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API Usage Record Quality Audit
x402
API Usage Record Quality Audit is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-15).
Audits up to 200 API usage records for numeric and data quality issues, returning a bounded quality assessment report.
Price / call$0.01
Success—
StatusUnknown
How this service works
API usage - Record quality audit: Record quality audit for API usage. 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 record quality audit deliverable.
Output
A bounded quality audit deliverable containing data quality flags and assessments for each submitted API usage record, identifying numeric anomalies, missing values, and integrity issues across the supplied rows — processed locally without external enrichment.
Example use cases
Pre-pipeline data validation for API logs
“Before I load this week's API usage data into our warehouse, can you audit these 50 records — each has a row_id, endpoint name, date, request count, and latency in milliseconds — and flag any quality issues or missing values?”
Spot-checking endpoint latency data integrity
“I have 120 rows of API latency records for our payment-service endpoint across October. Can you run a quality audit on them to check for nulls, out-of-range values, or other data problems?”
Automated quality gate for monitoring ingestion
“As part of our monitoring pipeline, audit these 200 API usage records for group-a and group-b endpoints dated 2026-09-01 through 2026-09-30, and tell me if any rows fail quality checks before we ingest them.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-analytics-api-usage-quality?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a fast, local data-quality audit of API usage records (request counts and latency) before ingestion, reporting, or analysis — especially when you want no external data enrichment or forecasting, and your batch is 200 records or fewer. Prefer it over general-purpose data validation services when your records conform to the API usage schema (endpoint, date, requests, latency_ms).
Side effects
- Submitting more than 200 rows returns a validation error
- Missing required fields (row_id, endpoint, date, requests, latency_ms) causes row-level rejection
- Dates not matching YYYY-MM-DD format fail pattern validation
- Empty rows array (minItems: 1 not met) returns an error
- Extremely large or small numeric values near the ±1e12 boundary may trigger range flags
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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