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Feature Availability Analytics: Numeric Row Ordering + Date by Category Pivot
x402
Feature Availability Analytics: Numeric Row Ordering + 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).
Generates two local analytical reports (numeric row ordering and date-by-category pivot) for supplied feature availability records with populated_features and expected_features fields, processing up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Numeric row ordering + Date by category pivot: Numeric row ordering + 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 row data: (1) a numeric row ordering ranking rows by the selected field (populated_features or expected_features), and (2) a date-by-category pivot table cross-tabulating values by date and dataset_group. No external data is fetched; all computation is performed locally on the submitted rows.
Example use cases
Dataset completeness audit by group
“I have 50 feature availability records across three dataset groups with dates and both populated and expected feature counts — can you give me a numeric row ordering plus a date-by-category pivot report on populated_features so I can see which groups are lagging?”
Expected vs populated feature tracking over time
“Run a date-by-category pivot and numeric row ordering on my expected_features data — I've got 120 rows with dataset groups like 'model-v1' and 'model-v2' and dates across Q3 2025, so I want to see trends by group and date.”
Feature coverage ranking for ML pipeline
“I need two reports on my ML pipeline feature availability data: rank all 80 rows numerically by populated_features and also pivot them by date and dataset_group category. The rows have fields: row_id, dataset_group, date, populated_features, and expected_features.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-122?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have feature availability tabular data (with populated vs expected feature counts, dates, and group labels) and need both a ranked row ordering and a date-by-category pivot in a single low-cost call. It is ideal for offline/batch analytical summaries of up to 200 records where no live lookup, prediction, or external enrichment is needed. At $0.01 for two reports, it is highly cost-effective for recurring dataset quality checks.
Side effects
- Submitting more than 200 rows returns a validation error
- Missing required fields (row_id, date, populated_features, expected_features) causes schema rejection
- Invalid date format (not YYYY-MM-DD) fails pattern validation
- Null values in both numeric fields may produce incomplete pivot cells
- Empty rows array (minItems:1 violated) returns an error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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