An MCP server that exposes RAG retrieval evaluation as agent tools, allowing agents to retrieve passages and measure retrieval quality across multiple strategies.
An MCP server that exposes a document store for evaluating agent tool-use accuracy and measuring resistance to indirect prompt injection, with built-in conformance to the 2026-07-28 MCP protocol.
A specialized testing harness that enables AI assistants to thoroughly test other MCP servers by connecting to them, discovering their tools/resources/prompts, executing test calls, and performing end-to-end validation with LLM integration.
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.
An MCP server that evaluates whether retrieval methods and AI outputs are grounded in long narrative manuscripts by retrieving evidence and scoring coverage deterministically, without external model APIs. It provides tools for chunking, indexing, retrieval, and evaluation.
Exposes hybrid document retrieval and evaluation tools from retrieval-lab via MCP, including search_documents, corpus_info, run_evaluation, and search_my_repos, with wire-level conformance tests.