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[2602.15197] OpaqueToolsBench: Learning Nuances of Tool Behavior Through Interaction
Summary
The paper introduces OpaqueToolsBench, a benchmark for evaluating Large Language Model (LLM) agents' performance with opaque tools, proposing a framework to improve tool documentation through interaction.
Why It Matters
As LLMs increasingly interact with real-world tools, understanding their behavior is crucial for effective task completion. This research addresses the challenges posed by opaque tools, providing insights into improving LLM efficiency and reliability in practical applications.
Key Takeaways
- OpaqueToolsBench evaluates LLM performance with poorly documented tools.
- Existing documentation methods are costly and unreliable for opaque tools.
- The proposed ToolObserver framework enhances tool documentation iteratively.
- The new method outperforms existing approaches in efficiency and effectiveness.
- Results indicate significant token savings during tool exploration.
Computer Science > Computation and Language arXiv:2602.15197 (cs) [Submitted on 16 Feb 2026] Title:OpaqueToolsBench: Learning Nuances of Tool Behavior Through Interaction Authors:Skyler Hallinan, Thejas Venkatesh, Xiang Ren, Sai Praneeth Karimireddy, Ashwin Paranjape, Yuhao Zhang, Jack Hessel View a PDF of the paper titled OpaqueToolsBench: Learning Nuances of Tool Behavior Through Interaction, by Skyler Hallinan and 6 other authors View PDF HTML (experimental) Abstract:Tool-calling is essential for Large Language Model (LLM) agents to complete real-world tasks. While most existing benchmarks assume simple, perfectly documented tools, real-world tools (e.g., general "search" APIs) are often opaque, lacking clear best practices or failure modes. Can LLM agents improve their performance in environments with opaque tools by interacting and subsequently improving documentation? To study this, we create OpaqueToolsBench, a benchmark consisting of three distinct task-oriented environments: general function calling, interactive chess playing, and long-trajectory agentic search. Each environment provides underspecified tools that models must learn to use effectively to complete the task. Results on OpaqueToolsBench suggest existing methods for automatically documenting tools are expensive and unreliable when tools are opaque. To address this, we propose a simple framework, ToolObserver, that iteratively refines tool documentation by observing execution feedback from tool-calling t...
