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Join Coverage Analytics: Category Frequencies + Adjacent Row Changes
x402
Join Coverage Analytics: Category Frequencies + 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 2026-10-15).
Analyzes join coverage records (matched_rows, source_rows) to produce two local reports: category frequency distribution by dataset_group and adjacent row-to-row change detection for a selected numeric field.
Price / call$0.01
Success—
StatusUnknown
How this service works
Join coverage — Category frequencies + Adjacent row changes: Category frequencies + 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 derived entirely from the supplied rows: (1) a category frequency table showing how often each dataset_group value appears, and (2) an adjacent row change report showing delta values between consecutive rows for the selected numeric field (matched_rows or source_rows). No external lookups or predictions are performed; all analysis is local.
Example use cases
Audit join match rates by group
“Here are my join coverage logs for October — each row has a dataset_group, a date, matched_rows, and source_rows. Can you give me a frequency breakdown by dataset_group and show me where matched_rows changed between consecutive rows?”
Spot coverage drops in ETL pipeline
“I'm monitoring my ETL pipeline's join quality. I have 150 records with source_rows and matched_rows tagged by dataset_group. Run a category frequency report and highlight adjacent row changes in source_rows so I can see where coverage drops.”
Profile join dataset before QA review
“Before my data QA review, I need two quick reports on these join coverage records: how often each dataset_group appears, and what the row-to-row changes look like in matched_rows. I have about 80 rows with dates and group labels.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-09-104?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have join coverage tabular data (with matched_rows and source_rows fields) and need two specific analytical views — category frequency by dataset_group and adjacent row change detection — computed locally without any external API calls, predictions, or live data lookups. It is cost-effective at $0.01 for both reports simultaneously on up to 200 rows. Prefer this over general analytics tools when your data is already structured per the schema and you want fast, deterministic local computation.
Side effects
- Fewer than 1 row submitted returns a validation error
- More than 200 rows submitted exceeds the limit and is rejected
- Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) causes schema validation failure
- Invalid date format (not YYYY-MM-DD) triggers a pattern mismatch error
- Numeric values outside ±1,000,000,000,000 are rejected
- Invalid field enum value (not matched_rows or source_rows) causes an error
- Payment of 0.01 USDC not included or rejected results in a 402 response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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