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Join Coverage Analytics: String Normalisation + Daily Sum Series
x402
Join Coverage Analytics: String Normalisation + Daily Sum 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).
Computes string normalisation and daily sum time series for join coverage metrics (matched_rows and source_rows) across up to 200 supplied rows, returning two analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Join coverage — String normalisation + Daily sum series: String normalisation + Daily sum 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
Returns two analytical reports: (1) a string normalisation report covering the selected join coverage field values across dataset groups, and (2) a daily sum series aggregating the chosen field (matched_rows or source_rows) by date. No external lookups or live data are used — analysis is performed entirely on the supplied rows.
Example use cases
ETL pipeline join quality audit
“I have a week's worth of daily join coverage records from two dataset groups — give me a string normalisation report and the daily sum series for matched_rows so I can see where my pipeline is dropping rows.”
Data reconciliation daily rollup
“Here are 50 join coverage records with dates, matched_rows, and source_rows for groups 'invoices' and 'payments' — run the string normalisation and daily sum series analysis on source_rows so I can reconcile my datasets.”
Coverage trend monitoring across groups
“I want to monitor join coverage trends across my dataset groups for Q3 — can you compute the daily sum series and string normalisation for matched_rows across these 120 records I'm uploading?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-09-027?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 string normalisation pass and a daily sum time series in one call for up to 200 rows. It is ideal for data engineers auditing ETL pipelines or reconciling datasets without needing live lookups or external data.
Side effects
- Fewer than 1 or more than 200 rows submitted — request rejected
- Date values not in YYYY-MM-DD format — validation error
- matched_rows or source_rows values outside ±1,000,000,000,000 — schema rejection
- Missing required fields (row_id, dataset_group, date) — input validation failure
- Invalid field enum value (not 'matched_rows' or 'source_rows') — rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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