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Join Coverage Analytics — Daily Sum Series + Base-100 Series
x402
Join Coverage Analytics — Daily Sum Series + Base-100 Series 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 (daily sum series and base-100 indexed series) for join coverage data tracking matched_rows and source_rows across dates and dataset groups.
Price / call$0.01
Success—
StatusUnknown
How this service works
Join coverage — Daily sum series + Base-100 series: Daily sum series + Base-100 series 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 analytical reports: (1) a daily sum series aggregating the selected field (matched_rows or source_rows) per date across dataset groups, and (2) a base-100 indexed series normalizing the selected field relative to a specified base index row, enabling trend comparison. Up to 200 input rows are supported per call.
Example use cases
ETL pipeline join quality monitoring
“I want to see how my ETL job's join coverage has trended over the past week — here are the daily matched_rows and source_rows for my 'customers' and 'orders' dataset groups. Can you generate the daily sum series and a base-100 indexed series on matched_rows, starting from the first day?”
Comparing source vs matched row counts over time
“I have 30 days of join records for three dataset groups and I want to analyze source_rows rather than matched_rows — can you compute both the daily sum series and the base-100 index series for source_rows, starting from index 0?”
Normalizing join coverage for stakeholder reporting
“My team needs a normalized view of join coverage for a board report — I have 60 daily rows across two groups with matched_rows and source_rows. Please run the daily sum and base-100 reports on matched_rows using day 5 as the base index.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-09-138?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have tabular join coverage data (matched_rows vs source_rows) keyed by date and dataset group and need both a daily aggregated sum series and a base-100 normalized trend series in a single call. It is ideal for data engineers monitoring ETL join quality, analysts building time-series reports on data pipeline completeness, or anyone who wants two complementary analytical views (absolute and indexed) on join coverage without standing up their own analytics engine.
Side effects
- Input array exceeds 200 rows — returns validation error
- Missing required fields (row_id, date, dataset_group, matched_rows, source_rows) on any row — returns schema error
- Invalid date format (not YYYY-MM-DD) — returns pattern validation error
- base_index value out of range (>=number of rows) — returns index out-of-bounds error
- Numeric values outside allowed range (±1 trillion) — returns range validation error
- Payment not provided or insufficient — returns 402 Payment Required
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| base_index | integer |
Tags
x402
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