mlsanityData to 7 Oct 2026

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

arXiv 2407.04997 ↗First posted 6 Jul 2024cs.SE cs.AI cs.HCcited by 6

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.

Closest ancestors · 2

Same direction, newest first

  1. 2024Granite-Function Calling Model: Introducing Function Calling Abilities via Multi-task Learning of Granular Tasks27 Jun
  2. 2023ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs31 Jul

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 2.

6 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2025Unified Tool Integration for LLMs: A Protocol-Agnostic Approach to Function Calling
  2. 2023CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsets
  3. 2026Harness Engineering in LLM Tool Use via Agent-Native Reusable Tool Primitives
  4. 2023On the Tool Manipulation Capability of Open-source Large Language Models
  5. 2024LLM With Tools: A Survey
  6. 2024Tool Learning with Large Language Models: A Survey
  7. 2025Tool Calling for Arabic LLMs: Data Strategies and Instruction Tuning
  8. 2024Conveyor: Efficient Tool-aware LLM Serving with Tool Partial Execution