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Hallucination Detector via Consistency

Hallucination Detector via Consistency MCP Connector for Claude

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Detect factual contradictions across multiple LLM responses to identify potential hallucinations.

3 tools Official Updated Oct 1, 2026 Official Vinkius Partner
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This MCP server provides a suite of tools to verify the reliability of LLM outputs by checking for factual consistency. By analyzing multiple responses to the same query, it uses deterministic claim extraction to identify conflicting dates, numbers, or entities. Use analyze_consistency to get a high-level report, extract_claims to break down specific assertions, or find_contradictions to pinpoint logical conflicts between sets of claims.

hallucinationconsistencyllmfact-checkingvalidation

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

extract_claims

Decomposes text into individual factual assertions

analyze_consistency

Evaluates a set of LLM responses to determine if they are factually consistent

find_contradictions

Compares sets of claims to identify logical conflicts

See how to talk to your AI agent using Hallucination Detector via Consistency.

Check if these responses are consistent: ['The event happened in 2021.', 'The event occurred in 2022.']

The responses are inconsistent. The event date is contradictory (2021 vs 2022).

Extract the factual claims from: 'The population is 5 million and the capital is Paris.'

The extracted claims are: population is 5 million, capital is Paris.

Are these responses consistent? ['The price is $10.', 'The price is $10.']

Yes, the responses are consistent.

The score is calculated by subtracting the ratio of unique contradictions to the total number of extracted claims from 1.0. A score of 1.0 means perfect agreement.

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