Tips on how to Mix Google Search, Google Maps, and Customized Features in a Single Gemini API Name With Context Circulation, Parallel Instrument IDs, and Multi-Step Agentic Chains

import subprocess, sys


subprocess.check_call(
   [sys.executable, "-m", "pip", "install", "-qU", "google-genai"],
   stdout=subprocess.DEVNULL,
   stderr=subprocess.DEVNULL,
)


import getpass, json, textwrap, os, time
from google import genai
from google.genai import sorts


if "GOOGLE_API_KEY" not in os.environ:
   os.environ["GOOGLE_API_KEY"] = getpass.getpass("Enter your Gemini API key: ")


consumer = genai.Shopper(api_key=os.environ["GOOGLE_API_KEY"])


TOOL_COMBO_MODEL = "gemini-3-flash-preview"
MAPS_MODEL       = "gemini-2.5-flash"


DIVIDER = "=" * 72


def heading(title: str):
   print(f"n{DIVIDER}")
   print(f"  {title}")
   print(DIVIDER)


def wrap(textual content: str, width: int = 80):
   for line in textual content.splitlines():
       print(textwrap.fill(line, width=width) if line.strip() else "")


def describe_parts(response):
   components = response.candidates[0].content material.components
   fc_ids = {}
   for i, half in enumerate(components):
       prefix = f"   Half {i:second}:"
       if hasattr(half, "tool_call") and half.tool_call:
           tc = half.tool_call
           print(f"{prefix} [toolCall]        kind={tc.tool_type}  id={tc.id}")
       if hasattr(half, "tool_response") and half.tool_response:
           tr = half.tool_response
           print(f"{prefix} [toolResponse]    kind={tr.tool_type}  id={tr.id}")
       if hasattr(half, "executable_code") and half.executable_code:
           code = half.executable_code.code[:90].exchange("n", " ↵ ")
           print(f"{prefix} [executableCode]  {code}...")
       if hasattr(half, "code_execution_result") and half.code_execution_result:
           out = (half.code_execution_result.output or "")[:90]
           print(f"{prefix} [codeExecResult]  {out}")
       if hasattr(half, "function_call") and half.function_call:
           fc = half.function_call
           fc_ids[fc.name] = fc.id
           print(f"{prefix} [functionCall]    identify={fc.identify}  id={fc.id}")
           print(f"              └─ args: {dict(fc.args)}")
       if hasattr(half, "textual content") and half.textual content:
           snippet = half.textual content[:110].exchange("n", " ")
           print(f"{prefix} [text]            {snippet}...")
       if hasattr(half, "thought_signature") and half.thought_signature:
           print(f"              └─ thought_signature current ✓")
   return fc_ids




heading("DEMO 1: Mix Google Search + Customized Operate in One Request")


print("""
This demo exhibits the flagship new function: passing BOTH a built-in software
(Google Search) and a customized operate declaration in a single API name.


Gemini will:
 Flip 1 → Search the online for real-time data, then request our customized
          operate to get climate knowledge.
 Flip 2 → We provide the operate response; Gemini synthesizes every thing.


Key factors:
 • google_search and function_declarations go within the SAME Instrument object
 • include_server_side_tool_invocations have to be True (on ToolConfig)
 • Return ALL components (incl. thought_signatures) in subsequent turns
""")


get_weather_func = sorts.FunctionDeclaration(
   identify="getWeather",
   description="Will get the present climate for a requested metropolis.",
   parameters=sorts.Schema(
       kind="OBJECT",
       properties={
           "metropolis": sorts.Schema(
               kind="STRING",
               description="Town and state, e.g. Utqiagvik, Alaska",
           ),
       },
       required=["city"],
   ),
)


print("▶  Flip 1: Sending immediate with Google Search + getWeather instruments...n")


response_1 = consumer.fashions.generate_content(
   mannequin=TOOL_COMBO_MODEL,
   contents=(
       "What's the northernmost metropolis in the USA? "
       "What is the climate like there at present?"
   ),
   config=sorts.GenerateContentConfig(
       instruments=[
           types.Tool(
               google_search=types.GoogleSearch(),
               function_declarations=[get_weather_func],
           ),
       ],
       tool_config=sorts.ToolConfig(
           include_server_side_tool_invocations=True,
       ),
   ),
)


print("   Elements returned by the mannequin:n")
fc_ids = describe_parts(response_1)


function_call_id = fc_ids.get("getWeather")
print(f"n   ✅ Captured function_call id for getWeather: {function_call_id}")


print("n▶  Flip 2: Returning operate consequence & requesting ultimate synthesis...n")


historical past = [
   types.Content(
       role="user",
       parts=[
           types.Part(
               text=(
                   "What is the northernmost city in the United States? "
                   "What's the weather like there today?"
               )
           )
       ],
   ),
   response_1.candidates[0].content material,
   sorts.Content material(
       position="person",
       components=[
           types.Part(
               function_response=types.FunctionResponse(
                   name="getWeather",
                   response={"response": "Very cold. 22°F / -5.5°C with strong Arctic winds."},
                   id=function_call_id,
               )
           )
       ],
   ),
]


response_2 = consumer.fashions.generate_content(
   mannequin=TOOL_COMBO_MODEL,
   contents=historical past,
   config=sorts.GenerateContentConfig(
       instruments=[
           types.Tool(
               google_search=types.GoogleSearch(),
               function_declarations=[get_weather_func],
           ),
       ],
       tool_config=sorts.ToolConfig(
           include_server_side_tool_invocations=True,
       ),
   ),
)


print("   ✅ Closing synthesized response:n")
for half in response_2.candidates[0].content material.components:
   if hasattr(half, "textual content") and half.textual content:
       wrap(half.textual content)