  Recent advances in large language models (LLMs) have enabled autonomous
agents with complex reasoning and task-fulfillment capabilities using a wide
range of tools. However, effectively identifying the most relevant tools for a
given task becomes a key bottleneck as the toolset size grows, hindering
reliable tool utilization. To address this, we introduce Re-Invoke, an
unsupervised tool retrieval method designed to scale effectively to large
toolsets without training. Specifically, we first generate a diverse set of
synthetic queries that comprehensively cover different aspects of the query
space associated with each tool document during the tool indexing phase.
Second, we leverage LLM's query understanding capabilities to extract key
tool-related context and underlying intents from user queries during the
inference phase. Finally, we employ a novel multi-view similarity ranking
strategy based on intents to pinpoint the most relevant tools for each query.
Our evaluation demonstrates that Re-Invoke significantly outperforms
state-of-the-art alternatives in both single-tool and multi-tool scenarios, all
within a fully unsupervised setting. Notably, on the ToolE datasets, we achieve
a 20% relative improvement in nDCG@5 for single-tool retrieval and a 39%
improvement for multi-tool retrieval.
