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Feature Availability Analytics — Record Quality Audit + Exact Row Deduplication + Daily Sum Series
x402
Feature Availability Analytics — Record Quality Audit + Exact Row Deduplication + 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 2026-11-03).
Runs three local analytical reports (record quality audit, exact row deduplication, and daily sum series) on up to 200 feature availability records containing populated_features and expected_features fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Record quality audit + Exact row deduplication + Daily sum series: Record quality audit + Exact row deduplication + Daily sum series for supplied feature availability records (populated_features, expected_features). 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 independent analytical reports: (1) a record quality audit flagging null values, format issues, and completeness problems; (2) an exact row deduplication report identifying fully duplicate records; and (3) a daily sum series aggregating the selected field (populated_features or expected_features) by date — all computed locally without external lookups.
Example use cases
Feature coverage completeness audit
“I have a batch of feature availability records with populated_features and expected_features by date and dataset group — can you run a quality audit, deduplicate exact rows, and show me daily sums so I can see where coverage is dropping?”
Deduplication check on ingested feature logs
“We ingested feature availability logs from two pipelines and I suspect there are duplicate rows. Can you run exact row deduplication plus a record quality audit on these 150 rows and tell me what's redundant or bad quality?”
Daily aggregation of expected vs populated features
“I need a daily sum series for the expected_features column across these feature availability records grouped by date — also run a quality audit and deduplicate while you're at it, here are the rows.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-215?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to simultaneously audit data quality, remove exact duplicate records, and compute daily aggregated sums on feature availability data in a single low-cost call. It is ideal for small-to-medium batches (up to 200 rows) where you want three complementary reports without setting up a full analytics pipeline. Prefer alternatives if you need predictive modeling, live data lookups, outlier detection, or more than 200 rows processed at once.
Side effects
- Submitting more than 200 rows returns a validation error
- Missing required fields (row_id, date, populated_features, expected_features) causes row-level rejection
- Malformed date strings not matching YYYY-MM-DD pattern cause parse failures
- Null values in both numeric fields may produce incomplete sum series
- Empty rows array rejected due to minItems:1 constraint
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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