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Transport Ridership Record Quality Audit + String Normalisation + Numeric Distribution
x402
Transport Ridership Record Quality Audit + String Normalisation + Numeric Distribution is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-26).
Runs three local analytical reports on supplied transport ridership data: a record quality audit, string normalisation, and numeric distribution analysis for passenger_count or service_count fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Transport ridership — Record quality audit + String normalisation + Numeric distribution: Record quality audit + String normalisation + Numeric distribution for supplied transport ridership records (passenger_count, service_count). 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
Three independent analytical reports: (1) a record quality audit flagging missing values, type errors, and constraint violations; (2) a string normalisation report showing standardised region values; and (3) a numeric distribution summary (min, max, mean, percentiles, histogram bins, etc.) for the selected numeric field — all computed locally on the supplied rows, no external lookups.
Example use cases
Pre-load ridership data audit
“Before I load my transit dataset into the warehouse, can you run a record quality audit, normalise the region name strings, and show me the numeric distribution of passenger_count for these 120 ridership rows?”
Service count distribution analysis
“I have 80 rows of bus service data with region, date, and service_count fields — can you give me a numeric distribution of service_count and flag any quality issues in the records?”
Regional string cleanup in ridership data
“My ridership export has inconsistent region labels — can you normalise those strings, audit overall record quality, and profile the passenger_count distribution across these 45 rows?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-economics-05-193?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three simultaneous data-profiling reports (quality audit, string normalisation, and numeric distribution) on transport ridership records in a single cost-efficient call. It is best suited for pre-ingestion validation, data cleaning pipelines, and exploratory profiling of up to 200 ridership rows without requiring external data access or live lookups. Prefer it over general-purpose analytics when your data specifically includes passenger_count and service_count fields alongside region and date dimensions.
Side effects
- Payload exceeds 200 rows — request rejected
- Missing required fields (row_id, region, date, passenger_count, service_count) causes validation error
- Invalid date format (not YYYY-MM-DD) triggers schema rejection
- Numeric values outside ±1,000,000,000,000 bounds rejected
- Empty rows array returns error
- Payment of 0.01 USDC not completed — 402 response blocks execution
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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