MCP server for measuring, tracking, scoring, and improving AI agent reliability with tools for recording interactions, scoring reliability, analyzing failures, recommending improvements, generating audit reports, and checking MCP health.
An MCP server that audits LLM-as-judge evaluations, detecting judge drift across runs, measuring bias through controlled probes, and comparing judge agreement with human raters.
MCP server that analyzes AI agent execution logs to calculate reliability scores, detect failure patterns, and suggest concrete improvements for making AI agents more reliable.
An MCP server that enables agents to evaluate LLMs daily through capability benchmarks and value alignment tests, providing tools to list models, get almanac, judge dilemmas, match user values, and score models.
MCP server that computes trust scores, permission decisions, and silent-failure risk for AI agents with tools for reliability scoring, silent failure detection, permission evaluation, and audit report generation.
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.
