Large Language Model (LLM)-based systems increasingly rely on function calling to enable structured and controllable interaction with external data sources, yet existing datasets do not address mental health-oriented access to wearable sensor data. This paper presents a synthetic function-calling dataset designed for mental health assistance grounded in wearable health signals such as sleep, physical activity, cardiovascular measures, stress indicators, and metabolic data. The dataset maps diverse natural language queries to standardized API calls derived from a widely adopted health data schema. Each sample includes a user query, a query category, an explicit reasoning step, a normalized temporal parameter, and a target function. The dataset covers explicit, implicit, behavioral, symptom-based, and metaphorical expressions, which reflect realistic mental health-related user interactions. This resource supports research on intent grounding, temporal reasoning, and reliable function invocation in LLM-based mental health agents and is publicly released to promote reproducibility and future work.

翻译：基于大语言模型（LLM）的系统日益依赖功能调用来实现与外部数据源的结构化、可控交互，然而现有数据集未能解决面向心理健康应用的可穿戴传感器数据访问问题。本文提出了一个合成的功能调用数据集，专为基于可穿戴健康信号（如睡眠、体力活动、心血管指标、压力指示物及代谢数据）的心理健康辅助任务而设计。该数据集将多样化的自然语言查询映射至源自广泛采用的健康数据模式的标准API调用。每个样本包含用户查询、查询类别、显式推理步骤、归一化时间参数及目标函数。数据集涵盖了显式、隐式、行为驱动、症状导向及隐喻性表达，反映了现实中心理健康相关的用户交互模式。本资源支持基于LLM的心理健康智能体中意图理解、时序推理及可靠功能调用的研究，并已公开发布以促进可复现性及后续工作。