Building High-Quality MCP Tools with Arcade.dev Evals

Arcade.dev Evals is a framework designed to test whether large language models (LLMs) can correctly select and use MCP tools based on well-defined tool definitions, focusing on their practical application. The text highlights the importance of crafting high-quality tool definitions, emphasizing that they should not be treated like function signatures but more like detailed menu items that guide LLMs in selecting the right tool and formatting inputs correctly. Proper tool definitions, which include clear names, concise descriptions, and specific parameter formatting, significantly enhance the performance of LLMs by reducing ambiguity and token consumption during retries. The text provides examples of vague versus descriptive tool definitions and demonstrates how descriptive versions perform better in tests. Arcade Evals is built into the Arcade CLI and offers a method to evaluate MCP tools' effectiveness across multiple models without executing tools, ensuring that LLMs can accurately match and fill in tool parameters.

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