ToolMenuBench: Benchmarking Tool-Menu Filtering Strategies for Reliable and Efficient LLM Agents

2026-06-13 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

ToolMenuBench is a new benchmark designed to evaluate tool-menu construction in multi-step large language model (LLM) agents, addressing the gap in existing evaluations that overlook how visible tool menus impact reliability, efficiency, and safety. The benchmark systematically varies tool-menu size, distractor type, state-dependent task structure, and risk exposure, reporting both filter-level and downstream agent metrics like visible-tool count, risky-tool exposure, task success, wrong-tool calls, premature actions, and token usage. In a controlled evaluation involving seven model backends, three tool-menu sizes, six filtering methods, and seven settings, Causal Minimal Tool Filtering (CMTF) significantly improved task success from 32.1% to 85.7% while reducing average token usage by approximately 98%. CMTF demonstrated the strongest overall tradeoff, minimizing visible tools, wrong-tool calls, premature actions, and risky-tool exposure compared to other filtering strategies.

Key takeaway

For AI Engineers developing LLM agents that interact with extensive tool libraries, you should prioritize implementing sophisticated tool-menu filtering strategies. Consider Causal Minimal Tool Filtering (CMTF) to drastically improve task success from 32.1% to 85.7% and reduce token usage by approximately 98%. This approach also mitigates risky-tool exposure and wrong-tool calls, significantly enhancing your agent's overall reliability and efficiency in multi-step tasks.

Key insights

Causal Minimal Tool Filtering significantly enhances LLM agent reliability, efficiency, and safety by optimizing tool menu exposure.

Principles

• Tool menu size directly affects agent reliability.
• Distractor tools degrade agent performance.
• Filtering reduces risky tool exposure.

Method

ToolMenuBench evaluates filtering strategies by varying menu size, distractor type, state-dependency, and risk exposure, measuring visible tools, risky exposure, task success, wrong calls, premature actions, and token usage.

In practice

• Apply Causal Minimal Tool Filtering.
• Use ToolMenuBench for agent evaluation.
• Prioritize filtering for safety-critical agents.

Topics

• LLM Agents
• Tool Use
• Tool Filtering
• ToolMenuBench
• Agent Reliability
• Token Efficiency

Best for: AI Architect, NLP Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, AI Engineer

Related on AIssential

• ToolChoiceConfusion: Causal Minimal Tool Filtering for Reliable LLM Agents — CMTF enhances LLM agent reliability and efficiency by causally filtering tools, exposing only the minimal set needed for the next step.
• ToolChoiceConfusion: Causal Minimal Tool Filtering for Reliable LLM Agents — Reliable LLM agent tool selection hinges on causal necessity, not just semantic relevance or executability.
• GIST-CMTF: Goal-State Inference for Causal Minimal Tool Filtering in LLM Agents — Reliable tool-augmented LLM agents must validate goal state, not just tool relevance, before external actions.
• Looking Is Not Picking: An Attention-Segment Account of Tool-Selection Failures in LLM Agents — LLM tool-selection failures stem from decision readout, not attention, despite models often attending to the correct tool.
• AgentNoiseBench: Benchmarking Robustness of Tool-Using LLM Agents Under Noisy Condition — LLM agents universally suffer significant performance degradation under realistic noise, especially from tool failures.
• When Does Memory Help Multi-Trajectory Inference for Tool-Use LLM Agents? — The effectiveness of memory methods in tool-use LLM agents is highly dependent on the chosen multi-trajectory inference strategy.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.