How tool calls work under the hood

simple_tool_call.py

#!/usr/bin/env -S uv run

# /// script
# dependencies = ["openai"]
# ///


# repurposed from https://github.com/willccbb/agent-engineering/blob/main/lectures-1-through-4/lec1-agent-patterns/agent_patterns.ipynb
import json
from openai import OpenAI

client = OpenAI()

# The system message tells the model how to call the tool.
system_prompt = """
You can call a weather tool.
Return JSON like this:
{"tool": "weather", "args": {"city": "Tokyo", "country": "Japan", "scale": "celsius"}}
"""

# A tiny fake tool to demonstrate what happens after the model calls it.
def weather(city, country, scale):
    return f"The weather in {city}, {country} is 20° {scale}."

# Ask the model to decide what tool to call.
response = client.chat.completions.create(
    model="gpt-4.1-mini",
    messages=[
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": "What's the weather in Tokyo?"}
    ]
)

# The model’s text reply is expected to be JSON.
tool_call = json.loads(response.choices[0].message.content)

# Run the chosen tool with its arguments.
result = weather(
    city=tool_call["args"]["city"],
    country=tool_call["args"]["country"],
    scale=tool_call["args"]["scale"]
)

print(result)