Enables LLM evaluation and observability by uploading documents, building test sets, running RAG pipelines, and automatically scoring answers for groundedness, hallucination risk, retrieval quality, latency, and cost, with tools exposed to MCP-compatible clients.
MCP-native auditor for LLM hallucination and grounding issues in RAG systems. Provides prioritized findings in table, JSON, or SARIF format for CI gating and AI agent integration.
A local MCP server that packages LLM evaluation gates as reusable CI/CD primitives, enabling AI agents to run datasets against models, score responses, and enforce quality thresholds.
An MCP server that lets any AI agent evaluate RAG outputs -- faithfulness scoring, hallucination detection, and retrieval quality metrics -- with zero API keys, using MCP sampling.
Exposes a deterministic AI-slop scanner and RAG grounding grader as MCP Tools, Resource, and Prompt, enabling any MCP client to evaluate text quality and context faithfulness.
