MCP server implementing Chain-of-Verification to reduce LLM hallucinations by drafting answers, planning fact-check questions, answering them independently, and revising.
A QA verification MCP server that prevents AI hallucination by using adversarial verification with screenshots and subagents for accessibility, visual regression, and UI verification.
Adaptive Retrieval-Augmented Self-Refinement MCP Server — a closed-loop system that lets LLMs iteratively verify and correct their own claims using uncertainty-guided retrieval.
A deterministic verification gate for MCP clients that independently checks model outputs against evidence, contradictions, calibration, and provenance without relying on LLM self-assessment.
The first MCP server that verifies AI outputs in real-time, ensuring every LLM response is correct, complete, and reliable before it reaches your editor.