Problem
Research questions and friction points this paper is trying to address.
tool-using AI agents
reliability
intrinsic tool accuracy
community-driven framework
tool integration
Open, Reliable, and Collective: A Community-Driven Framework for Tool-Using AI Agents
🤖 AI Summary
This work addresses a critical limitation in current tool-augmented AI agent research—the frequent neglect of tools’ intrinsic accuracy, which undermines overall system reliability. To remedy this, the authors propose OpenTools, a novel framework that systematically emphasizes and optimizes the inherent correctness of individual tools. OpenTools establishes an open, evolvable, and community-driven tool ecosystem through standardized tool schemas, lightweight wrappers, automated test suites, and continuous monitoring mechanisms. The framework enables users to contribute both tools and test cases and includes a public web demonstration platform. Empirical evaluation demonstrates that high-quality, domain-specific tools contributed by the community yield relative performance gains of 6%–22% across diverse agent architectures, substantially improving end-to-end reproducibility and task effectiveness.
📝 Abstract
Tool-integrated LLMs can retrieve, compute, and take real-world actions via external tools, but reliability remains a key bottleneck. We argue that failures stem from both tool-use accuracy (how well an agent invokes a tool) and intrinsic tool accuracy (the tool's own correctness), while most prior work emphasizes the former. We introduce OpenTools, a community-driven toolbox that standardizes tool schemas, provides lightweight plug-and-play wrappers, and evaluates tools with automated test suites and continuous monitoring. We also release a public web demo where users can run predefined agents and tools and contribute test cases, enabling reliability reports to evolve as tools change. OpenTools includes the core framework, an initial tool set, evaluation pipelines, and a contribution protocol. Experiments and evaluations show improved end-to-end reproducibility and task performance; community-contributed, higher-quality task-specific tools deliver 6%-22% relative gains over an existing toolbox across multiple agent architectures on downstream tasks and benchmarks, highlighting the importance of intrinsic tool accuracy.
Innovation
Methods, ideas, or system contributions that make the work stand out.
tool-using AI agents
intrinsic tool accuracy
community-driven framework
automated evaluation
reproducibility
🔎 Similar Papers
No similar papers found.
