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arXiv · 2506.19998

Doc2Agent: Scalable Generation of Tool-Using Agents from API Documentation

Xinyi Ni·Haonan Jian·Qiuyang Wang·Vedanshi Chetan Shah·Pengyu Hong
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Abstract

REST APIs play important roles in enriching the action space of web agents, yet most API-based agents rely on curated and uniform toolsets that do not reflect the complexity of real-world APIs. Building tool-using agents for arbitrary domains remains a major challenge, as it requires reading unstructured API documentation, testing APIs and inferring correct parameters. We propose Doc2Agent, a scalable pipeline to build agents that can call Python-based tools generated from API documentation. Doc2Agent generates executable tools from API documentations and iteratively refines them using a code agent. We evaluate our approach on real-world APIs, WebArena APIs, and research APIs, producing validated tools. We achieved a 55\% relative performance improvement with 90\% lower cost compared to direct API calling on WebArena benchmark. A domain-specific agent built for glycomaterial science further demonstrates the pipeline's adaptability to complex, knowledge-rich tasks. Doc2Agent offers a generalizable solution for building tool agents from unstructured API documentation at scale.

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Xinyi Ni, Haonan Jian, Qiuyang Wang, Vedanshi Chetan Shah, Pengyu Hong. 2025-06-24. Doc2Agent: Scalable Generation of Tool-Using Agents from API Documentation. https://arxiv.org/abs/2506.19998

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