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Time Series Completeness — Duplicate Key Report + Equal-Width Histogram
x402
Time Series Completeness — Duplicate Key Report + Equal-Width Histogram 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).
Analyzes time series completeness records (observed_points, expected_points) to produce a duplicate key report and an equal-width histogram for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Duplicate key report + Equal-width histogram: Duplicate key report + Equal-width histogram 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 analytical reports: (1) a duplicate key report identifying rows with repeated row_id or date/dataset_group combinations, and (2) an equal-width histogram showing the frequency distribution of the selected field (observed_points or expected_points) across the specified number of bins. Both reports are computed locally from the supplied data with no external lookups.
Example use cases
Audit sensor data completeness logs
“I have 150 daily completeness records for my IoT sensor groups with observed_points and expected_points — can you run a duplicate key check and a 10-bin histogram on the observed_points to see if any dates are duplicated and how the data is distributed?”
Validate ETL pipeline completeness tracking
“My ETL pipeline logs expected vs observed data points each day by dataset group. Can you check these 80 rows for duplicate row IDs and generate an equal-width histogram with 5 bins on the expected_points field?”
Monitor data feed completeness over time
“Here are 200 rows of daily completeness records for our data feeds across multiple groups — run a duplicate key report and a 15-bin histogram on observed_points so I can spot any repeated entries and understand the distribution.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-064?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to simultaneously check for duplicate keys and understand the statistical distribution of completeness metrics across a time series dataset — ideal for data quality audits on completeness tracking records with up to 200 rows. Prefer this over single-report alternatives when both duplicate detection and histogram analysis are needed in one call at low cost.
Side effects
- Exceeding 200 rows returns a validation error
- Invalid date format (not YYYY-MM-DD) causes rejection
- bins value outside 1–30 range returns a validation error
- Empty rows array is not accepted (minItems: 1)
- Missing required fields (row_id, dataset_group, date, observed_points, expected_points) per row causes schema validation failure
- Non-numeric or out-of-range values for observed_points or expected_points may be rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| bins | integer | |
| rows | array | |
| field | string |
Tags
x402
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