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Delx Commerce — Numeric Outlier Scan
x402
Delx Commerce — Numeric Outlier Scan is a paid API for AI agents from commerce.delx.ai, paid per call via x402, $0.003/call, status unknown (last checked 2026-10-01).
Scans an array of numeric values and identifies statistical outliers using z-score thresholding, returning mean, standard deviation, and outlier indexes.
Price / call$0.003
Success—
StatusUnknown
How this service works
Pay-per-result APIs for agents. No signup. Exact price. Verifiable delivery. USDC on Base + Solana via x402.
Output
Returns a JSON object containing the computed mean, standard deviation, total count of values, the configured z-score threshold, the number of outliers detected, and an array of indexes corresponding to the outlier values in the original input array.
Example use cases
Sensor spike detection in IoT data
“I have a batch of temperature sensor readings: [22.1, 21.8, 22.5, 99.3, 21.9, 22.0] — can you scan them for outliers using a z-score threshold of 2.5 and tell me which readings look like spikes?”
Financial price anomaly flagging
“Here are daily closing prices for a stock this month: [102, 105, 103, 101, 98, 245, 104, 100] — run an outlier scan with z-threshold 2 and tell me which days had abnormal prices.”
Quality control on manufacturing measurements
“I've got a list of product weight measurements from today's production run: [500, 499, 501, 498, 502, 350, 500, 499] — can you identify any outliers at a z-score threshold of 3?”
Technical details
Endpoint
POSThttps://commerce.delx.ai/api/v1/x402/numeric-outlier-scan?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a fast, stateless, pay-per-call numeric outlier scan without setting up infrastructure or a data pipeline. Ideal for agents processing ad-hoc numeric arrays where you want exact z-score-based anomaly detection with no recurring subscription cost. Prefer over general-purpose data platforms when you only need this single computation on demand.
Side effects
- Empty or missing 'values' array causes validation error
- Non-numeric values in array may cause computation failure
- Very small arrays (fewer than 2 elements) may produce unreliable standard deviation
- Extremely high z_threshold values may result in zero outliers being detected even for visibly extreme data
- Missing z_threshold defaults to a preset value — results may differ from expectation if not explicitly set
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| values | array | Input field: values. |
| z_threshold | number | Input field: z_threshold. |
Output
Schema · Response
{
"type": "json",
"example": {
"mean": 48.6,
"count": 5,
"schema": "delx/util-numeric-outlier-scan/v1",
"z_threshold": 2,
"outlier_count": 0,
"outlier_indexes": [],
"standard_deviation": 75.703633
}
}Tags
x402
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