The Step-By-Step Guide to MCP Evaluation

The Model Context Protocol (MCP) is an open standard introduced by Anthropic in 2024 that enables large language models (LLMs) to interact with external tools and data sources, enhancing their task completion capabilities. MCP evaluation assesses how effectively LLM applications utilize this framework by ensuring correct tool usage, argument generation, and task completion. DeepEval, an open-source LLM evaluation framework, facilitates this process by offering metrics tailored for both single-turn and multi-turn MCP applications. The evaluation process involves adding MCP servers, tracking interactions, creating test cases, and running evaluations. These evaluations provide insights into an application's performance, helping developers refine their applications by choosing appropriate MCP servers, refining context descriptions, and selecting optimal LLM models. Confident AI's DeepEval platform offers comprehensive evaluation reports, aiding in the continuous improvement of MCP-based applications by providing visibility and observability into their operational effectiveness.