Tool calling (also called function calling) is how AI models execute actions in the real world. Instead of just generating text, the model outputs a structured request to call a specific function with specific parameters.

How it works

User: "What's the weather in Tokyo?"
    ↓
Model decides to call: get_weather(city="Tokyo")
    ↓
Your code executes the function
    ↓
Result: {temp: 22, condition: "sunny"}
    ↓
Model: "It's 22°C and sunny in Tokyo."

The model doesn’t execute the function itself — it tells YOUR code what to call. You execute it and return the result.

Why it matters

Tool calling is the foundation of:

• MCP — the standard protocol for AI tool integration
• AI agents — agents that read files, run tests, deploy code
• RAG — retrieving documents before generating answers
• AI coding tools — Claude Code, Aider, Cursor

Example (OpenAI format)

response = client.chat.completions.create(
    model="gpt-5.4",
    messages=[{"role": "user", "content": "Check if my server is up"}],
    tools=[{
        "type": "function",
        "function": {
            "name": "check_server",
            "parameters": {
                "type": "object",
                "properties": {"url": {"type": "string"}},
                "required": ["url"]
            }
        }
    }]
)

Learn more

• Tool Calling Patterns — sequential, parallel, conditional, recursive
• MCP Complete Guide — the standardized protocol for tool integration
• How to Build an AI Agent — agents that use tools autonomously
• What is Prompt Engineering? — crafting instructions for AI

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