Enables AI agents to verify claims against evidence through MCP tools, returning approved, rejected, or needs_review verdicts with replayable receipts.
This MCP server enables AI agents to extract, verify, and cache factual claims from text using Claude Haiku and semantic fingerprinting, with tools for content verification, signal verification, and fact memory search.
Enables AI agents to cross-verify candidate claims against caller-supplied source texts, flagging hallucinations, numerical drift, entity mismatches, contradictions, and unverified assertions. It returns sentence-level verdicts with matched evidence snippets and machine-readable factual grounding confidence scores, exposed over MCP stdio, HTTP REST, and A2A discovery routes.
An MCP server that verifies whether a claim is actually supported by the source text at a given citation — independent of what the calling LLM asserts.
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.