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Join Coverage — String Normalisation + Category Frequencies
x402
Join Coverage — String Normalisation + Category Frequencies 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).
Analyzes join coverage data (matched_rows, source_rows) by running string normalization and category frequency reports on up to 200 supplied rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Join coverage — String normalisation + Category frequencies: String normalisation + Category frequencies 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 for the supplied join coverage rows: (1) a string normalization report that standardizes and cleans string fields like dataset_group and date, and (2) a category frequency report showing how often each category (e.g. dataset_group value) appears across the submitted rows. Both reports are computed locally with no external lookups.
Example use cases
ETL pipeline join quality audit
“I ran a join between two datasets and got these coverage records — can you normalize the group names and show me the frequency breakdown by dataset_group? I have 8 rows across groups 'sales_US', 'sales_uk', and 'Sales_EU' with matched and source row counts for each date in October 2026.”
Data team weekly join coverage report
“Give me a category frequency report on these join coverage results from our weekly pipeline run. I've got 50 rows with dataset_group labels like 'customers', 'orders', and 'inventory', dates in November 2026, and matched_rows and source_rows counts for each. I want to see how the groups are distributed and any string normalization issues.”
Diagnosing low match rates by group
“Something looks off in our join results — can you analyze these 20 join coverage records to see which dataset_group categories appear most often and normalize the group name strings? Rows span 2025-09-01 to 2025-09-20 with source_rows up to 500 and matched_rows sometimes lower than expected.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-09-024?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have tabular join coverage data with matched_rows and source_rows fields and need both string normalization and category frequency distribution in a single call. It is ideal for data engineers auditing ETL join quality, analysts checking dataset group distributions, or pipelines that need lightweight local analytics without external API calls or predictions. Prefer this over general-purpose analytics tools when you specifically need the combination of string normalization and category frequency for up to 200 join coverage records at low cost.
Side effects
- Rows array is empty (minItems: 1 violated) — request rejected
- More than 200 rows submitted (maxItems exceeded) — request rejected
- Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) on any row — validation error
- Date field not matching YYYY-MM-DD pattern — validation error
- row_id or dataset_group exceeding 80 character limit — validation error
- matched_rows or source_rows outside allowed numeric range — validation error
- Payment of 0.01 USDC not provided or rejected — 402 response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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