{"uid":"cap_vme9JOm1MalzLE3sCBele","slug":"hallucination-detector-mcp-22dc8c2e","name":"Hallucination Detector 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/hallucination-detector-mcp/api/process","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"payload":{"type":"string"}}},"responseSchema":{"type":"json","example":{"success":true}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.3","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":"$0.3/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.3","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.3","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_gDvEpSw5xDl2NadDXBl-C","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.3","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Analyzes text to detect hallucinations, factual inconsistencies, or unsupported claims generated by AI models","exampleAgentPrompt":"Check this AI-generated response for hallucinations and flag any unsupported or fabricated claims: 'The Eiffel Tower was built in 1776 by Gustave Moreau and stands 450 meters tall.'","exampleUseCases":[{"title":"RAG pipeline output verification","prompt":"Before we return this answer to the user, run it through hallucination detection and tell me if any of the facts in this AI-generated response look made up or unsupported by the source documents."},{"title":"LLM benchmark quality audit","prompt":"I'm evaluating outputs from several language models — can you scan this generated paragraph and flag any hallucinated claims or factual inconsistencies so I can score the model's reliability?"},{"title":"Chatbot response safety gate","prompt":"Check this chatbot reply before it goes live: 'Aspirin was invented in 1920 by Alexander Fleming at Oxford University.' Does it contain any hallucinations I should worry about?"}],"resultDescription":"Returns a structured result indicating whether hallucinations were detected in the input text, along with flagged spans or claims that appear fabricated or unsupported, and likely a confidence or severity score for each detected issue.","failureModes":["Empty or null 'data' field returns an error or empty result","Extremely long text may exceed processing limits","Ambiguous or opinion-based content may produce false positives","Network timeout for large payloads","Payment failure (402) if USDC balance is insufficient"],"whenToPreferThis":"Choose this endpoint when you need to automatically screen AI-generated text for hallucinations before presenting it to end users, when auditing LLM output quality in a pipeline, or when building a safety gate that flags fabricated facts. It is especially useful for RAG systems, chatbot guardrails, and LLM evaluation workflows where factual reliability is critical.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-15T01:17:30.544Z","isFirstParty":false}