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Structured Data Observations — Record Quality Audit + String Normalisation + Numeric Row Ordering
x402
Structured Data Observations — Record Quality Audit + String Normalisation + Numeric Row Ordering is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-23).
Audits record quality, normalises strings, and orders rows numerically for up to 200 rows of structured observations data containing valid_items and total_items fields, producing 3 independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Structured data observations — Record quality audit + String normalisation + Numeric row ordering: Record quality audit + String normalisation + Numeric row ordering for supplied structured data observations records (valid_items, total_items). 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: (1) a record quality audit identifying missing, null, or malformed values across the supplied rows; (2) a string normalisation report for text fields such as site_group; and (3) a numeric row ordering report ranking rows by the selected numeric field (valid_items or total_items). All reports are generated locally from the supplied data with no external lookups.
Example use cases
Data quality check on site observations
“I have about 80 rows of site observation records with site_group, date, valid_items, and total_items columns. Can you audit the record quality, normalise the string fields, and sort the rows by valid_items to help me spot issues?”
Validate and clean multi-group dataset
“We collected observations across several site groups and I'm worried about inconsistent strings and bad records in our dataset. Can you run the three-report check — quality audit, string normalisation, and numeric ordering — on these 120 rows, focusing on total_items?”
Pre-processing check before analytics pipeline
“Before I feed this 200-row observations dataset into our dashboard, can you audit the record quality, normalise the site_group strings, and order everything by valid_items so I know the data is clean?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-web-08-196?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a bundled three-in-one local data quality analysis — record auditing, string normalisation, and numeric ordering — on tabular structured observation data with valid_items and total_items fields, without requiring external lookups, predictions, or live data. It is cost-effective at $0.01 USDC for all three reports on up to 200 rows. Prefer it over single-report alternatives when you need all three analytical angles in one call.
Side effects
- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, date, valid_items, total_items) causes rejection
- Invalid date format (non YYYY-MM-DD) triggers a schema error
- Numeric values outside allowed range (-1e12 to 1e12) are rejected
- Invalid field enum value (not valid_items or total_items) causes error
- Empty rows array returns an error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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