Designing task-specific robotic hands requires translating diverse task requirements into morphologies that are both functional and manufacturable. Existing parametric and optimization-based approaches provide systematic design tools, but often rely on expert-defined objectives, manual tuning, or simulation-based search. Large language models (LLMs), with broad semantic knowledge and reasoning capabilities, offer a promising way to interpret task requirements directly from natural language. We present Lang2Morph, an LLM-driven framework that maps natural-language task descriptions to fabricable robotic hand morphologies within the Open Parametric Hand (OPH) design space. Lang2Morph combines task reasoning, structured morphology generation, geometric constraints, and feedback-guided selection to produce task-conditioned hand designs. Morphology-level analyses demonstrate high design feasibility and task-dependent structural variation, while real-world experiments with three fabricated hands reveal distinct functional characteristics across manipulation tasks. These results highlight the potential of LLMs as practical interfaces for task-driven robotic hand design.

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