Meta-Tool: Efficient Few-Shot Tool Adaptation for Small Language Models

Can small language models achieve strong tool-use performance without complex adaptation mechanisms? This paper investigates this question through Meta-Tool, a controlled empirical study comparing hypernetworkbased LoRA adaptation against carefully designed few-shot prompting. Using a Llama-3.2-3B-Instruct backbone, we evaluate four adaptation mechanisms-few-shot prompting, documentation encoding, hypernetworkgenerated LoRA weights, and value-guided beam search-across four diverse benchmarks: Gorilla APIBench, Spider 2.0, WebArena, and InterCode. Our central finding is a wellsupported negative result: despite generating non-trivial weight matrices, the 227.8Mparameter hypernetwork provides no measurable improvement over few-shot prompting alone. Comprehensive ablation studies reveal that few-shot examples contribute +21.5% to performance and documentation contributes +5.0%, while the hypernetwork adds 0%. A 3B model with well-designed prompts achieves 79.7% of GPT-5's average performance at 10× lower latency. Error analysis across 722 failure cases spanning all shot counts (0-5) shows that at the 5-shot configuration (106 failures), failure modes are task-dependent: schema-heavy tasks (Spider 2.0, WebArena) show near-zero format errors with remaining failures semantic, while format errors dominate on Gorilla (100%) and InterCode (70%). These findings redirect practitioners toward prompt engineering and example curation rather than complex adaptation architectures. * This research was conducted independently and does not reflect the views or represent work done at LexisNexis. prise settings: frontier models like GPT-5 achieve strong tool-use performance but impose prohibitive latency and cost, while small language models (SLMs) offer efficiency but lack the procedural knowledge needed for domain-specific tools (Shen et al., 2025) . The two dominant adaptation strategies-In-Context Learning (ICL) and Supervised Fine-Tuning (SFT)-represent opposing tradeoffs: ICL provides rapid flexibility but is constrained by context limits, while SFT yields strong performance but requires thousands of annotated trajectories and costly retraining as APIs evolve (Ghosh et al., 2024; Verma et al., 2024) . Recent work on hypernetworks-secondary networks that generate task-specific parameter updates for a primary model-has shown promise for rapid adaptation in other NLP domains (Lv et al., 2024; Abdalla et al., 2025) . A natural question is whether such mechanisms can bridge the tool-use adaptation gap: given a tool's documentation and a handful of usage examples, can a hypernetwork generate LoRA adapter weights that improve tooluse performance beyond what few-shot prompting alone provides? This paper investigates this question through a controlled empirical study, with a surprising answer: for tool-use tasks across four diverse benchmarks, the answer is no. The fewshot examples and documentation fully specify the task, and hypernetwork-generated parameter updates provide no additional benefit. Our code is available at https://github.com/ techsachinkr/Meta-Tool Research Objectives and Contributions This paper presents Meta-Tool, an empirical investigation into what drives tool-use performance in small language models. Our primary contributions are: • Negative Result on Hypernetwork Adaptation: The first controlled demonstration-