What is an MCP eval? MCP testing explained

An MCP eval is a repeatable, multi-trial assessment of how reliably an AI agent uses a specific Model Context Protocol server to complete realistic tasks, recording model decisions, tool calls, arguments, responses, errors, and resulting system state in detailed traces. Unlike server and protocol tests, which verify deterministic responses to known requests, MCP evals measure whether an agent can interpret tool descriptions, select necessary and appropriate tools, construct valid arguments, sequence multi-step actions, recover from failures, and actually achieve the requested outcome. Evaluations should mirror the production configuration, including transport, authentication, permissions, tools, and server version, because changes to models, instructions, clients, schemas, tool descriptions, permissions, or available tools can alter agent behavior even when server tests still pass. Effective scoring combines trajectory analysis and state assertions with deterministic checks, model-based judges, and human review, while repeated trials produce pass rates that reveal inconsistency rather than relying on a single successful run. The framework can assess MCP tools, resources, and prompts, distinguish application-specific readiness testing from public benchmarks, and support release decisions by turning production failures into regression cases run through CI/CD.

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