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Catalog Completeness — String Normalisation + Secondary-Measure Weighted Mean
x402
Catalog Completeness — String Normalisation + Secondary-Measure Weighted Mean is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-11-02).
Performs string normalisation and secondary-measure weighted mean analysis on catalog completeness records (populated_fields, required_fields) for up to 200 rows, returning 2 independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Catalog completeness — String normalisation + Secondary-measure weighted mean: String normalisation + Secondary-measure weighted mean for supplied catalog completeness records (populated_fields, required_fields). 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
Two independent analytical reports: (1) a string normalisation report over the selected field values across the submitted catalog completeness rows, and (2) a secondary-measure weighted mean report summarising the relationship between populated_fields and required_fields — both computed locally with no external lookups, covering up to 200 input rows.
Example use cases
Retail catalog fill-rate review
“I have a week's worth of catalog completeness data across two store groups — each row has a date, store group, and counts for populated and required fields. Can you run string normalisation and a weighted mean on populated_fields for all 50 rows and give me the two analysis reports?”
Data quality audit for required fields
“We're auditing catalog data quality and need to check required_fields completeness across our store groups for last month. Run string normalisation and a secondary-measure weighted mean on these 120 catalog completeness records and return both analytical reports.”
E-commerce SKU completeness benchmarking
“I want to benchmark how completely our product SKUs are filled in across different store groups — here are 80 rows with populated_fields and required_fields counts per date and group. Can you compute the string normalisation and weighted mean reports using required_fields as the field?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-retail-10-034?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both string normalisation and a secondary-measure weighted mean computed together on catalog completeness records (populated_fields / required_fields) in a single low-cost call. It is ideal for retail or e-commerce data quality pipelines processing up to 200 rows per request. Prefer it over general statistics APIs when your data is specifically structured as catalog completeness records with store group and date dimensions.
Side effects
- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, store_group, date, populated_fields, required_fields) causes schema rejection
- Invalid date format (non-YYYY-MM-DD) triggers a pattern validation error
- Null values in both populated_fields and required_fields may produce degenerate weighted mean results
- Invalid field enum value (not 'populated_fields' or 'required_fields') causes rejection
- Empty rows array (minItems:1 violated) returns an error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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