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Page Extraction Coverage — Record Quality Audit + String Normalisation + Date by Category Pivot
x402
Page Extraction Coverage — Record Quality Audit + String Normalisation + Date by Category Pivot is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-29).
Runs three analytical reports (record quality audit, string normalisation, and date-by-category pivot) on page extraction coverage data containing extracted_fields and expected_fields counts.
Price / call$0.01
Success—
StatusUnknown
How this service works
Page extraction coverage — Record quality audit + String normalisation + Date by category pivot: Record quality audit + String normalisation + Date by category pivot for supplied page extraction coverage records (extracted_fields, expected_fields). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.
Output
Returns three independent analytical reports computed locally on the supplied rows: (1) a record quality audit flagging anomalies or completeness issues per row, (2) a string normalisation report standardising categorical string fields, and (3) a date-by-category pivot summarising the chosen numeric field (extracted_fields or expected_fields) grouped by date and site_group category. No external lookups are performed.
Example use cases
Web scraper quality check across site groups
“I've got extraction coverage logs for two site groups over the past week, each row has a date, site_group, extracted_fields, and expected_fields — can you run a record quality audit plus string normalisation and pivot the results by date and category so I can see where coverage is dropping?”
ETL pipeline field completeness audit
“Run a record quality audit and a date-by-category pivot on my ETL extraction data — I have about 120 rows with row IDs, site groups, dates, and counts for extracted_fields versus expected_fields, and I want to focus the analysis on the extracted_fields column.”
Multi-site extraction anomaly detection
“I need to check whether our scrapers are extracting the right number of fields across all site groups this month — here are 80 rows of extraction coverage records with dates and expected vs actual field counts, please give me all three reports including string normalisation.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-web-02-206?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three simultaneous analytical passes — quality audit, string normalisation, and date pivot — on page extraction coverage data in a single cheap call. It is ideal for batch auditing scraper output quality across multiple site groups over time without needing an external analytics platform. Prefer it over single-report endpoints when you need all three report types at once and have up to 200 rows of extracted_fields/expected_fields data.
Side effects
- Fewer than 1 row or more than 200 rows supplied — request rejected
- Missing required row fields (row_id, date, extracted_fields, expected_fields) — validation error
- Date field does not match YYYY-MM-DD pattern — schema validation failure
- Numeric field values outside allowed range (±1,000,000,000,000) — rejected
- Invalid field enum value (must be 'extracted_fields' or 'expected_fields') — error response
- Payment of 0.01 USDC not provided via x402 protocol — 402 Payment Required
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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