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Time Series Completeness Analyzer — Record Quality Audit, String Normalisation & Numeric Distribution
x402
Time Series Completeness Analyzer — Record Quality Audit, String Normalisation & Numeric Distribution 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).
Runs three analytical reports (record quality audit, string normalisation, and numeric distribution) on supplied time series completeness records containing observed and expected data points, for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Record quality audit + String normalisation + Numeric distribution: Record quality audit + String normalisation + Numeric distribution for supplied time series completeness records (observed_points, expected_points). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.
Output
Returns three independent analytical reports for the supplied rows: (1) a record quality audit flagging malformed, missing, or inconsistent records; (2) a string normalisation report for fields like dataset_group; and (3) a numeric distribution summary for the selected field (observed_points or expected_points), covering statistics such as min, max, mean, and spread — all computed locally with no external lookups.
Example use cases
Data pipeline completeness health check
“I have daily completeness records for two dataset groups over the past month — each row has a date, observed_points, and expected_points. Can you run the record quality audit, string normalisation, and numeric distribution reports on all of them so I can spot any data gaps or quality issues?”
Numeric distribution profiling for expected points
“Run a numeric distribution analysis plus record quality audit on my time series completeness data focusing on the expected_points field — I want to understand the spread and catch any bad records across my dataset groups.”
Data quality review before ingestion
“Before I ingest these time series completeness records into our warehouse, can you audit them for quality issues, normalise the string fields like dataset_group, and show me the distribution of observed_points? I have about 150 rows with dates, row IDs, and both observed and expected point counts.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-193?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three simultaneous offline analytical reports — quality audit, string normalisation, and numeric distribution — specifically for time series completeness data structured around observed vs expected point counts. It is ideal for pre-ingestion data profiling, completeness monitoring, and batch quality checks on up to 200 rows. Prefer it over generic validators when you need bundled, multi-report analytics in a single low-cost call without any live data lookup or external dependencies.
Side effects
- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, date, observed_points, expected_points) cause row-level rejection
- Invalid date format (not YYYY-MM-DD) fails pattern validation
- Numeric values outside allowed range (-1e12 to 1e12) are rejected
- Empty rows array is not accepted (minItems: 1)
- Invalid field enum value (not observed_points or expected_points) causes a request error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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