From Documentation to Action: Learning to Register Unseen Tools for LLM Agents

Tool use extends the capabilities of large language models (LLMs) beyond text generation to interaction with external systems. Existing approaches primarily supply tool documentation through retrieval or learn to generate tool identifiers. Connecting document-based tool selection to argument generation through a compact interface remains a key challenge. To address this challenge, we introduce Unseen Tool Registration (UTR), a framework that compiles tool documentation into a continuous output embedding and a compact memory. The output embedding is scored against the request hidden state within the model's output layer, enabling a unified next-token decision over tools and text. The selected tool's memory then guides argument generation without placing the full documentation in the task context or requiring further training for newly registered tools. Two-stage training first learns to select tools using document-generated embeddings, then teaches the same decoder to generate arguments from memory while retaining selection ability. Both stages optimize components shared across tools, allowing unseen tools to be registered through one document encoding pass without parameter updates. Evaluations on STQ, ToolE, and ToolBench show that UTR matches or exceeds strong baselines in tool selection. On STQ, UTR achieves 93.43% tool exact match and 74.77% call exact match, improving the latter by 6.29 percentage points over the strong baseline, and reaches 84.67% top-1 selection accuracy on ToolE-OOD. UTR also demonstrates strong performance in large-scale tool selection across tool libraries of varying sizes.