Evaluation of text-to-speech (TTS) models is currently dominated by Mean-Opinion-Score (MOS) listening test, but MOS has been increasingly questioned for its validity. MOS tests place listeners in a passive setup, in which they do not actively interact with the TTS and usually evaluate isolated utterances without context. Thus it gives no indication for how well a TTS model suits an interactive application like spoken dialogue system, in which the capability of generating appropriate speech in the dialogue context is paramount. We aim to take a first step towards addressing this shortcoming by evaluating several state-of-the-art neural TTS models, including one that adapts to dialogue context, in a custom-built spoken dialogue system. We present system design, experiment setup, and results. Our work is the first to evaluate TTS in contextual dialogue system interactions. We also discuss the shortcomings and future opportunities of the proposed evaluation paradigm.