How do I get started with function calling?
Step 1: Choose a provider and set up
Most major LLM APIs support function calling, though the exact syntax varies. OpenAI uses a `tools` parameter with `type: "function"`, Anthropic uses `tools` with `input_schema`, and Google Gemini uses `function_declarations`. Pick one and read its quickstart guide.
You'll need an API key and a development environment. Start with a simple script in Python or Node.js using the provider's official SDK. The SDK handles the request/response formatting so you can focus on defining functions.
- OpenAI: tools parameter, JSON Schema for parameters
- Anthropic: tools parameter, input_schema similar to JSON Schema
- Google Gemini: function_declarations in the request
- All require you to describe the function name, purpose, and parameters
Step 2: Define and test a simple function
Start with a trivial function like `get_current_weather(location, unit)`. Define its name, description, and parameters in the format your provider expects. The description matters: the model uses it to decide when to call the function.
Send a user message that should trigger the function, such as "What's the weather in Boston?" The model should respond with a function call instead of a text answer. You then execute the function in your code and send the result back to the model to get a final natural-language response.
Test edge cases: missing parameters, invalid values, and ambiguous requests. The model may ask for clarification or hallucinate arguments if your schema is unclear.
- Define function name, description, and parameters
- Use clear, specific descriptions to guide the model
- Execute the function and return results in the expected format
- Handle errors gracefully and inform the model
Step 3: Expand and integrate
Once you have one function working, add more. Group related functions into a single toolset. For example, a weather tool might include `get_forecast`, `get_historical`, and `get_alerts`.
Integrate function calling into your application logic. You'll need to parse the model's response, call your actual API or database, and format the result. Many providers support parallel function calls, where the model requests multiple functions at once.
Consider using an orchestration library or framework (like LangChain, LlamaIndex, or the provider's own agents SDK) to manage conversation state and tool execution. But for simple use cases, direct API calls are fine.
- Add multiple functions and test combined scenarios
- Use parallel calls to speed up independent operations
- Store conversation history to maintain context
- Monitor token usage and cost as you scale
Common mistakes
- Thinking function calling is a separate model; it's just a structured output mode of the same LLM.
- Writing vague function descriptions, which leads the model to call the wrong function or none at all.
- Forgetting to send the function result back to the model, so you never get a final answer.