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Join Coverage Analytics: Adjacent Row Changes + Secondary-Measure Weighted Mean
x402
Join Coverage Analytics: Adjacent Row Changes + Secondary-Measure Weighted Mean 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).
Computes two analytical reports on join coverage data: adjacent row-to-row changes and a secondary-measure weighted mean, given matched_rows and source_rows per record.
Price / call$0.01
Success—
StatusUnknown
How this service works
Join coverage — Adjacent row changes + Secondary-measure weighted mean: Adjacent row changes + Secondary-measure weighted mean 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) adjacent row changes showing period-over-period differences in the selected metric (matched_rows or source_rows), and (2) a secondary-measure weighted mean summarizing the coverage metric across the supplied rows, grouped by dataset context. Up to 200 rows processed per call.
Example use cases
ETL pipeline join quality audit
“I have 4 days of join coverage records for my ETL pipeline across two dataset groups, with matched_rows and source_rows per day. Can you run the adjacent row changes and weighted mean analysis on matched_rows so I can see where coverage dropped?”
Data warehouse coverage drift detection
“Here are 10 daily join coverage snapshots for my data warehouse tables — row_id, date, dataset_group, matched_rows, and source_rows all filled in. Run both the adjacent-change and weighted-mean reports on source_rows so I can spot any drift.”
Multi-group join rate comparison
“I've got join coverage logs across three dataset groups for the past week. Can you compute the adjacent row-to-row changes plus a secondary weighted mean on matched_rows so I can compare coverage stability across groups?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-09-159?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have tabular join coverage data with matched_rows and source_rows and need both a sequential delta analysis and a weighted mean summary in a single paid call. Ideal for data engineers validating ETL pipeline join rates, analysts monitoring coverage drift, or QA workflows checking join quality across dataset groups. Best for batch sizes up to 200 rows where no external data lookup or prediction is needed — purely local computation on your supplied data.
Side effects
- Missing required fields (row_id, date, matched_rows, source_rows, dataset_group) returns a validation error
- Submitting more than 200 rows exceeds the maxItems limit and is rejected
- Invalid date format (not YYYY-MM-DD) causes schema validation failure
- Null values for both matched_rows and source_rows may produce incomplete or undefined calculations
- Submitting fewer than 1 row (empty array) is rejected
- Invalid field enum value (anything other than matched_rows or source_rows) causes rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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