mind_call: A Dataset for Mental Health Function Calling with Large Language Models
On the map: LLM agents and tool use → Medical and scientific domain agents
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use. Builds on 8 of its 11 references that are in the map. First posted on arXiv 11 Jan 2026.
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8 of 11 references.
References · 8
- 2022Training language models to follow instructions with human feedbackcited by 15,971
- 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
- 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
- 2023A Comprehensive Overview of Large Language Modelscited by 648
- 2024ToolACE: Winning the Points of LLM Function Callingcited by 216
- 2025Think Twice: Enhancing LLM Reasoning by Scaling Multi-round Test-time Thinkingcited by 46
- 2024Applying and Evaluating Large Language Models in Mental Health Care: A Scoping Review of Human-Assessed Generative Taskscited by 6
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- 2023From Classification to Clinical Insights: Towards Analyzing and Reasoning About Mobile and Behavioral Health Data With Large Language Models
- 2025MindEval: Benchmarking Language Models on Multi-turn Mental Health Support
- 2025LENS: LLM-Enabled Narrative Synthesis for Mental Health by Aligning Multimodal Sensing with Language Models
- 2026BALMS: Benchmarking Agentic LLMs for Longitudinal Mental Health Sensing
- 2025ProMind-LLM: Proactive Mental Health Care via Causal Reasoning with Sensor Data
- 2023An Integrative Survey on Mental Health Conversational Agents to Bridge Computer Science and Medical Perspectives
- 2023Towards Interpretable Mental Health Analysis with Large Language Models
- 2024Transforming Wearable Data into Personal Health Insights using Large Language Model Agents