Chapter D · 4 of 4~11 min

Function calling in code

Parse tool calls from the model, run your functions safely, return results to the chat.

Reviewed July 2026 · OpenAI / Anthropic tool calling

Paths & resources for this lesson

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Reference

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1Try it yourself

Code Lab

Tools in code

Wire function calling: tool name → function → result → reply.

Python

# Tools in code — model chooses a tool name + args
def weather(city):
    return f"Weather in {city}: 28C sunny"

def calculator(expr):
    # tiny safe demo calculator for a few expressions
    allowed = {
        "17*24": "408",
        "17×24": "408",
        "10+5": "15",
        "100/4": "25",
    }
    return allowed.get(expr.replace(" ", ""), "0")

TOOLS = {
  "weather": weather,
  "calculator": calculator,
}

def run_tool(name, arg):
    return TOOLS[name](arg)

# Pretend the model requested a tool:
call = {"name": "calculator", "arg": "17*24"}
result = run_tool(call["name"], call["arg"])
print("tool result:", result)
print("final reply: The answer is", result)

# Challenge: change the call to weather for Dubai

2Read & reflect

Recap

#What you just did

You traced model → tool name + args → your function → final natural-language reply.

Teach

#How it works

Register tools with JSON schemas
Model returns {name, arguments}
Validate args → call allowlisted function
Send {role: "tool", content: result} back
Model writes user-facing answer

Same pattern across OpenAI, Anthropic, and others (names vary).

Use it

#When you'd use this

Calculators, CRM lookups, booking, internal APIs
Anything live data or actions

Watch out

#Watch out

Never expose destructive tools without confirmation. Validate every argument.

Try next

#Try this next

How-to: add function calling · Concept: structured outputs

3Spark check

Continue learning · glossary & guides

Glossary: function calling · Glossary: structured output

Tool args should be… validated / passed straight to shell
Destructive actions need… human confirm / auto-run