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Model Hallucination Probability Oracle MCP
x402
Model Hallucination Probability Oracle MCP is a paid API for AI agents from api.m2mcent.com, paid per call via x402, $0.08/call, status unknown (last checked 2026-10-12).
Estimates the probability that a given model output contains hallucinations or fabricated information
Price / call$0.08
Success—
StatusUnknown
How this service works
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Output
Returns a hallucination probability score indicating the likelihood that the submitted text contains fabricated, incorrect, or unsupported claims generated by an AI model, enabling downstream filtering or flagging decisions.
Example use cases
Fact-check chatbot responses before delivery
“Before sending this customer support answer to the user, check what the hallucination probability is — I need to know if the model made anything up: 'Our return policy allows 60 days for all electronics purchased after January 2023.'”
Audit research summaries from LLMs
“I used an LLM to summarize this research paper and I want to know how likely it is that the summary contains hallucinated facts before I share it with my team. Here's the summary: 'The study found a 34% reduction in mortality rates when using drug X in patients over 65.'”
Quality gate for AI content pipeline
“Run a hallucination probability check on this AI-generated product description before it goes live on the website: 'This laptop features a 12-core Intel i9 processor, 64GB DDR5 RAM, and a 4K OLED display with 1ms response time.'”
Technical details
Endpoint
POSThttps://api.m2mcent.com/model-hallucination-probability-oracle-mcp/api/process?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need a quick, automated signal on whether AI-generated text is likely to contain hallucinations before surfacing it to users or downstream systems. Prefer it over manual review when operating at scale in LLM pipelines, content moderation workflows, or RAG validation layers where factual reliability is critical.
Side effects
- Empty or missing 'data' field returns an error
- Extremely long inputs may be truncated or rejected
- Non-English or low-resource language text may yield lower accuracy scores
- Ambiguous or highly technical content may produce uncertain probability estimates
- Network timeout on slow upstream inference
Output
Schema · Response
{
"type": "json",
"example": {
"success": true
}
}Tags
x402
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