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Feature Availability Analytics — Record Quality Audit, String Normalisation & Numeric Row Ordering
x402
Feature Availability Analytics — Record Quality Audit, String Normalisation & Numeric Row Ordering is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-24).
Runs three local analytical reports on feature availability records: a record quality audit, string normalisation, and numeric row ordering — up to 200 rows per call.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Record quality audit + String normalisation + Numeric row ordering: Record quality audit + String normalisation + Numeric row ordering for supplied feature availability records (populated_features, expected_features). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.
Output
Three independent analytical reports for the submitted rows: (1) a record quality audit flagging data issues, (2) a string normalisation report standardising text fields, and (3) a numeric row ordering report ranking rows by the selected numeric field (populated_features or expected_features). All computed locally with no external lookups.
Example use cases
ML pipeline feature completeness audit
“I have a batch of 120 feature availability records from our ML pipeline — each row has a row_id, dataset_group, date, populated_features, and expected_features. Can you run a quality audit, normalise the strings, and order the rows by populated_features so I can see which records are complete and well-formed?”
Data team dataset group QA review
“Our data team wants a quality check on this week's feature availability snapshot — about 80 rows across dataset groups 'group-a' and 'group-b'. Run the record quality audit, string normalisation, and numeric ordering reports on these rows using expected_features as the ordering field.”
Feature store ingestion validation
“Before we ingest these 200 feature availability rows into our feature store, can you audit them for quality issues, normalise the string fields, and order the rows numerically by populated_features so we can spot any anomalies before committing?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-196?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled local analytics reports (quality audit, string normalisation, numeric ordering) on feature availability data in a single cheap call ($0.01 USDC). Ideal for ML/data ops teams validating feature datasets before ingestion or downstream use. Prefer this over individual analytics tools when you want all three report types in one round trip without external API calls or live lookups.
Side effects
- Fewer than 1 or more than 200 rows submitted — schema validation error
- Invalid date format (not YYYY-MM-DD) — pattern mismatch error
- Missing required fields (row_id, dataset_group, date, populated_features, expected_features) — validation failure
- Numeric values outside allowed range (-1,000,000,000,000 to 1,000,000,000,000) — schema error
- Invalid field enum value (must be 'populated_features' or 'expected_features') — enum validation error
- Payment not attached or insufficient USDC — x402 payment required error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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