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Join Coverage — Trailing Mean Series + Keyed Dataset Comparison
x402
Join Coverage — Trailing Mean 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 2026-10-19).
Computes a trailing mean time series and performs a keyed dataset comparison on two sets of join coverage records (matched_rows, source_rows) for up to 200 rows each.
Price / call$0.01
Success—
StatusUnknown
How this service works
Join coverage — Trailing mean series + Keyed dataset comparison: Trailing mean 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 trailing mean time series of the selected field (matched_rows or source_rows) across the provided records, and (2) a keyed dataset comparison between the two supplied datasets, highlighting differences in join coverage metrics by key. Both reports are computed locally from the supplied data with no external lookups.
Example use cases
ETL pipeline join coverage drift detection
“I've got daily join coverage logs from my ETL pipeline across two time windows — baseline from last month and the current month — each row has a date, dataset_group, source_rows, and matched_rows. Run a trailing mean on matched_rows and compare the two datasets by key so I can see where coverage has dropped.”
Data migration match rate audit
“We just ran a data migration and I have before-and-after join coverage snapshots with matched_rows and source_rows per group. Compute a trailing mean series on source_rows and a keyed comparison between the old and new datasets so we can audit match quality across groups.”
Weekly join coverage trend review
“Here are two weeks of join coverage records — each with row_id, date, dataset_group, matched_rows, and source_rows. Give me the trailing mean of matched_rows over time and a side-by-side keyed comparison between week one and week two so I can see which groups improved or regressed.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-09-153?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both a trailing moving average time series AND a keyed side-by-side comparison of two join coverage datasets in a single paid call. Ideal for data engineers auditing ETL pipeline join completeness, detecting coverage drift between a baseline and updated dataset, or reviewing rolling match rate trends. Best suited for batch analytical work on pre-collected data (up to 200 rows) where no live lookup or external data enrichment is needed.
Side effects
- Fewer than 1 row supplied — request rejected
- More than 200 rows supplied — exceeds limit, request rejected
- Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) — validation error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Numeric values outside allowed range — rejected
- Invalid field enum value — must be matched_rows or source_rows
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| other | array | |
| window | integer |
Tags
x402
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