Everything Is a Test: How to Evaluate MCP Tools for Reliable AI Agents

Arcade Evals is a system designed to evaluate the effectiveness of tool definitions for Machine Learning Models (LLMs) in simulated environments, similar to how language instruction uses role-playing to prepare students for real-world interactions. By creating role-play scenarios where the LLM acts as a student, Arcade Evals allows developers to test the clarity and usability of tool definitions without executing real API calls. This method ensures that tools can be selected and populated with the correct arguments by the model, providing feedback on whether the tool descriptions are intuitive and aiding in iterative improvements. The evaluation framework uses rubrics to score tool performance, aiming to improve agent reliability by ensuring tools work well across diverse model capabilities. While it does not validate the actual execution of tools, this approach allows for safer and more cost-effective testing of tool schemas and definitions before deploying them to production environments.

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