A multi-agent verification MCP server that uses cross-family LLM critics, NLI, and consistency checks to minimize hallucinations and false claims in LLM outputs.
A standalone MCP server that validates model output against retrieved sources. It flags any claim, statistic, attribution, quote, or URL that cannot be traced back to a real source.
An MCP server that enforces fail-closed deterministic checks, independent refute-first review, and tamper-evident hash-chained receipts for AI agent outputs before claiming completion.
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.
A local MCP reasoning gate that enables structured engineering thought through step-by-step reasoning, branching, merging, validation, and quality metrics without calling external LLM APIs.