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Feature Availability Analytics: Daily Sum Series + Date by Category Pivot
x402
Feature Availability Analytics: Daily Sum Series + Date by Category Pivot is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-20).
Computes two analytical reports — a daily sum time series and a date-by-category pivot table — from supplied feature availability records (populated_features and expected_features) for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Daily sum series + Date by category pivot: Daily sum series + Date by category pivot 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 analytical reports derived from the supplied feature availability rows: (1) a daily sum time series showing the aggregated total of the selected field (populated_features or expected_features) for each date, and (2) a date-by-category pivot table cross-tabulating the same field by date and dataset_group, both computed locally on the up-to-200 supplied rows with no external lookups.
Example use cases
Daily feature completeness trend report
“I have 3 months of daily feature availability logs with populated_features and expected_features across two dataset groups — can you give me a daily sum time series and a date-by-category pivot on the populated_features field so I can see completeness trends over time?”
Dataset group coverage comparison
“Run both a daily sum series and a date-by-category pivot on expected_features for these feature availability records across my group-a and group-b datasets — I want to compare how expected feature counts break down by group per day.”
Spot-check feature population gaps
“I want to audit last week's feature availability data to see where populated_features dips — can you compute the daily totals and a category-by-date breakdown for these rows so I can spot which groups had low coverage?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-143?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 feature availability records and need both a daily aggregated time series AND a category-by-date pivot in a single low-cost call ($0.01 USDC). Ideal for data quality monitoring, feature engineering audits, or dataset completeness reporting where you supply your own data and need two complementary analytical views without any external data lookups or predictions.
Side effects
- Exceeding the 200-row limit results in rejection or truncation
- Rows missing required fields (row_id, date, populated_features, expected_features) will cause validation errors
- Malformed date strings (not YYYY-MM-DD format) will fail pattern validation
- Numeric values outside ±1,000,000,000,000 range will be rejected
- Empty rows array not accepted (minimum 1 row required)
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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