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Join Coverage Analytics: Record Quality Audit + Exact Deduplication + Base-100 Series
x402
Join Coverage Analytics: Record Quality Audit + Exact Deduplication + Base-100 Series is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-23).
Runs three local analytical reports on join coverage data (matched_rows, source_rows): record quality audit, exact row deduplication, and a base-100 index series — up to 200 rows per call.
Price / call$0.01
Success—
StatusUnknown
How this service works
Join coverage — Record quality audit + Exact row deduplication + Base-100 series: Record quality audit + Exact row deduplication + Base-100 series for supplied join coverage records (matched_rows, source_rows). 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 computed locally on the supplied rows: (1) a record quality audit flagging missing, null, or out-of-range values; (2) an exact row deduplication report identifying identical records; and (3) a base-100 normalized index series for the selected numeric field (matched_rows or source_rows) anchored at the specified base_index row.
Example use cases
ETL pipeline join quality check
“I just ran a join between two datasets and want to audit the quality of the results — can you check these join coverage records for data quality issues, find any exact duplicates, and give me a base-100 index on matched_rows starting from row 0? Here are up to 50 rows grouped by pipeline stage and date.”
Daily join coverage drift monitoring
“Here are my daily join coverage numbers across dataset groups for the past month — run the record quality audit, deduplication, and base-100 series on source_rows so I can see where coverage started drifting from baseline.”
Deduplicating multi-source join results
“My data warehouse aggregates join results from several sources and I think there are duplicate rows sneaking in — can you run the exact deduplication report plus the quality audit on these 120 join coverage records grouped by dataset_group, using matched_rows as the target field?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-09-218?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have join coverage data (matched_rows and source_rows per date/group) and need three analytical outputs in a single call: a data quality audit, exact duplicate detection, and a base-100 normalized time series — all computed without any external lookups or live data access. It is well-suited for offline ETL validation, data pipeline QA, and coverage trend analysis on small-to-medium datasets (up to 200 rows).
Side effects
- Fewer than 1 row supplied — minimum 1 row required
- More than 200 rows supplied — maxItems limit exceeded
- Invalid date format (must match YYYY-MM-DD pattern)
- Missing required fields: row_id, dataset_group, date, matched_rows, or source_rows
- base_index out of range (must be 0–199 and within actual row count)
- field value not in enum ['matched_rows', 'source_rows']
- Numeric values outside allowed range (±1,000,000,000,000)
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| base_index | integer |
Tags
x402
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