How do I enable tool calling in my AI app?
Step 1: Choose a model and API that supports tool calling
First, select an AI model that has tool calling capabilities. Popular options include OpenAI's GPT-4 and GPT-3.5 Turbo, Anthropic's Claude, and Google's Gemini. Many open-source models also support it, but you may need to host them yourself or use a provider.
Check the API documentation for how to enable tool calling. For example, OpenAI uses the 'tools' parameter in chat completions, while Anthropic uses 'tools' in the messages API. The exact syntax varies.
Step 2: Define your tools and make the API call
You need to describe each tool in a JSON schema, including its name, description, and parameters. This tells the model what tools are available and how to use them.
When you send a user message, include the tool definitions in the API request. The model will then decide whether to call a tool and return a structured response with the tool name and arguments.
- Define tools with name, description, and parameter schema.
- Include tools in the API request (e.g., 'tools' parameter).
- Send user message along with tool definitions.
- Model returns a tool call if needed.
Step 3: Handle the tool call and return results
When the model responds with a tool call, your app must execute the corresponding function with the provided arguments. Then, you send the function's output back to the model in a new API call so it can generate a final answer.
This often involves a loop: the model may call multiple tools in sequence. You need to manage the conversation state and ensure the model receives all tool results before producing the final response.
Common mistakes
- Not defining tools clearly, leading the model to misuse or ignore them.
- Forgetting to handle the tool call response and instead expecting the model to directly answer.
- Not validating or sanitizing tool arguments before execution, which can cause errors or security issues.