Researchers propose using small language models to verify each agent tool call against task intent, with a multi-tool dataset spanning distinct MCP servers
The study investigates whether small language models (SLMs) can serve as task-tool relevance classifiers that evaluate each selected tool independently against the assigned task and return a relevance signal for downstream enforcement, and it introduces a novel dataset of multi-tool tasks whose required tools span distinct Model Context Protocol (MCP) servers, using prompt optimization, supervised fine-tuning, and reinforcement learning through GRPO to optimize and specialize SLMs.