When using a model separately from an agent, it is up to you to execute the requested tool and return the result back to the model for use in subsequent reasoning.
Model Suggestion: The LLM's initial call returns an AIMessage containing the suggestion to use a specific tool (the tool_calls object).
Developer Action (Execution): The developer's code must intercept this message, parse the tool name and arguments, and manually execute the corresponding Python function.
Result Feedback: The developer must then package the output of the tool execution into a ToolMessage and send it back to the Model, along with the previous conversation history, for the Model to complete its final reasoning and generate the answer.