In-N-Out: A Parameter-Level API Graph Dataset for Tool Agents

• URL: http://arxiv.org/abs/2509.01560v1
• Date: Mon, 01 Sep 2025 15:42:21 GMT
• Title: In-N-Out: A Parameter-Level API Graph Dataset for Tool Agents
• Authors: Seungkyu Lee, Nalim Kim, Yohan Jo,
• Abstract summary: We introduce In-N-Out, the first expert-annotated dataset of API graphs built from two real-world API benchmarks and their documentation.<n>Using In-N-Out significantly improves performance on both tool retrieval and multi-tool query generation.<n>Our findings highlight the promise of using explicit API graphs for tool agents and the utility of In-N-Out as a valuable resource.
• Score: 12.78469884522289
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tool agents -- LLM-based systems that interact with external APIs -- offer a way to execute real-world tasks. However, as tasks become increasingly complex, these agents struggle to identify and call the correct APIs in the proper order. To tackle this problem, we investigate converting API documentation into a structured API graph that captures API dependencies and leveraging it for multi-tool queries that require compositional API calls. To support this, we introduce In-N-Out, the first expert-annotated dataset of API graphs built from two real-world API benchmarks and their documentation. Using In-N-Out significantly improves performance on both tool retrieval and multi-tool query generation, nearly doubling that of LLMs using documentation alone. Moreover, graphs generated by models fine-tuned on In-N-Out close 90% of this gap, showing that our dataset helps models learn to comprehend API documentation and parameter relationships. Our findings highlight the promise of using explicit API graphs for tool agents and the utility of In-N-Out as a valuable resource. We will release the dataset and code publicly.

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