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Join Coverage Base-100 Series + Secondary-Measure Weighted Mean Analytics
x402
Join Coverage Base-100 Series + Secondary-Measure Weighted Mean Analytics is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).
Computes a base-100 index series and a secondary-measure weighted mean for join coverage data (matched_rows vs source_rows) across up to 200 rows, grouped by dataset_group and date.
Price / call$0.01
Success—
StatusUnknown
How this service works
Join coverage — Base-100 series + Secondary-measure weighted mean: Base-100 series + 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 computed from the supplied join coverage rows: (1) a base-100 index series normalizing the selected field (matched_rows or source_rows) relative to the specified base index row, and (2) a secondary-measure weighted mean across the rows. Results are derived entirely from the supplied data using local analytical engines — no external data is fetched.
Example use cases
ETL pipeline join coverage trending
“I'm running nightly ETL jobs and I have daily matched_rows and source_rows counts for groups 'orders' and 'customers' from 2026-09-01 to 2026-09-30. Please compute a base-100 index series and a weighted mean for matched_rows, with the first row as my base, so I can track join coverage trends over the month.”
Data quality audit across dataset groups
“I need to audit the join coverage across three dataset groups — raw, cleaned, and enriched — for dates in Q3 2026. I have source_rows and matched_rows for each. Can you run both reports — the base-100 series and the weighted mean — using source_rows as the field, starting from base index 0?”
Comparing join efficiency before and after schema change
“We made a schema change on 2026-11-15 and I want to compare join efficiency before and after. I have 20 rows of matched_rows and source_rows data across two groups for November 2026. Please compute the base-100 normalized series and weighted mean for matched_rows, using the first row as baseline, so I can see the impact clearly.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-09-166?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both a base-100 normalization series and a weighted mean computed together from join coverage data (matched_rows / source_rows) in a single paid call. It is ideal for batch analytical reporting on data integration quality metrics without requiring any external lookup or live data sources. Prefer it over general-purpose analytics tools when you need a low-cost ($0.01 USDC for 2 reports), deterministic, offline computation on up to 200 structured rows of join coverage data grouped by dataset_group and date.
Side effects
- Fewer than 1 or more than 200 rows supplied — request rejected
- Invalid date format (not YYYY-MM-DD) on any row — validation error
- base_index out of range (>= number of rows supplied) — computation error
- matched_rows or source_rows values outside allowed numeric range — validation error
- Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) on any row — schema error
- All values for the selected field are null — weighted mean undefined
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| base_index | integer |
Tags
x402
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