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Join Coverage Analytics: Record Quality Audit + Exact Row Deduplication + Grouped Numeric Summary
x402
Join Coverage Analytics: Record Quality Audit + Exact Row Deduplication + Grouped Numeric Summary 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).
Runs three local analytical reports on join coverage data (matched_rows, source_rows): a record quality audit, exact row deduplication, and grouped numeric summary — all in a single call for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Join coverage — Record quality audit + Exact row deduplication + Grouped numeric summary: Record quality audit + Exact row deduplication + Grouped numeric summary 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 reports in a single response: (1) a record quality audit flagging issues like missing or out-of-range values in the supplied rows, (2) an exact row deduplication report identifying and removing duplicate records, and (3) a grouped numeric summary (e.g. count, sum, mean) of the selected numeric field (matched_rows or source_rows) broken down by dataset_group. All analysis is performed locally on the supplied data with no external lookups.
Example use cases
ETL pipeline join audit
“Here are 150 join coverage records from our data pipeline — each has a row_id, dataset_group, date, matched_rows, and source_rows. Can you run a record quality audit, deduplicate any exact duplicate rows, and give me a grouped numeric summary of matched_rows by dataset_group?”
Duplicate detection in join output
“I exported 80 rows of join coverage data with columns row_id, dataset_group, date, matched_rows, and source_rows. Can you find all exact duplicate rows, flag any record quality issues, and summarize source_rows by group?”
Data quality check before reporting
“Before I send our weekly join coverage report, can you audit these 60 records for quality issues, remove duplicates, and give me a grouped numeric breakdown of matched_rows across the dataset groups? Each row has row_id, dataset_group, date, matched_rows, and source_rows.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-09-211?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need all three analytical operations — record quality audit, exact deduplication, and grouped numeric summary — applied to join coverage data in a single low-cost call. It is ideal for ETL validation workflows where you want to check data completeness, remove duplicates, and summarize join metrics by group before downstream reporting. Prefer it over separate single-purpose tools when your dataset fits within 200 rows and you want bundled analytics at a fixed cost of $0.01 USDC.
Side effects
- Fewer than 1 row supplied — minimum of 1 row required
- More than 200 rows submitted — exceeds maximum row limit
- Invalid date format (not YYYY-MM-DD) causes row rejection
- Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) causes validation error
- Numeric values outside allowed range (-1,000,000,000,000 to 1,000,000,000,000) rejected
- Invalid field enum value (must be 'matched_rows' or 'source_rows')
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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