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Feature Availability Analytics: Numeric Row Ordering + Two-Measure Correlation
x402
Feature Availability Analytics: Numeric Row Ordering + 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 2026-10-20).
Runs two local analytical reports — numeric row ordering and two-measure correlation — on up to 200 feature availability records (populated_features vs expected_features) per call.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Numeric row ordering + Two-measure correlation: Numeric row ordering + 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 analytical reports: (1) a numeric row ordering of the submitted rows ranked by the selected field (populated_features or expected_features), and (2) a two-measure correlation report quantifying the statistical relationship between populated_features and expected_features across the submitted rows. No external data is fetched; all analysis is performed locally on the supplied rows (up to 200).
Example use cases
Feature coverage ranking across dataset groups
“I have 50 rows of feature availability data across two dataset groups — each row has a date, populated_features, and expected_features. Can you rank them numerically by populated_features and also run the two-measure correlation report so I can see how the groups compare?”
Tracking expected vs actual feature population over time
“I want to understand whether our populated_features values are tracking our expected_features targets over the past quarter. Can you run the correlation analysis on my 120 feature availability records grouped by dataset_group and dated from 2026-07-01 to 2026-09-30, using populated_features as the field?”
Data completeness audit for ML pipeline
“Before we train our next model, I need a quick audit of feature completeness. Can you take these 80 rows — each with a row_id, group name, date, and both populated and expected feature counts — and give me the row ordering plus the correlation between the two measures?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-120?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need quick, offline analytical computation — specifically numeric ordering and cross-measure correlation — on feature availability data without any external lookup, prediction, or human assessment. It is ideal for data quality audits, ML pipeline completeness checks, and dataset group comparisons where you already have both populated and expected feature counts and want statistical insight at minimal cost ($0.01 USDC for two reports).
Side effects
- Missing required fields (row_id, date, populated_features, expected_features) causes validation error
- Date strings not matching YYYY-MM-DD pattern are rejected
- More than 200 rows submitted exceeds the per-call limit
- Null values in both measure fields for a row may produce incomplete correlation results
- row_id exceeding 80 characters is rejected
- Submitting zero rows violates minItems constraint
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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