{"uid":"cap_UUHBbiO3Zk-Zqb8QfuDgC","slug":"healthcare-fraud-detection-synthesizer-mcp-f20fc61f","name":"Healthcare Fraud Detection Synthesizer 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/healthcare-fraud-detection-synthesizer-mcp-345/api/execute?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","required":["dataset"],"properties":{"dataset":{"type":"string"}}},"responseSchema":{"type":"object","required":["hash"],"properties":{"hash":{"type":"boolean"}}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.15","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":"$0.593381/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.593381","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.593381","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_boLyaies5vHYlpjtwGgej","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.593381","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Analyzes a healthcare dataset to detect and synthesize fraud patterns using a remote MCP node.","exampleAgentPrompt":"Analyze this healthcare claims dataset for fraud patterns and tell me what suspicious activity is detected: [paste dataset here].","exampleUseCases":[{"title":"Insurance claims fraud screening","prompt":"I have a batch of medical insurance claims data — can you run it through the healthcare fraud detection synthesizer and flag anything that looks fraudulent or anomalous?"},{"title":"Medical billing compliance audit","prompt":"We need to audit our hospital's billing records for potential fraud before our compliance review. Can you analyze this dataset and synthesize any fraud indicators?"},{"title":"Provider billing anomaly detection","prompt":"I've got a dataset of provider billing submissions from last quarter. Run it through the fraud detection system and tell me which entries show suspicious patterns."}],"resultDescription":"Returns a JSON object containing a 'hash' boolean field indicating whether fraud was detected or a fraud signal was synthesized from the submitted dataset.","failureModes":["Missing or empty 'dataset' field returns a 400 validation error","Malformed dataset string may yield unexpected boolean results","Payment failure via x402 protocol returns a 402 Payment Required response","Large datasets may time out or return incomplete results","The boolean-only response schema provides no detailed fraud breakdown or confidence scores"],"whenToPreferThis":"Use this endpoint when you need a quick, low-cost fraud signal on a healthcare dataset and can accept a binary (boolean) result. Best suited for automated pipelines that triage claims before deeper analysis. Avoid if you need detailed fraud explanations, confidence scores, or structured fraud category breakdowns.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-13T06:55:34.336Z","isFirstParty":false,"canonicalSlug":"healthcare-fraud-detection-synthesizer-mcp-f20fc61f"}