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Record Routing: String Normalisation + IQR Outlier Flags
x402
Record Routing: String Normalisation + IQR Outlier Flags is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-24).
Normalises string fields and applies IQR-based outlier detection to accepted_records and rejected_records columns in up to 200 rows of record routing data, returning two analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Record routing — String normalisation + IQR outlier flags: String normalisation + IQR outlier flags for supplied record routing records (accepted_records, rejected_records). 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
Two analytical reports per call: (1) a string normalisation report covering the supplied record routing rows, and (2) an IQR outlier flags report identifying rows whose accepted_records or rejected_records values fall outside the interquartile range. Results are based entirely on the supplied data with no external lookups or predictions.
Example use cases
Spike detection in daily routing batches
“I have a week of daily record routing data — about 50 rows with accepted and rejected record counts per dataset group — can you flag any IQR outliers and normalise the string fields, focusing the outlier report on rejected_records?”
Data quality audit before pipeline hand-off
“Before I hand off this routing dataset to the next stage, can you run IQR outlier flags and string normalisation on the accepted_records and rejected_records columns across my 120 rows so I can catch anything anomalous?”
Comparing routing performance across groups
“I want to compare routing behaviour across my dataset groups — can you normalise the string fields and flag IQR outliers in accepted_records for these 30 rows split across group-a, group-b, and group-c with dates in October 2026?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-19-032?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both string normalisation and IQR-based outlier detection applied together to record routing data (accepted_records and rejected_records) in a single low-cost call. It is ideal for lightweight data quality checks on batches up to 200 rows without needing external data enrichment, live lookups, or predictions. Prefer alternatives if you need outlier detection on non-routing domains, more than 200 rows, or methods beyond IQR (e.g. z-score, ML-based anomaly detection).
Side effects
- Payload exceeds 200 rows — request rejected with validation error
- Missing required fields (row_id, date, accepted_records, rejected_records) — schema validation failure
- Date field does not match YYYY-MM-DD pattern — rejected
- accepted_records or rejected_records values outside ±1,000,000,000,000 bounds — validation error
- Fewer than 1 row supplied — minimum items constraint violated
- Invalid field enum value (not accepted_records or rejected_records) — rejected
- Payment of 0.01 USDC not completed — x402 payment required response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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