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Promotion Observations — Keyed Dataset Comparison + Explicit Missing-Value Fill
x402
Promotion Observations — Keyed Dataset Comparison + Explicit Missing-Value Fill is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-25).
Compares two supplied datasets of promotion observations (discount_amount, units_sold) by row key and fills missing values, producing 2 analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Promotion observations — Keyed dataset comparison + Explicit missing-value fill: Keyed dataset comparison + Explicit missing-value fill for supplied promotion observations records (discount_amount, units_sold). 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 analytical reports: one keyed comparison showing value-level differences between the two supplied datasets on the selected field (discount_amount or units_sold), and one explicit missing-value fill report resolving nulls in the same field — covering up to 200 rows, computed locally with no external lookups.
Example use cases
Reconciling promotion discount data across periods
“I have two exports of our Q4 promotion records — each up to 200 rows with row_id, store_group, date, discount_amount, and units_sold — and I need you to compare them key-by-key on discount_amount and flag or fill any missing values so I can reconcile the differences.”
Filling missing units sold in promotional data
“Can you take my two promotion observation datasets and fill in any null units_sold values explicitly, then give me a comparison report showing what changed between the two sets? Both datasets have up to 150 rows keyed by row_id.”
Store group promotion performance comparison
“I want to compare promotion performance between group-a and group-b stores — I'll supply two datasets of discount_amount and units_sold rows, up to 200 rows each, and I need a keyed comparison report with missing values filled in for the units_sold field.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-retail-05-190?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have two batches of promotion observation data (discount amounts and units sold) that you need to reconcile key-by-key and resolve missing values in, without any need for live external data, predictions, or professional assessments. It is ideal for retail analysts needing a structured diff between two promotion report snapshots with null imputation, up to 200 rows per batch at very low cost.
Side effects
- Missing required fields (row_id, store_group, date) causes validation error
- Exceeding 200 rows returns a payload-too-large error
- Invalid date format (not YYYY-MM-DD) causes schema rejection
- Numeric values outside ±1,000,000,000,000 range are rejected
- Empty rows array returns a minItems validation error
- Mismatched row keys between the two datasets may result in sparse comparison output
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| other | array | |
| method | string |
Tags
x402
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