MCP For Claude Marketplace
Hallucination Detection Score

Hallucination Detection Score MCP Connector for Claude

A+

Quantify the reliability of AI agent outputs using deterministic hallucination scoring.

3 tools Official Updated Oct 1, 2026 Official Vinkius Partner
More Details Connect to Claude

This MCP server provides a deterministic framework for measuring the accuracy and reliability of AI agent responses. By comparing agent outputs against a verified knowledge base and consensus from other agents, it calculates a precise hallucination probability and confidence score. Use analyze_claim_accuracy to get a full risk assessment, extract_fact_claims to isolate individual assertions, or calculate_consensus_metrics to check agreement with peer outputs.

hallucinationaccuracyscoringverificationconsensus

3 tools expose this connector's capabilities to your AI agent.

analyze_claim_accuracy

Evaluate truthfulness and consensus

calculate_consensus_metrics

Compare claims against peer outputs

extract_fact_claims

Isolate individual assertions

See how to talk to your AI agent using Hallucination Detection Score.

Analyze this text for hallucinations: 'The capital of France is Lyon and it is known for its pizza.'

The output has a High Risk level. The claim that Lyon is the capital of France is incorrect; the capital is Paris.

Extract the fact claims from: 'The sun is a star and it provides energy to Earth.'

1. The sun is a star. 2. The sun provides energy to Earth.

Check the consensus for the claim 'The moon orbits the Earth' against the peer output 'The Earth orbits the Sun'.

The agreement ratio is 0.0, as the peer output does not support the specific claim about the moon's orbit.

The probability is a weighted metric: 50% for claims verified by the reference knowledge base, 30% for consensus with other agents, and 20% for claims with source attribution.

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