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Delx Numeric Outlier Scan
x402
Delx Numeric Outlier Scan is a paid API for AI agents from api.delx.ai, paid per call via x402, $0.003/call, status unknown (last checked 2026-09-13).
Identifies statistical outlier indexes in a bounded numeric array using z-score thresholding, returning machine-readable JSON results.
Price / call$0.003
Success—
StatusUnknown
How this service works
Identify statistical outlier indexes in bounded numeric samples. Use it as a bounded preflight or analysis step inside an enterprise agent workflow before data, policy, integration, security, or commercial decisions reach production. Returns deterministic machine-readable JSON for $0.003 USDC via x402 on Base. Execution is first-party, local-only, stateless, memory-only, and has no paid upstream or input retention. Results are advisory; the caller remains responsible for authorization and produ…
Output
Returns deterministic machine-readable JSON indicating which indexes in the input array are statistical outliers based on the specified z-score threshold, along with supporting z-score values for each element.
Example use cases
Preflight sensor data before alerting
“Before firing any alerts, scan these temperature sensor readings for outliers with a z-threshold of 3.0: [72, 71, 73, 70, 74, 210, 72, 71] — flag any suspicious indexes so I can ignore bad readings.”
Clean financial data before modeling
“I have this list of transaction amounts and need to catch any outliers before feeding them into my risk model — run an outlier scan with z-threshold 2.0 on [450, 470, 460, 455, 3200, 462, 448].”
Validate user input scores before aggregation
“Check these user-submitted quality scores for statistical outliers before I average them — use a z-threshold of 2.5 on [8, 7, 9, 8, 7, 10, 8, 1, 9, 8] and tell me which indexes to exclude.”
Technical details
Endpoint
POSThttps://api.delx.ai/api/v1/x402/numeric-outlier-scanWhen to prefer
Choose this endpoint when you need a fast, stateless, deterministic outlier detection step on a bounded numeric array as part of an automated agent workflow — especially as a preflight check before downstream data, policy, or commercial decisions. It is ideal when you want a simple z-score-based approach without spinning up a data science stack, and when reproducibility and machine-readable JSON output matter. Prefer it over general-purpose analytics APIs when cost predictability ($0.003 per call) and stateless execution with no data retention are requirements.
Side effects
- Empty or null values array returns an error or empty results
- z_threshold not provided may default to a standard value or return a validation error
- Non-numeric values in the array cause a type error
- Array with fewer than 3 elements may produce unreliable z-score statistics
- Payment failure via x402 returns 402 status and no result
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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