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Inventory Counts Date-by-Category Pivot
x402
Inventory Counts Date-by-Category Pivot is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).
Pivots inventory count records by date and warehouse category, summarizing book_units or counted_units across up to 200 rows into a cross-tabulated matrix.
Price / call$0.01
Success—
StatusUnknown
How this service works
Inventory counts - Date by category pivot: Date by category pivot for Inventory counts. Up to 200 supplied typed records. Local numeric and data-quality processing; no external enrichment or forecasting. One of 25 profiles or calculations across 20 shared operations in this sector. 0.01 USDC includes one bounded date by category pivot deliverable.
Output
A date-by-category pivot table (cross-tabulation matrix) where rows represent dates and columns represent warehouse categories, with cells containing aggregated values of the selected field (book_units or counted_units). Derived locally from the supplied records with no external data enrichment.
Example use cases
Warehouse discrepancy overview by date
“I have about 150 inventory count rows from our East and West warehouses over the past month. Can you pivot the counted_units by date and warehouse so I can see at a glance which dates have coverage gaps?”
Book units matrix for cycle count review
“Here are our cycle count records from three warehouse groups for Q3. Pivot the book_units by date and warehouse category so our auditors can review the expected quantities across each location and date.”
Multi-location inventory trend summary
“We track inventory at four distribution centers daily. Can you take these 180 rows of count data and build a date-by-warehouse pivot on book_units so we can spot which centers are consistently short?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-analytics-inventory-counts-pivot?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have structured inventory count records (with dates, warehouse labels, and unit counts) and need a cross-tabulated pivot view for auditing, reporting, or comparison — especially when you want a lightweight, deterministic local computation without calling a full BI platform or data warehouse. Ideal for up to 200 records where no external enrichment or forecasting is needed.
Side effects
- Fewer than 1 row supplied — validation error
- More than 200 rows submitted — request rejected at schema level
- Invalid date format (not YYYY-MM-DD) — row-level or request-level error
- Missing required fields (row_id, warehouse, date) — schema validation failure
- All numeric values null — pivot may return empty or zero-filled cells
- Duplicate row_ids — may cause unexpected aggregation behavior
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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