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Feature Availability Analytics — Category Frequencies & Daily Sum Series
x402
Feature Availability Analytics — Category Frequencies & 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 2026-10-19).
Computes two analytical reports (category frequencies and daily sum series) for up to 200 feature availability records defined by populated_features and expected_features fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Category frequencies + Daily sum series: Category frequencies + Daily sum series for supplied feature availability records (populated_features, expected_features). 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 for the submitted rows: (1) a category frequency table showing how feature availability values are distributed across categories, and (2) a daily sum series aggregating the selected field (populated_features or expected_features) by date — both computed locally without external lookups.
Example use cases
Data completeness audit by group
“I have a dataset with daily feature availability records for two groups, 'group-a' and 'group-b', across October 2026 — can you run the category frequency and daily sum reports on the populated_features column so I can see which groups are filling in their data?”
Expected vs populated feature trend
“Run analytics on my 120 feature availability records for Q3 — I want the category frequencies and daily sum series for the expected_features field so I can spot which days had unusually low expected feature counts.”
Pipeline feature coverage monitoring
“I'm monitoring a data pipeline and have 200 rows of feature availability logs with dates and dataset groups — generate the category frequency distribution and the daily sum series for populated_features so I can check for gaps or anomalies in coverage.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-102?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have structured feature availability records (with populated and expected feature counts per date and dataset group) and need both a category frequency summary and a daily aggregation series in a single low-cost call. Ideal for data quality audits, pipeline monitoring, and completeness reporting on up to 200 rows without needing live lookups or external data enrichment.
Side effects
- More than 200 rows submitted — exceeds max row limit
- Missing required fields (row_id, date, populated_features, expected_features) on any row — validation error
- Invalid date format (must match YYYY-MM-DD pattern) — schema rejection
- Numeric values outside allowed range (±1,000,000,000,000) — validation error
- Empty rows array — minItems constraint violation
- Invalid field enum value (must be 'populated_features' or 'expected_features') — schema rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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