{"uid":"cap_TVjaq3CYZidEtGVFVvwK-","slug":"halowerk-iot-fleet-anomaly-detector-be981f18","name":"Halowerk IoT Fleet Anomaly Detector","description":"Computes cohort median, median absolute deviation and robust z-scores for one numeric reading per device. A caller-selected threshold determines flags. It detects cross-sectional outliers only and does not diagnose root cause or learn temporal behavior.","url":"https://iot.halowerk.com/v1/fleet-anomaly","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"readings":{"type":"array","items":{"type":"object","required":["device_id","value"],"properties":{"value":{"type":"number","maximum":1000000000000,"minimum":-1000000000000},"device_id":{"type":"string","maxLength":128,"minLength":1}},"additionalProperties":false},"maxItems":10000,"minItems":3},"threshold":{"type":"number","maximum":20,"minimum":0.1,"description":"Absolute robust z-score threshold."}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.003","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.003/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.003","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.003","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_qD89_Md-euaqO3XlNLHhu","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.003","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Detects cross-sectional outliers in a fleet of IoT device readings using robust z-scores based on cohort median and median absolute deviation.","exampleAgentPrompt":"I have readings from 500 temperature sensors across my fleet — can you check which devices are outliers using a robust z-score threshold of 3.5? Here are the device IDs and their current values.","exampleUseCases":[{"title":"Factory floor sensor anomaly scan","prompt":"I've got 200 pressure sensors on the factory floor and I want to know which ones are reading abnormally compared to the rest of the fleet — flag anything with a robust z-score above 4.0."},{"title":"Smart grid meter outlier detection","prompt":"Here are current power consumption readings from 800 smart meters across the grid. Can you find which meters are statistical outliers using a threshold of 3.0?"},{"title":"Agricultural IoT soil moisture check","prompt":"I have soil moisture sensor data from 150 field devices. Which ones are way out of line with the rest of the cohort? Use a z-score threshold of 2.5 to flag the unusual ones."}],"resultDescription":"Returns per-device robust z-scores computed against the fleet cohort median and median absolute deviation, along with the cohort median, MAD value, and boolean outlier flags for devices exceeding the caller-specified threshold. Does not provide root-cause diagnosis or temporal trend analysis.","failureModes":["Fewer than 3 devices submitted — minimum array size not met","Device value outside allowed range (±1,000,000,000,000) — validation error","Threshold outside 0.1–20 range — rejected with validation error","All device values identical (MAD = 0) — division by zero edge case, may return error or undefined z-scores","Malformed device_id (empty string or exceeds 128 chars) — schema validation failure","Array exceeds 10,000 devices — request rejected"],"whenToPreferThis":"Choose this endpoint when you need to identify which devices in an IoT fleet are statistical outliers at a single point in time, using a robust (median-based) approach that is resistant to extreme values. It is ideal for cross-sectional fleet health checks where you have a cohort of 3 or more devices and want a threshold-based flag without training a model or maintaining historical state. Prefer this over time-series anomaly detectors when you only have a single snapshot of readings and want immediate, stateless results.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-14T06:39:56.996Z","isFirstParty":false}