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Join Coverage Record Quality Audit with IQR Outlier Flags
x402
Join Coverage Record Quality Audit with IQR Outlier Flags 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).
Performs a record quality audit and IQR-based outlier detection on supplied join coverage data (matched_rows, source_rows) for up to 200 rows, returning 2 independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Join coverage — Record quality audit + IQR outlier flags: Record quality audit + IQR outlier flags for supplied join coverage records (matched_rows, source_rows). 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 2 independent analytical reports: (1) a record quality audit summarizing completeness, validity, and structural issues across the supplied rows, and (2) IQR-based outlier flags identifying which rows have anomalous values in the selected field (matched_rows or source_rows), all computed locally without external lookups.
Example use cases
ETL pipeline join coverage audit
“I ran an ETL job and want to audit the quality of my join coverage results — here are 50 rows with row_id, date, dataset_group, matched_rows, and source_rows. Can you flag any IQR outliers in the matched_rows field and give me a full record quality report?”
Detecting anomalous source row counts
“I have daily join coverage data for three dataset groups spanning 30 days. Can you run the IQR outlier detection on the source_rows field and tell me which records look anomalous compared to the rest?”
Data quality check before pipeline merge
“Before I merge my datasets I want to validate the join coverage stats — I've got 100 rows with matched_rows and source_rows per group. Can you do a record quality audit and highlight any outlier rows using IQR so I know what to investigate?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-09-014?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a combined record quality audit AND IQR outlier detection on join coverage metrics in a single API call. It is ideal for data engineers and analysts who want to validate ETL join outputs, detect anomalous match rates, or audit data pipeline quality without external dependencies. Prefer it over generic stats APIs when your data specifically involves matched_rows and source_rows join coverage fields and you need two structured reports for up to 200 rows at low cost.
Side effects
- Fewer than 1 row supplied — minimum input not met
- More than 200 rows submitted — exceeds max item limit
- Missing required fields (row_id, date, matched_rows, source_rows, dataset_group) in any row
- Date field not in YYYY-MM-DD format — pattern validation failure
- Numeric values out of allowed range (±1 trillion)
- Invalid field enum value — must be 'matched_rows' or 'source_rows'
- Payment not completed — x402 protocol payment required before response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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