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Join Coverage Analytics — Exact Row Deduplication + Adjacent Row Changes
x402
Join Coverage Analytics — Exact Row Deduplication + Adjacent Row Changes 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 two local analytical reports on join coverage data: exact row deduplication and adjacent row change detection, for up to 200 supplied rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Join coverage — Exact row deduplication + Adjacent row changes: Exact row deduplication + Adjacent row changes 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 analytical reports: (1) an exact row deduplication report identifying repeated records among the supplied join coverage rows, and (2) an adjacent row changes report flagging where the selected field (matched_rows or source_rows) changes between consecutive rows. Both reports are computed locally against the supplied data with no external lookups.
Example use cases
Deduplicating ETL join coverage logs
“I have a batch of join coverage records from our ETL pipeline — each row has a row_id, date like 2025-06-01, a dataset_group, and both matched_rows and source_rows counts. Can you run exact row deduplication on the matched_rows field and tell me which records are duplicates?”
Spotting day-over-day changes in source rows
“I want to detect where source_rows values change between adjacent rows in my join coverage dataset — I have about 80 rows with dates running from 2025-01-01 through 2025-03-31, grouped by dataset_group. Can you run the adjacent row changes report on the source_rows field?”
Auditing join coverage data before pipeline merge
“Before we merge this dataset, I need to check for exact duplicate join coverage rows and also flag any adjacent row changes in matched_rows — here are 120 rows with row_id, date, dataset_group, matched_rows, and source_rows. Can you run both reports?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-09-046?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need lightweight, self-contained deduplication and sequential change analysis on join coverage records without any external data lookups. It is ideal for data quality audits during ETL pipelines, pre-merge validation, or routine join coverage monitoring where you already have the raw rows and need two complementary analytical views at low cost ($0.01 USDC for both reports).
Side effects
- Exceeding 200 rows causes rejection — batch must be split
- Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) cause validation errors
- Invalid date format (non-YYYY-MM-DD) causes schema rejection
- Null values in the selected field may affect change detection output
- Submitting fewer than 1 row returns an error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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