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Microservice architectures are increasingly becoming the standard design for cloud-native applications, yet their complex dependency structures make architectural understanding a challenge. Our research explores how Large Language Models (LLMs) can be leveraged to automatically reconstruct microservice architectures directly from source code, a task that requires both high-level architectural reasoning and fine-grained code understanding. We introduce a Minimum Viable Agent (MVA) as a single-agent that combines tool-assisted repository exploration with role-guided prompting to infer the services, inter-service connections, and exposed endpoints in Java microservice systems. We evaluate two state-of-the-art LLMs, GPT-4o and Llama4-16:17B, on 17 open-source microservice applications with 20 independent runs per system. GPT-4o achieves a mean F1 score of 0.604, outperforming Llama4 (F1 = 0.254). Both models show higher precision than recall, suggesting a tendency to under-represent architectural relationships rather than hallucinate them. Results also reveal substantial variation across applications and architectural element types. Analysis by architectural element indicates that service-to-service connections are the most challenging to recover for both models, while large performance differences emerge from endpoint reconstruction. Overall, our findings suggest that LLMs show promise for automated architecture recovery, but current capabilities remain insufficient for fully reliable reverse engineering. We discuss implications for integrating LLMs into architecture analysis workflows and outline directions for future research.