# Achieving Tool Calling Functionality in LLMs Using Only Prompt Engineering Without Fine-Tuning

arXiv: https://arxiv.org/abs/2407.04997 · HTML: https://mlsanity.com/paper/2407.04997/

## Key facts

- Cited by 6 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 2 of its 6 references that are in the map.
- First posted on arXiv 6 Jul 2024.

## Placed in

- LLM agents and tool use / [Tool learning and function calling](https://mlsanity.com/agents/tool-learning-and-function-calling/)

## Builds on (most cited first, 2 of 2)

- [ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs](https://mlsanity.com/paper/2307.16789/) (2023)
- [Granite-Function Calling Model: Introducing Function Calling Abilities via Multi-task Learning of Granular Tasks](https://mlsanity.com/paper/2407.00121/) (2024)

## Built on by (most cited first, 6 of 6)

- [A Survey of Context Engineering for Large Language Models](https://mlsanity.com/paper/2507.13334/) (2025)
- [Agent WARPP: Workflow Adherence via Runtime Parallel Personalization](https://mlsanity.com/paper/2507.19543/) (2025)
- [CaveAgent: Transforming LLMs into Stateful Runtime Operators](https://mlsanity.com/paper/2601.01569/) (2026)
- [MRT at IberLEF-2025 PRESTA Task: Maximizing Recovery from Tables with Multiple Steps](https://mlsanity.com/paper/2507.12981/) (2025)
- [CienaLLM: Generative Climate-Impact Extraction from News Articles with Autoregressive LLMs](https://mlsanity.com/paper/2512.19305/) (2025)
- [Small Reasoning Models are Instruction Followers in Function Calling](https://mlsanity.com/paper/2608.22472/) (2026)

## Abstract (arXiv)

Currently, the vast majority of locally deployed open-source large language models (LLMs) and some commercial model interfaces do not support stable tool calling functionality. The existing solution involves fine-tuning LLMs, which results in significant time and computational resource consumption. This paper proposes a method that enables LLMs to achieve stable tool calling capabilities using only prompt engineering and some ingenious code design. We conducted experiments on multiple LLMs that lack tool calling capabilities across various tool calling tasks, achieving a success rate of 100%.