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Feature Availability Analytics: Grouped Numeric Summary + Two-Measure Correlation
x402
Feature Availability Analytics: Grouped Numeric Summary + Two-Measure Correlation 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 a grouped numeric summary and a two-measure correlation report for feature availability records (populated_features, expected_features) across up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Grouped numeric summary + Two-measure correlation: Grouped numeric summary + Two-measure correlation 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 supplied rows: (1) a grouped numeric summary breaking down statistics on the chosen field by dataset_group, and (2) a two-measure correlation between populated_features and expected_features across the submitted records. No external lookups or predictions are performed — all analysis is done on the supplied data.
Example use cases
Feature coverage audit by dataset group
“I want to understand how feature completeness varies across my dataset groups. Here are 150 rows of feature availability records, each with a date, a group label, and counts of populated vs expected features. Can you give me a grouped numeric summary and tell me how correlated the populated and expected counts are?”
Data quality regression check after pipeline run
“After yesterday's pipeline run I want to verify data quality. I have 80 rows of feature availability records — each has a row_id, a dataset_group, a date, and values for populated_features and expected_features. Please summarize them by group and compute the correlation between the two measures so I can spot any drift.”
Benchmarking expected vs actual feature population
“I'm benchmarking how consistently our feature store populates data relative to what's expected. Can you take these 200 feature availability records across groups alpha and beta and produce a grouped numeric summary plus a two-measure correlation between populated_features and expected_features?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-095?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 feature availability records with populated_features and expected_features and need both a grouped numeric summary and a two-measure correlation in a single low-cost call ($0.01 USDC). It is ideal for data quality monitoring, feature store auditing, or ML pipeline observability where you want offline, supply-side analysis without any external API calls or live predictions. Prefer alternatives if you need more than 200 rows, more than two analytical reports, real-time lookups, or predictions.
Side effects
- Fewer than 1 row or more than 200 rows submitted — request rejected
- Missing required fields (row_id, dataset_group, date, populated_features, expected_features) on any row — validation error
- Date values not matching YYYY-MM-DD format — pattern validation failure
- Numeric values outside the allowed range (±1,000,000,000,000) — schema rejection
- All values for one measure are null — correlation computation may be undefined or return NaN
- Insufficient variance in data — correlation result may be 0 or undefined
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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