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Feature Availability — Record Quality Audit + String Normalisation + Keyed Dataset Comparison
x402
Feature Availability — Record Quality Audit + String Normalisation + 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 2026-10-24).
Runs three analytical reports on feature availability records: a record quality audit, string normalisation, and a keyed dataset comparison between two supplied datasets (up to 200 rows each).
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Record quality audit + String normalisation + Keyed dataset comparison: Record quality audit + String normalisation + Keyed dataset comparison 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
Returns three independent analytical reports: (1) a record quality audit flagging data issues in the supplied feature availability rows, (2) a string normalisation report cleaning and standardising string fields, and (3) a keyed dataset comparison report identifying differences between the primary and secondary datasets by row key — all computed locally with no external lookups.
Example use cases
Feature completeness audit for ML pipeline
“I have a dataset of 80 feature availability records with row IDs, group labels, dates, and populated vs expected feature counts — can you run a record quality audit on them so I can see where data is missing or malformed?”
Comparing two feature datasets for drift
“I need to compare two versions of our feature availability dataset by key — each has about 120 rows with row_id, dataset_group, date, and populated and expected feature counts. Can you run a keyed dataset comparison to find any mismatches?”
Normalising feature label strings before joining
“Before I join these feature availability records across teams, can you run string normalisation on the dataset_group and other string fields across my 60-row dataset so inconsistent labels get cleaned up?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-207?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled analytical operations on feature availability records — quality auditing, string normalisation, and keyed dataset comparison — in a single $0.01 call for up to 200 rows. Prefer it over single-purpose analytics endpoints when your workflow requires all three reports and you want to minimise API calls and cost. Best suited for batch offline analysis, not real-time or predictive use cases.
Side effects
- Input array exceeds 200 rows — request rejected
- Missing required fields (row_id, date, populated_features, expected_features) — validation error
- Date field does not match YYYY-MM-DD pattern — schema validation failure
- Numeric values out of allowed range (±1 trillion) — rejection
- Empty rows array (minItems: 1 not met) — validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| other | array |
Tags
x402
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