While general-purpose large language models (LLMs) demonstrate robust problem-solving capabilities through universal tools, their effectiveness in specialized domains remains constrained by insufficient professional tool usage and prohibitive customization costs for end-users. This paper presents the Tool-Responsive Instruction-Aligned Development (TRIAD) framework, enabling resource-efficient enhancement of domain-specific capabilities in small language models through synergistic tool-data optimization. The framework comprises three synergistic components: (1) Tool-Semantic Anchored Dataset Construction filters non-geometric problems from MATH [11] and converts them to Wolfram Language code (3,366 samples); (2) Autonomous Prompt Optimization employs DeepSeek-R1 [7] guided iterative refinement to develop tool-adapted templates, achieving significant code structure similarity improvements; (3) Tool-Sensitive Instruction Tuning integrates domain knowledge via LoRA-based parameter-efficient adaptation [13]. Experiments reveal TRIAD’s substantial performance gains across 7B-parameter models: Qwen2-7B-instruct [27] shows 42.6% TUPS improvement through APO optimization, while Gemma-7B [19] and Mistral-7B-instruct [14] achieve TUPS boosts from 15.0%  \(\rightarrow \)  60.5% and 32.6%  \(\rightarrow \)  58.4% respectively via full TRIAD implementation. This work proposes a cost-effective framework to enhance small language models domain capabilities, with experimental results supporting its effectiveness.