The ultimate guide to MCP testing and evals

MCP testing spans server unit tests, protocol conformance, security, load testing, and MCP evals, with evals specifically assessing how AI agents interpret tool descriptions, select tools, generate arguments, execute multi-step workflows, avoid unsafe actions, and complete user requests. Effective evaluations use realistic datasets drawn from production traces, support incidents, and reviewed synthetic edge cases, covering no-tool requests, ambiguous tool choices, error recovery, permissions, and side effects; they commonly run repeated trials to account for model variability. Scoring should combine deterministic checks for calls and arguments, trajectory and final-state validation for execution, and narrowly calibrated model-based scoring for semantic outcomes, while enforcing separate thresholds for high-risk failures such as unauthorized or destructive actions. Detailed traces of available tools, calls, responses, timing, and subsequent actions help attribute failures to scorers, tool definitions, schemas, server behavior, instructions, or model capability. Tool names, descriptions, and the size and composition of a tool set can materially alter agent behavior, making versioned regression evaluation important. The text also describes MCP Inspector for protocol-level debugging, open-source harnesses and public benchmarks for broader testing, and Braintrust workflows for versioned datasets, instrumented traces, custom scoring, experiment comparisons, and CI gates that evaluate changes to models, prompts, schemas, tool definitions, and server versions.

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