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Join Coverage Analytics: Daily Sum Series + Date by Category Pivot
x402
Join Coverage Analytics: Daily Sum Series + 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).
Analyzes join coverage data (matched_rows, source_rows) by generating a daily sum time series and a date-by-category pivot table, returning two independent analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Join coverage — Daily sum series + Date by category pivot: Daily sum series + 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
Two independent analytical reports: (1) a daily sum time series showing aggregated values of the selected field (matched_rows or source_rows) across all records per day, and (2) a date-by-category pivot table cross-tabulating the selected field by date and dataset_group. Both reports are computed locally from the supplied rows with no external lookups.
Example use cases
ETL pipeline join rate audit
“I have 4 days of join coverage records for two dataset groups and I want to see both a daily sum time series and a pivot of matched_rows by date and group — can you run those two reports for me? The rows span 2026-10-01 to 2026-10-04 with groups alpha and beta, and I want to focus on matched_rows.”
Source vs matched row comparison
“Run a daily sum series and a date-by-category pivot on my source_rows data — I have about 150 records spread across three dataset groups over the past month and I want to understand how source row counts vary by group and day.”
Data quality monitoring over time
“I want to monitor how my join coverage has been trending — can you take my 200 rows of join coverage data with matched_rows and source_rows across multiple groups and give me both a daily aggregated time series and a pivot table breaking it down by dataset_group and date?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-09-143?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need two complementary analytical views — a time series and a pivot — on join coverage metrics in a single $0.01 call. Ideal for data engineers monitoring ETL pipeline health, auditing matched vs source row ratios over time, or exploring category-level trends without setting up a local analytics environment. Best when you already have structured join coverage records with dates and group labels and want quick, self-contained analysis without external data lookups.
Side effects
- Missing required row fields (row_id, date, dataset_group, matched_rows, source_rows) returns a validation error
- Date strings not matching YYYY-MM-DD pattern are rejected
- Submitting more than 200 rows exceeds the maxItems limit and is rejected
- Submitting 0 rows (empty array) fails minItems constraint
- Payment failure or insufficient USDC balance prevents processing
- Numeric values outside ±1,000,000,000,000 are rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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