Microservices offer flexibility and scalability, but their decentralized and fast-changing nature makes it difficult to maintain consistent and meaningful test coverage, particularly at the level of service endpoints. Services are developed independently, logic is spread across multiple layers, and execution paths vary widely based on input and control flow. As a result, automated testing is hard to scale, and manual testing is time-consuming and error-prone. Recent advances in large language models present a promising opportunity for generating tests automatically. However, existing approaches often rely on providing the entire source code as input, which can exceed model limitations and include unrelated logic that reduces test quality. This paper proposes a structured and scalable alternative to guide language models in generating accurate, maintainable endpoint tests that better reflect the complexity of modern microservice systems.