How Do Tool Surfaces Shape Inference Trajectories in LLM Agents?

Tool surfaces, the tools exposed to an agent at each model call, define its available actions and can shape its observable inference trajectory before any tool executes. We investigate their immediate effects on generated reasoning, propagation across calls, and value for execution efficiency. We introduce Tool-Surface-Controlled Trajectory Dynamics (TSCTD), a state-conditioned theoretical framework that distinguishes immediate shaping, historical effects after withdrawal, and effects of continued exposure. Controlled experiments reveal immediate changes in generated reasoning and setting-dependent behavioral carryover after tool withdrawal. They further show that explicit task-closing instructions reduce the additional token cost of continued tool exposure. Building on these findings, we develop a state-dependent configuration policy that uses a compact tool set during execution and withdraws tools once public tests pass. We evaluate this policy on programming problems from the Mostly Basic Python Problems (MBPP) benchmark, adapted into interactive coding and testing tasks. Compared with keeping all tools available throughout execution, the policy reduces aggregate token usage across tasks by 34.74%, while meeting a prespecified success noninferiority criterion. This work establishes tool exposure as a design variable for agent inference and execution and provides an experimental framework for studying when to show, retain, and withdraw tools.