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Feature Availability Analytics — Duplicate Key Report + Daily Sum Series
x402
Feature Availability Analytics — Duplicate Key Report + Daily Sum Series is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-20).
Generates two offline analytical reports — a duplicate key report and a daily sum series — for supplied feature availability records (populated_features, expected_features) across up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Duplicate key report + Daily sum series: Duplicate key report + Daily sum series for supplied feature availability records (populated_features, expected_features). 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
Returns two independent analytical reports: (1) a duplicate key report identifying rows with repeated row_id values, and (2) a daily sum series aggregating the selected field (populated_features or expected_features) by date — both derived locally from the supplied rows without any external lookups.
Example use cases
Audit feature pipeline for duplicate row IDs
“I've got a feature availability export with row IDs and daily records across two dataset groups — can you check it for duplicate keys and also give me the daily sum rollup for populated_features? Here are up to 50 rows with dates, row_ids, dataset_groups, and populated_features counts.”
Daily totals for expected feature coverage
“I need a daily sum series for expected_features across my dataset — I have about 120 rows tagged by date and dataset_group. Can you aggregate them day by day and also flag any duplicate row IDs while you're at it?”
Data quality check before ML pipeline ingestion
“Before I feed this feature availability table into my ML pipeline, can you run a duplicate key check and produce a daily sum for populated_features? I have 80 rows with row_id, date, dataset_group, expected_features, and populated_features.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-060?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need two quick offline analytical reports — duplicate key detection and daily aggregation — on a feature availability dataset of up to 200 rows in a single low-cost call ($0.01 USDC). It is ideal for data quality checks before pipeline ingestion and for summarizing daily feature coverage without requiring an external database or live data source.
Side effects
- Input exceeds 200 rows — request rejected with validation error
- Missing required fields (row_id, date, populated_features, expected_features) cause row-level validation failure
- Invalid date format (not YYYY-MM-DD) triggers schema rejection
- Null values in numeric fields may affect sum accuracy
- Empty rows array returns an error due to minItems:1 constraint
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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