Building and testing a Model Context Protocol (MCP) server involves ensuring both technical correctness and effective usability by AI models. The process requires traditional API testing to verify technical aspects like tool discovery, parameter validation, error handling, and content validation, ensuring that the server returns the correct information. A crucial aspect of technical testing is covering edge cases and ensuring error messages are precise to prevent AI models from repeating mistakes. Behavioral testing, however, assesses whether AI models can effectively use the server's tools, which involves testing real-world scenarios by prompting models like GPT-4 to interact with the server. This approach helps identify issues that may not be apparent in unit tests, such as confusing tool descriptions or mismatched parameter names. A comprehensive testing strategy begins with validating basic functionality, followed by parameter validation, edge case testing, and error handling, then concludes with behavioral tests to ensure practical usability. The goal of testing is not just to create a functioning MCP server but one that is intuitive and useful for AI models, thus elevating it from a simple tool to a valuable resource.