Gemini allows the combination of built-in tools, such
as google_search, and function calling
(also known as custom tools) in a single interaction by preserving and exposing
the context history of tool calls. Built-in and custom tool combinations allow
for complex, agentic workflows where, for example, the model can ground itself
in real-time web data before calling your specific business logic.
Here's an example that enables built-in and custom tool combinations with
google_search and a custom function getWeather:
Python
# This will only work for SDK newer than 2.0.0
from google import genai
client = genai.Client()
getWeather = {
"type": "function",
"name": "getWeather",
"description": "Gets the weather for a requested city.",
"parameters": {
"type": "object",
"properties": {
"city": {
"type": "string",
"description": "The city and state, e.g. Utqiaġvik, Alaska",
},
},
"required": ["city"],
},
}
# The Interactions API manages context automatically across tool calls.
# The model will first use Google Search, then call getWeather.
interaction = client.interactions.create(
model="gemini-3.8-flash",
input="What is the northernmost city in the United States? What's the weather like there today?",
tools=[
{"type": "google_search"},
getWeather,
],
)
# Process steps: the interaction contains search results and a function call
for step in interaction.steps:
if step.type == "function_call":
print(f"Function call: {step.name} with args: {step.arguments}")
# In a real application, you would execute the function here
# and provide the result back to the model.
JavaScript
// This will only work for SDK newer than 2.0.0
import { GoogleGenAI } from '@google/genai';
const client = new GoogleGenAI({});
const getWeather = {
type: "function",
name: "getWeather",
description: "Get the weather in a given location",
parameters: {
type: "object",
properties: {
location: {
type: "string",
description: "The city and state, e.g. San Francisco, CA"
}
},
required: