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.
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.
An MCP server that builds structured research source packs for a topic, extracting verified facts, quotes, numbers, dates, and primary links with a coverage map of claims across sources.
MCP server for verifying AI agent claims vs reality — single-transcript inline grounding-check that flags when an agent's response states facts not in the input context, when its code silently swallows exceptions and substitutes mock data, or when its multi-turn transcript contains contradictions or unverified completion claims. Sub-second, local, free, no API calls.
A standalone MCP server that validates and cleans structured data returned by AI agents, removing invented fields, coercing incorrect types, and ensuring the output matches the expected schema before it enters your pipeline.