Outlier detection (IQR + z-score)

Flag outliers in a numeric series using either the IQR rule (Tukey fences at 1.5·IQR - robust, default) or z-score (|z| > threshold - assumes normality). Returns the outlier values + their indices + the thresholds used so you can decide whether to trust them.

Input

Field	Type	Description
values *	array	Numeric series (max 10000, at least 4 values)
method	string	"iqr" (default) or "zscore"
threshold	number	IQR multiplier (default 1.5) or z-score cutoff (default 3)

Example output

{
  "method": "iqr",
  "n": 10,
  "threshold": 1.5,
  "lowerBound": -4.625,
  "upperBound": 14.375,
  "outliers": [
    {
      "index": 9,
      "value": 100
    }
  ],
  "outlierCount": 1
}

Try it - see the 402 challenge (free)

curl -i -X POST https://agent402.tools/api/outliers \
  -H "Content-Type: application/json" \
  -d '{"values":[1,2,3,4,5,6,7,8,9,100],"method":"iqr"}'

The response is HTTP 402 Payment Required with exact payment requirements. Any x402 v2 client pays automatically and retries:

Paid call (JavaScript agent)

import { wrapFetchWithPayment } from "@x402/fetch";
import { x402Client } from "@x402/core/client";
import { registerExactEvmScheme } from "@x402/evm/exact/client";
import { privateKeyToAccount } from "viem/accounts";

const client = new x402Client();
registerExactEvmScheme(client, { signer: privateKeyToAccount(KEY) });
const payFetch = wrapFetchWithPayment(fetch, client);

const res = await payFetch("https://agent402.tools/api/outliers", {
  method: "POST",
  headers: { "Content-Type": "application/json" },
  body: JSON.stringify({
    "values": [
      1,
      2,
      3,
      4,
      5,
      6,
      7,
      8,
      9,
      100
    ],
    "method": "iqr"
  }),
});

No wallet? Pay with compute

This is a pure-CPU tool, so an agent without a wallet can pay with proof-of-work instead of USDC: fetch a challenge, solve the sha256 puzzle (16 leading zero bits - a fraction of a second of CPU, no money, no AI tokens), and resend with the X-Pow-Solution header.

import { createHash } from "node:crypto";
const lz = (b) => { let t = 0; for (const x of b) { if (!x) { t += 8; continue; } t += Math.clz32(x) - 24; break; } return t; };
const c = await (await fetch("https://agent402.tools/api/pow/challenge?slug=outliers")).json();
let n = 0;
while (lz(createHash("sha256").update(c.challenge + ":" + n).digest()) < c.difficulty) n++;
await fetch("https://agent402.tools/api/outliers", { method: "POST", headers: { "X-Pow-Solution": c.token + ":" + n, "Content-Type": "application/json" }, body: JSON.stringify({"values":[1,2,3,4,5,6,7,8,9,100],"method":"iqr"}) });

Part of these workflows

This tool is one step in 3 curated multi-tool workflows - agents can fetch the whole sequence as an MCP prompt or call https://agent402.tools/api/skill-packs/{slug}/prompt.

• Trend analysis - Take any numeric time series - a stock's daily close, a FRED macro indicator, a treasury yield history - and run it through the full quantitative workup: descriptives, moving averages, trend line, outliers, optional correlation against a benchmark, and a deterministic forecast forward with a 95% prediction interval. Everything an analyst writes a notebook for, in one chain of cheap calls.
• CSV profile - Hand the pack a CSV and get back a column-by-column profile: descriptive stats, outliers, pairwise correlations, and a baseline linear regression. The deterministic 'what's in this dataset?' workup before deciding what to actually model.
• Number crunch - Statistical analysis suite: descriptive statistics, correlation analysis, and outlier detection on a single dataset.

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