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Unified Tool Integration for LLMs: A Protocol-Agnostic Approach to Function Calling

Artificial Intelligence 2025-08-06 v1 Computation and Language Machine Learning

Abstract

The proliferation of tool-augmented Large Language Models (LLMs) has created a fragmented ecosystem where developers must navigate multiple protocols, manual schema definitions, and complex execution workflows. We address this challenge by proposing a unified approach to tool integration that abstracts protocol differences while optimizing execution performance. Our solution demonstrates how protocol-agnostic design principles can significantly reduce development overhead through automated schema generation, dual-mode concurrent execution, and seamless multi-source tool management. Experimental results show 60-80% code reduction across integration scenarios, performance improvements up to 3.1x through optimized concurrency, and full compatibility with existing function calling standards. This work contributes both theoretical insights into tool integration architecture and practical solutions for real-world LLM application development.

Keywords

Cite

@article{arxiv.2508.02979,
  title  = {Unified Tool Integration for LLMs: A Protocol-Agnostic Approach to Function Calling},
  author = {Peng Ding and Rick Stevens},
  journal= {arXiv preprint arXiv:2508.02979},
  year   = {2025}
}

Comments

arXiv admin note: substantial text overlap with arXiv:2507.10593

R2 v1 2026-07-01T04:34:21.169Z