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Hallucination Detector MCP
x402
Hallucination Detector MCP is a paid API for AI agents from api.m2mcent.com, paid per call via x402, $0.3/call, status unknown (last checked 2026-09-15).
Analyzes text to detect hallucinations, factual inconsistencies, or unsupported claims generated by AI models
Price / call$0.3
Success—
StatusUnknown
How this service works
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Output
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.
Example use cases
RAG pipeline output verification
“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.”
LLM benchmark quality audit
“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?”
Chatbot response safety gate
“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?”
Technical details
Endpoint
POSThttps://api.m2mcent.com/hallucination-detector-mcp/api/processWhen to prefer
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.
Side effects
- 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
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| payload | string |
Output
Schema · Response
{
"type": "json",
"example": {
"success": true
}
}Tags
x402
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