{"uid":"cap_0EXYSR8uhSS3ZIslEJXqK","slug":"anomaly-detection-sentinel-mcp-7a151251","name":"Anomaly Detection Sentinel MCP","description":"The premier global index of 1,069 monetized MCP nodes across 205 specialized subdomains. Gasless USDC runtime settlements via x402 V2 Spec on Base L2. Save 95% token context.","url":"https://api.m2mcent.com/anomaly-detection-sentinel-mcp/api/process?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":null,"responseSchema":{"type":"json","example":{"success":true}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.1","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":"$0.1/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.1","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.1","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_-X53W7AJjO-_uLBCAkBla","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.1","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Analyzes input data to detect anomalies, outliers, or unexpected patterns using a sentinel-style monitoring approach","exampleAgentPrompt":"Can you scan this dataset for anomalies and flag anything that looks unusual or out of the ordinary? Here's the data: [1.2, 1.3, 1.1, 45.7, 1.2, 1.4, 0.9]","exampleUseCases":[{"title":"IoT sensor spike detection","prompt":"I've got a stream of temperature readings from my factory floor sensors — can you run anomaly detection on this data and tell me if any readings look abnormal? Data: '72.1,71.8,72.3,71.9,142.5,72.0,71.7'"},{"title":"Financial transaction fraud flagging","prompt":"Please analyze these transaction amounts and flag anything that looks like an outlier or potential fraud: '120.50, 98.30, 115.20, 4500.00, 103.80, 99.10'"},{"title":"Application log error pattern detection","prompt":"Here are my application log error counts per hour over the last 24 hours — can you detect any anomalies that might indicate an incident? Data: '5,4,6,5,3,5,4,87,6,5,4,3'"}],"resultDescription":"Returns a structured result indicating whether anomalies were detected in the submitted data, along with flagged data points, anomaly scores or indicators, and contextual details about the nature of the detected irregularities.","failureModes":["Empty or malformed data string returns an error response","Non-numeric or unparseable data may fail detection","Extremely small datasets may yield unreliable anomaly scores","Service unavailability may return HTTP 402 or 5xx errors","Payment failure via x402 protocol results in request rejection"],"whenToPreferThis":"Choose this endpoint when you need a fast, pay-per-call anomaly detection service for string-encoded data, particularly in agentic workflows where you want to programmatically flag outliers without setting up dedicated ML infrastructure. Best suited for one-off or low-frequency anomaly checks rather than continuous high-volume streaming.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-12T12:51:40.516Z","isFirstParty":false,"canonicalSlug":"anomaly-detection-sentinel-mcp-7a151251"}