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Join Coverage Analytics: Category Frequencies + Date-by-Category Pivot
x402
Join Coverage Analytics: Category Frequencies + 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 2026-10-15).
Generates two analytical reports for join coverage data — category frequency distribution and a date-by-category pivot — for up to 200 supplied rows of matched_rows/source_rows metrics.
Price / call$0.01
Success—
StatusUnknown
How this service works
Join coverage — Category frequencies + Date by category pivot: Category frequencies + 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 analytical reports: (1) a category frequency table showing how often each dataset_group appears and its aggregate statistics for the selected field (matched_rows or source_rows), and (2) a date-by-category pivot table cross-tabulating dates against dataset groups for the chosen numeric metric — all computed locally from the supplied rows with no external lookups.
Example use cases
ETL pipeline join audit by group
“I have daily join coverage records for two dataset groups — 'customers' and 'orders' — with matched_rows and source_rows for each date in October 2026. Can you generate the category frequency breakdown and a date-by-category pivot table for matched_rows so I can see which group has better join coverage?”
Data quality monitoring across sources
“I'm auditing our data warehouse joins and have 50 rows of coverage records with dataset_group, date, source_rows, and matched_rows. Run the category frequencies and date-by-category pivot for source_rows so I can spot which groups are underperforming.”
Ad-hoc join rate comparison across datasets
“Here are 8 join coverage records across dataset groups 'inventory', 'sales', and 'returns' from last week — can you pivot the matched_rows by date and category and also give me a frequency count per category so I can compare join rates?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-09-110?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need quick dual-report analytics (frequency + pivot) on join coverage data without setting up a local analytics environment. Ideal for data engineers auditing ETL pipeline join rates across multiple dataset groups and dates, especially when you want both a categorical frequency breakdown and a time-series pivot in a single API call for up to 200 rows at very low cost.
Side effects
- Fewer than 1 row or more than 200 rows supplied — rejected by minItems/maxItems validation
- Invalid date format (not YYYY-MM-DD) causes schema validation failure
- Numeric values outside ±1,000,000,000,000 range rejected
- Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) cause validation errors
- Invalid field enum value (not 'matched_rows' or 'source_rows') rejected
- Payment failure or insufficient USDC balance prevents call execution
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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