Large language models (LLMs) have recently advanced text-driven 3D generation, yet generating CAD models that satisfy practical design requirements remains challenging. Existing benchmarks focus primarily on single-part CAD models and geometric similarity, providing limited assessment of assembly structure and interface geometry. To address this gap, we introduce MUSE, a Text-to-CAD benchmark for generating multi-component, editable boundary representation (B-Rep) assemblies. MUSE pairs practical design instances with structured \textit{Design Specifications} and evaluates generated outputs across four dimensions: code executability, geometric validity, structural and interface fidelity, and functional consistency. We further introduce Structure and Interface Evaluation (SIE), which combines reference Physical Assembly Graphs with direct B-Rep analysis to assess component correspondence, connectivity, interface types, and interface clearances. Experiments on proprietary and open-weight LLMs show that high execution success does not necessarily translate into correct assembly structure or interface geometry. Together, MUSE provides a benchmark and evaluation framework for studying multi-component Text-to-CAD generation beyond shape similarity. Our project website, including the leaderboard, dataset, and code, is available at \url{https://dong7313.github.io/muse-benchmark/}.

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