agent-artifex:implement

Improving AI tool descriptions involves refactoring text based on design principles to help agents select the correct tools. You refine descriptions, fix error messages, and add output schemas to optimize system prompts and overall code quality.

Writing tests for MCP server correctness requires establishing robust testing frameworks that verify code correctness, agent behavior, and response accuracy. You implement unit tests, integration tests, and evaluation harnesses to ensure server reliability.

The best way to refactor error messages in AI services code is to apply established design principles and best practices. This improves robustness and reliability by ensuring messages are clear and actionable within the server environment.

Yes, you can add output schemas to existing AI system prompts. This approach supports optimizing system prompts and refining tool descriptions according to a provided rubric to ensure accurate AI agent responses.

Setting up an evaluation harness for AI agent response accuracy involves implementing comprehensive tests within your development workflow. You write integration tests and evaluation harnesses to verify that agents select correct tools and generate accurate outputs.

Your AI agent may select the wrong tools if descriptions do not adhere to design principles. Refactoring tool descriptions and adding output schemas based on a rubric ensures the agent correctly interprets and selects the intended tools.