Achieving Tool Calling Functionality in LLMs Using Only Prompt Engineering Without Fine-Tuning
On the map: LLM agents and tool use → Tool learning and function calling
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%.
Key facts. Cited by 6 papers in the map (data through 7 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.
Builds on
2 of 6 references.
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Citing papers per year: 2025: 4; 2026: 2.
6 papers.
- 2025A Survey of Context Engineering for Large Language Modelscited by 120
- 2025Agent WARPP: Workflow Adherence via Runtime Parallel Personalizationcited by 3
- 2026CaveAgent: Transforming LLMs into Stateful Runtime Operatorscited by 2
- 2025MRT at IberLEF-2025 PRESTA Task: Maximizing Recovery from Tables with Multiple Stepscited by 0
- 2025CienaLLM: Generative Climate-Impact Extraction from News Articles with Autoregressive LLMscited by 0
- 2026Small Reasoning Models are Instruction Followers in Function Callingcited by 0
- 2026Small Reasoning Models are Instruction Followers in Function Calling23 Aug 2026
- 2026CaveAgent: Transforming LLMs into Stateful Runtime Operators4 Jan 2026
- 2025CienaLLM: Generative Climate-Impact Extraction from News Articles with Autoregressive LLMs22 Dec 2025
- 2025Agent WARPP: Workflow Adherence via Runtime Parallel Personalization23 Jul 2025
- 2025A Survey of Context Engineering for Large Language Models17 Jul 2025
- 2025MRT at IberLEF-2025 PRESTA Task: Maximizing Recovery from Tables with Multiple Steps17 Jul 2025
Similar, not cited
Close in wording, no citation either way
- 2025Unified Tool Integration for LLMs: A Protocol-Agnostic Approach to Function Calling
- 2023CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsets
- 2026Harness Engineering in LLM Tool Use via Agent-Native Reusable Tool Primitives
- 2023On the Tool Manipulation Capability of Open-source Large Language Models
- 2024LLM With Tools: A Survey
- 2024Tool Learning with Large Language Models: A Survey
- 2025Tool Calling for Arabic LLMs: Data Strategies and Instruction Tuning
- 2024Conveyor: Efficient Tool-aware LLM Serving with Tool Partial Execution