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Join Coverage Analytics — Record Quality Audit, String Normalisation & Grouped Numeric Summary
x402
Join Coverage Analytics — Record Quality Audit, String Normalisation & 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 2026-10-11).
Runs three offline analytical reports on join coverage data (matched_rows, source_rows): a record quality audit, string normalisation, and grouped numeric summary — up to 200 rows per call.
Price / call$0.01
Success—
StatusUnknown
How this service works
Join coverage — Record quality audit + String normalisation + Grouped numeric summary: Record quality audit + String normalisation + 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 analytical reports for the supplied join coverage rows: (1) a record quality audit flagging missing values, anomalies, and completeness issues; (2) a string normalisation report cleaning dataset_group and other string fields; (3) a grouped numeric summary (e.g. counts, means, totals) of the selected numeric field (matched_rows or source_rows) broken down by dataset_group. All computed locally with no external lookups.
Example use cases
Data pipeline join coverage audit
“I've just run a join between two datasets and want to audit the quality — can you check these 10 join coverage records for data issues, normalise the group name strings, and give me a grouped numeric summary of matched_rows across my dataset groups?”
Detecting match rate anomalies by group
“I have join coverage records from last week with matched_rows and source_rows for groups 'transactions' and 'users' — can you run a quality audit and grouped numeric summary so I can spot which groups have the worst match rates?”
Pre-ingestion data validation
“Before I ingest these 50 join coverage rows into our warehouse, can you run a record quality audit and string normalisation on the dataset_group labels, and summarise the source_rows numeric stats by group?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-09-194?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three combined analytical operations on join coverage data in a single call — quality auditing, string normalisation, and grouped numeric summarisation — without making external API calls or requiring live data. Ideal for data engineering pipelines that need lightweight, offline validation and summarisation of match/source row metrics grouped by dataset category.
Side effects
- Submitting more than 200 rows causes rejection or truncation
- Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) causes validation errors
- Invalid date format (non YYYY-MM-DD) causes schema rejection
- Numeric values outside allowed range cause validation failure
- Empty rows array returns an error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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