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Join Coverage Analytics: Base-100 Series + Keyed Dataset Comparison
x402
Join Coverage Analytics: Base-100 Series + 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 2026-10-15).
Computes a Base-100 indexed time series and a keyed dataset comparison report for join coverage metrics (matched_rows, source_rows) across up to 200 supplied rows, producing two independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Join coverage — Base-100 series + Keyed dataset comparison: Base-100 series + 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 Base-100 indexed time series normalizing the selected field (matched_rows or source_rows) to a baseline of 100 for trend comparison, and (2) a keyed dataset comparison report aligning the two supplied datasets by row_id and dataset_group to highlight coverage differences. All computation is performed locally on the supplied data with no external lookups.
Example use cases
ETL pipeline join quality audit
“I have before-and-after join coverage data for my ETL pipeline — two arrays of about 40 rows each with row_id, date, dataset_group, matched_rows, and source_rows. Run a Base-100 series and keyed comparison on matched_rows so I can see where my join quality degraded.”
Dataset migration match-rate benchmarking
“We migrated data to a new system and I want to compare join coverage: I have the old system's rows and the new system's rows, each with row_id, date, dataset_group, matched_rows, and source_rows. Give me a Base-100 indexed trend and a side-by-side keyed comparison on source_rows.”
Multi-group join coverage trend analysis
“I have two monthly join coverage snapshots across three dataset groups — group-a, group-b, and group-c — each with about 30 rows including matched_rows and source_rows. Can you compute the Base-100 series and keyed dataset comparison on matched_rows so I can spot which group's coverage is diverging?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-09-168?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have two structured join coverage datasets (with matched_rows and source_rows) and need both a normalized time-series trend (Base-100) and a keyed side-by-side comparison in a single low-cost call. It is ideal for data engineers auditing ETL join quality, comparing pre/post migration datasets, or benchmarking coverage across dataset groups — all without requiring external data access or live lookups.
Side effects
- Fewer than 1 or more than 200 rows supplied — request rejected
- Missing required row fields (row_id, dataset_group, date, matched_rows, source_rows) — validation error
- Date strings not matching YYYY-MM-DD pattern — schema validation failure
- Numeric values exceeding ±1,000,000,000,000 — out-of-range error
- Mismatched row_ids between rows and other arrays may produce sparse comparison output
- Null values in matched_rows or source_rows may affect Base-100 calculation accuracy
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| other | array | |
| base_index | integer |
Tags
x402
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