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Time Series Completeness — Record Quality Audit + Numeric Row Ordering
x402
Time Series Completeness — Record Quality Audit + Numeric Row Ordering is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-28).
Audits record quality and orders rows numerically for time series completeness data (observed_points vs expected_points), producing two independent analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Record quality audit + Numeric row ordering: Record quality audit + Numeric row ordering for supplied time series completeness records (observed_points, expected_points). 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 independent analytical reports: (1) a record quality audit flagging issues in the submitted time series completeness rows (e.g. nulls, anomalies, malformed entries), and (2) a numeric row ordering ranking the rows by the selected field (observed_points or expected_points). Both reports are computed locally on the supplied data with no external lookups.
Example use cases
Completeness gap audit for sensor dataset
“Run a record quality audit and numeric row ordering on these 5 sensor completeness records — each has a date, group, observed_points, and expected_points — so I can see which rows have quality issues and how they rank by observed_points.”
Multi-group completeness validation
“I have completeness data from two dataset groups across 30 dates with observed and expected point counts for each day — can you do a record quality audit and numeric ordering by expected_points to flag any bad rows and sort them?”
Daily pipeline completeness report
“Every morning I want to check yesterday's time series completeness records across all my pipeline groups — audit the quality and rank the rows numerically by observed_points so I can quickly spot anomalies.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-007?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both a record quality audit AND numeric row ordering in a single call for time series completeness data with observed/expected point counts. It is ideal for data pipeline monitoring workflows where you want to validate completeness records and rank them by volume in one step, without needing live lookups, predictions, or external data enrichment. Prefer alternatives if you only need a single report type or are working with non-completeness time series data.
Side effects
- More than 200 rows submitted — request exceeds maximum row limit
- Missing required fields (row_id, date, observed_points, expected_points) cause validation rejection
- Invalid date format (not YYYY-MM-DD) triggers schema error
- Null values for both observed_points and expected_points may reduce audit usefulness
- Payment not included or insufficient USDC causes 402 rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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