The Missing Link: Relational Tool Retrieval for More Capable and Consistent Agents

Recent advances in tool-calling agents have focused on expanding capability through stronger foundation models and larger tool catalogs. Enterprise environments provide a natural setting for this challenge, with knowledge distributed across large, evolving collections of tools and resources. Yet the utility of a tool ecosystem depends not only on the tools it contains, but also on the relationships connecting them. We present GEAR (Graph-Expanded Agent Retrieval), a retrieval framework that uses an ontology to link tools, concepts, and execution contexts. GEAR was designed to avoid both the fine-tuning required by COLT and the additional large language model (LLM) calls that MCP-Zero incurs at inference time. Our experiments on CAR-bench demonstrate that GEAR outperforms both baselines in capability while maintaining comparable consistency and consuming fewer tokens than MCP-Zero.