Arkheia Hallucinationofficial
A
licenseNot graded
qualityD
maintenanceDetect fabrication and hallucination in any LLM output. Score responses from GPT-4o, Claude, Gemini, Llama and 30+ models. Free tier included.
2
MIT
groundlens
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TDQS
A3.9/5.0
Scored across 3 tools
Disambiguation4/5
Each tool takes a clearly different input shape: a JSON-RPC session trace, answer passages, and a JSON Lines record log. There is mild overlap because verify_records can validate run records that verify_run also produces/validates, which could cause a brief hesitation, but the described inputs keep them separable.
Naming Consistency5/5
All three tools follow a strict verb_noun pattern (verify_run, verify_answer, verify_records), with the same verb used consistently for the same conceptual action.
Tool Count4/5
Three tools is on the lean side but each covers a distinct verification target (run trace, answer grounding, record log), so nothing feels redundant or padded.
Completeness4/5
The core verification lifecycle (verify runs, verify answers, verify logs offline) is covered, and records are produced by the verify calls themselves. A minor gap is that no tool enumerates available built-in policies (e.g. 'eu_ai_act_high_risk_v1'), though agents can work around this by passing policy text or a path.
Maintenance
ActivityActive
ResponsivenessNo issues