Explainability is increasingly required in AI-enabled software systems to support transparency, user trust, and compliance. Yet, explainability requirements are often written ad hoc, and unguided large language model support can yield vague, inconsistent, or incomplete statements. This paper presents a sequential, guideline-driven workflow for formulating explainability requirements and evaluates its tool-based operationalization. We first elicited candidate quality properties through a structured literature review and developer interviews. We then prioritized these properties in an online survey with practitioners (n = 20) and derived a concise guideline of ten core properties with actionable formulation instructions. Next, we operationalized the guideline in a web-based tool that supports an iterative workflow of drafting, property-based checks, and revision. We evaluated the workflow in two complementary studies. In a task-based study with requirements engineers (n = 6), formulation time was 23.5% lower with tool support (mixed-effects model p = 0.049, Wilcoxon sensitivity analysis p = 0.021). In an independent online study with software developers (n = 18), tool-supported and manually written requirements did not differ significantly in implementability or formulation quality, with a descriptive slight preference tendency toward the tool-supported versions. Overall, our results suggest that combining a prioritized quality guideline with lightweight LLM support can reduce formulation effort without significant differences in perceived quality from manually written requirements.

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