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Feature Availability — Daily Sum Series + Keyed Dataset Comparison
x402
Feature Availability — Daily Sum Series + Keyed Dataset Comparison 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).
Analyzes feature availability records by computing a daily sum time series and a keyed dataset comparison across up to 200 rows, returning two independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Daily sum series + Keyed dataset comparison: Daily sum series + Keyed dataset comparison 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: (1) a daily sum series aggregating the selected field (populated_features or expected_features) by date across supplied rows, and (2) a keyed dataset comparison report matching rows between the primary and secondary datasets by group/key, highlighting differences. All computation is local and based solely on the supplied data.
Example use cases
Daily feature population trend analysis
“I have 120 rows of feature availability logs with populated_features and expected_features for each day across two dataset groups — can you compute the daily sum series for populated_features and also compare my primary dataset against a baseline dataset keyed by group?”
Expected vs actual feature gap reporting
“Run a daily sum series plus keyed dataset comparison on my feature availability data — I want to see how expected_features totals by day and compare them across two separate dataset groups to spot mismatches.”
Multi-group feature coverage audit
“I've got two batches of feature records, each up to 80 rows, both with date, dataset_group, populated_features, and expected_features fields — generate both the daily sum time series and the keyed comparison report for the expected_features field so I can audit coverage gaps.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-144?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both a date-based aggregate time series AND a side-by-side keyed comparison of two feature availability datasets in a single low-cost call. Ideal for data quality pipelines monitoring feature population rates, ML feature store audits, or ETL validation where you want daily rollups and cross-dataset diffing without building custom analytics. Best when data is already in-hand (no live lookups) and row count is under 200.
Side effects
- Fewer than 1 row supplied — validation error
- More than 200 rows supplied — request rejected
- Missing required fields (row_id, dataset_group, date, populated_features, expected_features) — schema validation error
- Invalid date format (not YYYY-MM-DD) — pattern validation error
- Null values in numeric fields may affect aggregation accuracy
- Mismatched row_ids between primary and secondary datasets may reduce comparison coverage
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| other | array |
Tags
x402
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