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Join Coverage Analytics — Exact Row Deduplication + IQR Outlier Flags
x402
Join Coverage Analytics — Exact Row Deduplication + 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).
Deduplicates exact rows and flags IQR-based outliers in join coverage data (matched_rows, source_rows) for up to 200 records, producing 2 independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Join coverage — Exact row deduplication + IQR outlier flags: Exact row deduplication + 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
Two independent analytical reports: (1) an exact row deduplication report identifying and removing duplicate records from the supplied join coverage rows, and (2) an IQR outlier flag report marking records whose selected field (matched_rows or source_rows) falls outside the interquartile range, indicating statistical anomalies. No external data is fetched; all analysis is performed on the supplied rows only.
Example use cases
ETL pipeline data quality check
“I just ran a join between two datasets and got back these coverage records — can you deduplicate them and flag any outliers in the matched_rows counts using IQR? Here are 50 rows with row_id, dataset_group, date, source_rows, and matched_rows.”
Anomaly detection in source row counts
“Check my join coverage export for outliers — I want IQR-based flags on the source_rows field and exact duplicates removed. I have about 120 rows covering multiple dataset groups from January through March 2025.”
Dataset join audit before reporting
“Before I finalize my join coverage report, can you deduplicate my records and highlight any matched_rows values that look statistically abnormal? I have 80 rows grouped by dataset_group with dates in Q4 2024.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-09-049?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need fast, local statistical analysis on join coverage datasets without any external lookups or predictions — specifically when you want both exact duplicate removal and IQR-based outlier flagging in a single call for up to 200 rows. It is ideal for data quality pipelines validating ETL join results, pre-reporting audits, or any workflow where matched_rows and source_rows counts need sanity checking. Prefer it over general-purpose analytics tools when the data is already structured in the expected join coverage schema and cost efficiency matters ($0.01 USDC for 2 reports).
Side effects
- Fewer than 1 row submitted — violates minItems constraint
- More than 200 rows submitted — exceeds maxItems limit
- Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) — validation error
- Invalid date format (not YYYY-MM-DD pattern) — schema rejection
- Null values for both matched_rows and source_rows may reduce outlier detection quality
- Invalid field selector value (not 'matched_rows' or 'source_rows') — enum validation failure
- Payment not completed — x402 payment required before response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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