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

Sachin Kumar

Abstract: 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 hypernetwork-based 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, hypernetwork-generated LoRA weights, and value-guided beam search—across four diverse benchmarks: Gorilla APIBench, Spider 2.0, WebArena, and InterCode. Our central finding is a well-supported negative result: despite generating non-trivial weight matrices, the 227.8M-parameter 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 $10 \times$ 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.