A State-Aware Dual-Tool Architecture for Improved Tool Use in Multi-Turn LLM Dialogue

This study investigates whether the dominant single-tool paradigm is sufficient for reliable tool use in multi-turn dialogues in which data are provided incrementally and constrained by conditional validation rules. To address the limitations of this approach, we propose a state-aware dual-tool architecture that separates data preparation and validation from final action execution, and we complement it with Token-Optimized Notation Language (TONL) as a compact representation of tool-facing state. The approach was evaluated in a controlled Django-based test platform for an insurance quotation task. Two configurations were compared across four language models (GPT-4o, GPT-5.4, Qwen3.5-122B, Nemotron-3-Super-120B) under four scripted scenarios, with 80 runs per scenario, model, and configuration (2560 total runs). Scenario-level success was defined as correct tool-use behavior over a full scripted dialogue replay, including no premature execution, no fabrication of missing values, and no payload inconsistency with the final validated state. Under this definition, the single-tool baseline achieved 74.8% success, whereas the proposed dual-tool architecture achieved 99.4% success across all evaluated runs. The strongest gain was observed for Qwen3.5-122B in the pressure scenario, where success increased from 5% (4/80) to 100% (80/80). In addition, TONL reduced the size of a representative tool-state payload by 34.0%. The results indicate that explicit completeness feedback and deterministic readiness-based execution gating can substantially improve the reliability of tool-augmented LLM systems while reducing the recurring token cost of repeated tool interactions.