Quantum computing has rapidly emerged as a powerful approach for addressing complex optimization problems, particularly in combinatorial optimization. Variational Quantum Circuits (VQCs), a key element in this domain, have proven to be especially promising for near-term quantum devices. These circuits underpin the Variational Quantum Eigensolver (VQE), widely applied in solving optimization problems such as MaxCut. The performance of VQCs, however, is significantly influenced by the choice of ansatz, which determines both the trainability and expressivity of the quantum model. In this paper, we introduce Quantum Trainability & Expressivity Evaluation (Q-TEE), a novel service-oriented tool designed to assess the trainability and expressivity of different ansatzes within VQCs. Q-TEE serves as a contribution to the growing field of Quantum Service Engineering, where the development of tools and frameworks supports the deployment of quantum algorithms as services. By applying Q-TEE to the MaxCut problem, we offer insights into how these metrics can be optimized to improve performance across different problem sizes and ansatz architectures.