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Returns Handling Analytics — Record Quality Audit, String Normalisation & Keyed Dataset Comparison
x402
Returns Handling Analytics — 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 2026-10-22).
Runs three offline analytical reports on retail returns handling data: a record quality audit, string normalisation, and keyed dataset comparison for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Returns handling — Record quality audit + String normalisation + Keyed dataset comparison: Record quality audit + String normalisation + Keyed dataset comparison for supplied returns handling records (returned_units, processing_minutes). 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 structured analytical reports: (1) a record quality audit highlighting missing, null, or out-of-range values in returned_units and processing_minutes; (2) a string normalisation report cleaning and standardising text fields like store_group; (3) a keyed dataset comparison showing row-level differences between the primary and reference datasets matched by row_id. All analysis is performed locally with no external lookups.
Example use cases
Spotting dirty data before reporting
“I need to audit my weekly returns records before sending them to management — can you run a quality check on these rows covering store groups A and B for October 2026, flagging any missing or invalid returned_units or processing_minutes values? Here are up to 150 rows.”
Comparing two extracts from different systems
“We pulled returns data from two separate warehouse systems for the same date range and I need to know where they diverge row by row — can you do a keyed dataset comparison between these two sets of up to 200 rows each, matched on row_id?”
Normalising inconsistent store group labels
“Our returns records have inconsistent capitalisation and spacing in the store_group field across about 80 rows — can you run a string normalisation report on this dataset so we get clean, consistent labels?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-retail-04-207?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a combined, cost-effective batch of three offline data quality reports — quality audit, string normalisation, and dataset comparison — on retail returns data (returned_units, processing_minutes) for up to 200 rows, all within a single $0.01 USDC call. It is ideal when no live lookup, prediction, or external action is required and you simply want to validate, clean, and reconcile two versions of a returns handling dataset.
Side effects
- More than 200 rows submitted — request will be rejected or truncated
- Missing required fields (row_id, store_group, date, returned_units, processing_minutes) cause validation errors
- Malformed date strings not matching YYYY-MM-DD pattern rejected
- Numeric values outside allowed range (-1e12 to 1e12) may cause errors
- Empty rows array (minItems: 1 violated) returns an error
- Mismatched row_id keys between primary and comparison datasets may result in incomplete comparison report
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| other | array |
Tags
x402
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