mlsanityData to 14 Oct 2026

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

arXiv 2508.02979 ↗First posted 5 Aug 2025cs.AI cs.CL cs.LGcited by 3

On the map: LLM agents and tool use → Tool learning and function calling

Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in Tool learning and function calling, one of 16 directions in LLM agents and tool use. Builds on 8 of its 16 references that are in the map. First posted on arXiv 5 Aug 2025.

Builds on

8 of 16 references.

Closest ancestors · 5

Same direction, newest first

  1. 2024Tool Learning with Large Language Models: A Survey28 May
  2. 2024Tool Learning in the Wild: Empowering Language Models as Automatic Tool Agents26 May
  3. 2023ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs31 Jul
  4. 2023Gorilla: Large Language Model Connected with Massive APIs24 May
  5. 2023Toolformer: Language Models Can Teach Themselves to Use Tools9 Feb

Other references · 3

  1. 2023HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Facecited by 1,190
  2. 2025A survey of agent interoperability protocols: Model Context Protocol (MCP), Agent Communication Protocol (ACP), Agent-to-Agent Protocol (A2A), and Agent Network Protocol (ANP)cited by 101
  3. 2025A Survey of AI Agent Protocolscited by 81

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026Harness Engineering in LLM Tool Use via Agent-Native Reusable Tool Primitives
  2. 2024Achieving Tool Calling Functionality in LLMs Using Only Prompt Engineering Without Fine-Tuning
  3. 2023CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsets
  4. 2026Schema First Tool APIs for LLM Agents: A Controlled Study of Tool Misuse, Recovery, and Budgeted Performance
  5. 2024Less is More: Optimizing Function Calling for LLM Execution on Edge Devices
  6. 2026The Evolution of Tool Use in LLM Agents: From Single-Tool Call to Multi-Tool Orchestration
  7. 2024An LLM-Tool Compiler for Fused Parallel Function Calling
  8. 2025ToolRegistry: A Protocol-Agnostic Tool Management Library for Function-Calling LLMs