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Join Coverage Record Quality Audit and Keyed Dataset Comparison
x402
Join Coverage Record Quality Audit and Keyed Dataset Comparison 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 two local analytical reports on join coverage data: a record quality audit and a keyed dataset comparison between two supplied datasets (rows vs other), for up to 200 rows each.
Price / call$0.01
Success—
StatusUnknown
How this service works
Join coverage — Record quality audit + Keyed dataset comparison: Record quality audit + Keyed dataset comparison 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 two independent analytical reports: (1) a record quality audit identifying data issues, nulls, or anomalous values in the join coverage records, and (2) a keyed dataset comparison report showing differences between the primary (rows) and secondary (other) datasets on matched_rows and source_rows fields — all computed locally without external lookups.
Example use cases
ETL pipeline join coverage audit
“I just ran our nightly ETL and have join coverage logs for the last 7 days across two dataset groups. Can you audit the record quality and flag any rows where matched_rows or source_rows look problematic? Here are the records with row_id, date, dataset_group, matched_rows, and source_rows.”
Comparing two versions of join output
“We have yesterday's join coverage snapshot and today's — both with the same row IDs and groups. Can you compare them side by side and tell me where the matched_rows or source_rows differ between the two versions?”
Match rate anomaly detection across groups
“I want to check whether our group-a and group-b datasets have any suspicious records where the matched_rows count is way off from source_rows. Can you run a quality audit on both my primary and secondary join coverage datasets — I'll provide up to 50 rows in each.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-09-018?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need offline, deterministic analytical reports on join coverage data — specifically both a record quality audit AND a keyed cross-dataset comparison in a single call. Ideal for data engineers validating ETL outputs, comparing pipeline snapshots, or auditing match rates without needing real-time lookups or external enrichment. Prefer this over general-purpose analytics when your data is already structured as join coverage records with matched_rows and source_rows fields.
Side effects
- Input arrays exceed 200 rows — request rejected
- Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) on any row causes validation error
- Date fields not matching YYYY-MM-DD pattern cause schema rejection
- Empty rows array (minItems: 1 not met) causes failure
- Numeric values outside ±1 trillion range rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| other | array |
Tags
x402
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