[2605.00737] To Call or Not to Call: A Framework to Assess and Optimize LLM Tool Calling

Computer Science > Artificial Intelligence arXiv:2605.00737 (cs) [Submitted on 1 May 2026] Title:To Call or Not to Call: A Framework to Assess and Optimize LLM Tool Calling Authors:Qinyuan Wu, Soumi Das, Mahsa Amani, Arijit Nag, Seungeon Lee, Krishna P. Gummadi, Abhilasha Ravichander, Muhammad Bilal Zafar View a PDF of the paper titled To Call or Not to Call: A Framework to Assess and Optimize LLM Tool Calling, by Qinyuan Wu and 7 other authors View PDF HTML (experimental) Abstract:Agentic AI architectures augment LLMs with external tools, unlocking strong capabilities. However, tool use is not always beneficial; some calls may be redundant or even harmful. Effective tool use, therefore, hinges on a core LLM decision: whether to call or not call a tool, when performing a task. This decision is particularly challenging for web search tools, where the benefits of external information depend on the model's internal knowledge and its ability to integrate potentially noisy tool responses. We introduce a principled framework inspired by decision-making theory to evaluate web search tool-use decisions along three key factors: necessity, utility, and affordability. Our analysis combines two complementary lenses: a normative perspective that infers true need and utility from an optimal allocation of tool calls, and a descriptive perspective that infers the model's self-perceived need and utility from their observed behaviors. We find that models' perceived need and utility of tool ...