Automated Creation and Enrichment Framework for Improved Invocation of Enterprise APIs as Tools

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Authors: Association for Artificial Intelligence 2026, Prerna Agarwal, Himanshu Gupta, Sameep Mehta, Renuka Sindhgatta, Soujanya Soni, Rohith Dwarakanath Vallam
Cited by: 0

Abstract

Recent advancements in Large Language Models (LLMs) has lead to the development of agents capable of complex reasoning and interaction with external tools. In enterprise contexts, the effective use of such tools that are often enabled by application programming interfaces (APIs) is hindered by poor documentation, complex input or output schema, and large number of operations. These challenges make tool selection difficult and reduce the accuracy of payload formation upto 25%. We propose ACE, an automated tool creation and enrichment framework that transforms enterprise APIs into LLM-compatible tools. ACE (i) generates enriched tool specifications with parameter descriptions and examples to improve selection and invocation accuracy, and (ii) incorporates a dynamic shortlisting mechanism that filters relevant tools at runtime, reducing prompt complexity while maintaining scalability. We validate our framework on both proprietary and open-source APIs and demonstrate its integration with agentic frameworks. To the best of our knowledge, ACE is the first end-to-end framework that automates the creation, enrichment, and dynamic selection of enterprise API tools for LLM agents.

Fields of Study & Subjects

Artificial Intelligence Applications Software Engineering Research Service-Oriented Architecture and Web Services

Publication details

Journal / source
Underline Science Inc.
Publication date
7 January 2026
Language
EN