---
dataset_info:
  features:
  - name: prompt
    dtype: string
  - name: tool
    dtype: string
  - name: args
    struct:
    - name: a
      dtype: float64
    - name: b
      dtype: float64
  splits:
  - name: train
    num_bytes: 2617582
    num_examples: 20000
  download_size: 999433
  dataset_size: 2617582
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
tags:
- function-calling
- agent
- synthetic
- mathematics
- nlp
---

# Simple Calculator: Agentic Function-Calling Dataset

## Overview
This dataset contains **20,000 highly structured, synthetically generated examples** designed to train Large Language Models (LLMs) in agentic tool-calling and parameter extraction. It focuses on mapping diverse natural language mathematical queries—ranging from direct commands to conversational word problems—into deterministic, executable JSON structures for a calculator tool.

## Dataset Structure
The dataset trains models to isolate numerical entities and identify the correct mathematical operation required to solve the user's prompt.

### Schema
* **`prompt`** *(string)*: The natural language input (e.g., "If I have a 200 bill and I want to split it with 3 friends").
* **`tool`** *(string)*: The target target routing function (`add`, `subtract`, `multiply`, or `divide`).
* **`args`** *(struct)*: The extracted numerical parameters.
  * **`a`** *(float64)*: The first numerical operand.
  * **`b`** *(float64)*: The second numerical operand.

### Data Example
```json
{
  "prompt": "A factory produces 45.5 items per hour. How many items does it make in 8 hours?",
  "tool": "multiply",
  "args": {
    "a": 45.5,
    "b": 8.0
  }
}