_rows). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.
Output
Returns two independent analytical reports: (1) a string-normalised view of the supplied join coverage records, and (2) a date-by-category pivot table aggregating the selected field (matched_rows or source_rows) across dataset groups and dates. Covers up to 200 rows per call with no external lookups.
Example use cases
ETL pipeline join quality audit
“Here are 50 join coverage rows from last month's ETL run across group-alpha and group-beta, each with a date, source_rows, and matched_rows — can you normalise the strings and give me a pivot of matched_rows by date and dataset group?”
Dataset matching rate breakdown by category
“I want to understand how well my data joins are performing across three categories — retail, wholesale, and online — over the past two weeks. I have 120 records with dates, source_rows, and matched_rows. Can you run the string normalisation and date-by-category pivot on source_rows for me?”
Daily join coverage trend for two teams
“We have join coverage logs for team-A and team-B from 2026-09-01 to 2026-09-30, about 80 rows total with matched_rows and source_rows filled in. Please run the two analytical reports — string normalisation plus date-category pivot — using matched_rows as the field.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-09-035?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 join coverage records with matched_rows and source_rows metrics per dataset group and date, and need both string normalisation and a date-by-category pivot in a single low-cost call ($0.01 USDC). It is a self-contained analytical compute endpoint — ideal when you do not need live database lookups, external enrichment, or prediction, just local analytical reporting on data you already have.
Side effects
- More than 200 rows supplied — request rejected with validation error
- Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) on any row — row rejected or error returned
- Date not in YYYY-MM-DD format — schema validation failure
- Invalid field selector (not matched_rows or source_rows) — enum validation error
- Empty rows array — minItems constraint violation
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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