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Data Catalog Coverage — String Normalisation + Date by Category Pivot
x402
Data Catalog Coverage — 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).
Runs two analytical reports on data catalog coverage records: string normalisation of field values and a date-by-category pivot, for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Data catalog coverage — String normalisation + Date by category pivot: String normalisation + Date by category pivot for supplied data catalog coverage records (documented_fields, total_fields). 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 normalisation report that cleans and standardises text values in the specified field across the supplied catalog coverage rows, and (2) a date-by-category pivot report that aggregates the chosen numeric measure (documented_fields or total_fields) across dataset_group categories and dates. Both reports are computed locally against the supplied data with no external lookups.
Example use cases
Coverage reporting across dataset groups
“I've got 80 catalog coverage records with row_id, dataset_group, date, documented_fields, and total_fields. Can you normalise the string values and pivot them by date and category so I can see how documentation coverage varies across groups over time? Use documented_fields as the focus field.”
Data quality audit for a governance team
“Our data governance team needs to audit field documentation rates. I'll give you 150 catalog records — each has a dataset_group, a date, and counts of documented_fields and total_fields. Please run string normalisation and a date-by-category pivot on total_fields so we can spot underdocumented datasets.”
Monthly catalog coverage summary
“Can you analyse this month's catalog coverage snapshot? I have around 40 rows with dataset_group, date, documented_fields, and total_fields. Run the string normalisation and date pivot reports on documented_fields so I can share a clean summary with the data team.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-21-035?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need two bundled analytical operations — string normalisation and a date-by-category pivot — applied to data catalog coverage records (documented_fields, total_fields) in a single low-cost call. It is ideal for data governance or catalog quality workflows where you supply your own rows and need fast, local computation without external data lookups, predictions, or professional assessments. Prefer it over general analytics APIs when your domain is specifically data catalog coverage metrics grouped by dataset_group and date.
Side effects
- Fewer than 1 row or more than 200 rows supplied — request rejected
- Missing required fields (row_id, dataset_group, date, documented_fields, total_fields) in any row — validation error
- Invalid date format (must match YYYY-MM-DD) — schema validation failure
- Numeric values outside allowed range (±1,000,000,000,000) — rejection
- Invalid field enum value (must be 'documented_fields' or 'total_fields') — schema error
- Payment of 0.01 USDC not completed — request blocked
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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