How do I connect tools to an AI model?
Defining tools for the model
Tools are typically defined as JSON schemas that describe the function name, parameters, and purpose. The AI model uses this description to decide when and how to call the tool.
For example, a weather tool might be defined with a `get_weather` function that takes a `location` parameter. The model can then generate a call to this function when the user asks about weather.
Most major AI providers, such as OpenAI, Anthropic, and Google, support function calling or tool use in their APIs. The exact format varies but generally follows a similar pattern.
- Write a clear, concise description of what the tool does.
- Define parameters with types and descriptions.
- Specify which parameters are required.
- Provide examples if the API supports it.
- Test the tool definitions with sample queries.
Integrating with the AI model
Once tools are defined, you pass them to the model in the API request. The model may respond with a tool call request instead of a direct answer.
Your application then executes the tool call, returns the result to the model, and the model continues the conversation with that information.
This loop allows the AI to access real-time data, perform actions, or retrieve information beyond its training data.
Common mistakes
- Providing vague tool descriptions, causing the model to misuse them.
- Not handling tool call errors gracefully, leading to broken conversations.
- Assuming all models support tool calling; check compatibility first.