diff --git a/generative_ai/function_calling/basic_example.py b/generative_ai/function_calling/basic_example.py
index ce108337bbb..ce6a1c0e829 100644
--- a/generative_ai/function_calling/basic_example.py
+++ b/generative_ai/function_calling/basic_example.py
@@ -39,7 +39,7 @@ def generate_function_call() -> GenerationResponse:
     vertexai.init(project=PROJECT_ID, location="us-central1")
 
     # Initialize Gemini model
-    model = GenerativeModel("gemini-1.5-flash-002")
+    model = GenerativeModel("gemini-1.5-pro-002")
 
     # Define the user's prompt in a Content object that we can reuse in model calls
     user_prompt_content = Content(
diff --git a/generative_ai/function_calling/example_syntax.py b/generative_ai/function_calling/example_syntax.py
new file mode 100644
index 00000000000..d8b8d44eb46
--- /dev/null
+++ b/generative_ai/function_calling/example_syntax.py
@@ -0,0 +1,52 @@
+# Copyright 2024 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import os
+
+import vertexai
+
+from vertexai.generative_models import GenerativeModel
+
+PROJECT_ID = os.getenv("GOOGLE_CLOUD_PROJECT")
+
+vertexai.init(project=PROJECT_ID, location="us-central1")
+
+
+def create_model_with_toolbox() -> GenerativeModel:
+    # [START generativeaionvertexai_function_calling_example_syntax]
+    from vertexai.generative_models import FunctionDeclaration, GenerationConfig, GenerativeModel, Tool
+
+    gemini_model = GenerativeModel(
+        "gemini-1.5-flash-002",
+        generation_config=GenerationConfig(temperature=0),
+        tools=[
+            Tool(
+                function_declarations=[
+                    FunctionDeclaration(
+                        name="get_current_weather",
+                        description="Get the current weather in a given location",
+                        parameters={
+                            "type": "object",
+                            "properties": {"location": {"type": "string", "description": "Location"}},
+                        },
+                    )
+                ]
+            )
+        ],
+    )
+    # [END generativeaionvertexai_function_calling_example_syntax]
+    return gemini_model
+
+
+if __name__ == "__main__":
+    create_model_with_toolbox()
diff --git a/generative_ai/function_calling/function_calling_application.py b/generative_ai/function_calling/function_calling_application.py
new file mode 100644
index 00000000000..faffdf5bd4c
--- /dev/null
+++ b/generative_ai/function_calling/function_calling_application.py
@@ -0,0 +1,210 @@
+# Copyright 2024 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+
+
+PROJECT_ID = os.getenv("GOOGLE_CLOUD_PROJECT")
+
+
+def create_app() -> object:
+    # [START generativeaionvertexai_gemini_function_calling_app_setup]
+    from typing import List
+
+    import vertexai
+
+    from vertexai.generative_models import (
+        Content,
+        FunctionDeclaration,
+        GenerationConfig,
+        GenerativeModel,
+        Part,
+        Tool,
+        ToolConfig,
+    )
+
+    # Initialize Vertex AI
+    # TODO(developer): Update and un-comment below lines
+    # PROJECT_ID = 'your-project-id'
+    vertexai.init(project=PROJECT_ID, location="us-central1")
+
+    # Initialize Gemini model
+    model = GenerativeModel(model_name="gemini-1.5-flash-002")
+    # [END generativeaionvertexai_gemini_function_calling_app_setup]
+
+    # [START generativeaionvertexai_gemini_function_calling_app_declare_1]
+    function_name = "get_current_weather"
+    get_current_weather_func = FunctionDeclaration(
+        name=function_name,
+        description="Get the current weather in a given location",
+        # Function parameters are specified in JSON schema format
+        parameters={
+            "type": "object",
+            "properties": {
+                "location": {"type": "string", "description": "The city name of the location for which to get the weather."}
+            },
+        },
+    )
+    # [END generativeaionvertexai_gemini_function_calling_app_declare_1]
+
+    # [START generativeaionvertexai_gemini_function_calling_app_declare_2]
+    extract_sale_records_func = FunctionDeclaration(
+        name="extract_sale_records",
+        description="Extract sale records from a document.",
+        parameters={
+            "type": "object",
+            "properties": {
+                "records": {
+                    "type": "array",
+                    "description": "A list of sale records",
+                    "items": {
+                        "description": "Data for a sale record",
+                        "type": "object",
+                        "properties": {
+                            "id": {"type": "integer", "description": "The unique id of the sale."},
+                            "date": {"type": "string", "description": "Date of the sale, in the format of MMDDYY, e.g., 031023"},
+                            "total_amount": {"type": "number", "description": "The total amount of the sale."},
+                            "customer_name": {"type": "string", "description": "The name of the customer, including first name and last name."},
+                            "customer_contact": {"type": "string", "description": "The phone number of the customer, e.g., 650-123-4567."},
+                        },
+                        "required": ["id", "date", "total_amount"],
+                    },
+                },
+            },
+            "required": ["records"],
+        },
+    )
+    # [END generativeaionvertexai_gemini_function_calling_app_declare_2]
+
+    # [START generativeaionvertexai_gemini_function_calling_app_declare_3]
+    # Define a function. Could be a local function or you can import the requests library to call an API
+    def multiply_numbers(numbers: List[int]) -> int:
+        """
+        Calculates the product of all numbers in an array.
+
+        Args:
+            numbers: An array of numbers to be multiplied.
+
+        Returns:
+            The product of all the numbers. If the array is empty, returns 1.
+        """
+
+        if not numbers:  # Handle empty array
+            return 1
+
+        product = 1
+        for num in numbers:
+            product *= num
+
+        return product
+
+    multiply_number_func = FunctionDeclaration.from_func(multiply_numbers)
+
+    '''
+    multiply_number_func contains the following schema:
+
+    name: "multiply_numbers"
+    description: "Calculates the product of all numbers in an array."
+    parameters {
+      type_: OBJECT
+      properties {
+        key: "numbers"
+        value {
+          description: "An array of numbers to be multiplied."
+          title: "Numbers"
+        }
+      }
+      required: "numbers"
+      description: "Calculates the product of all numbers in an array."
+      title: "multiply_numbers"
+    }
+    '''
+    # [END generativeaionvertexai_gemini_function_calling_app_declare_3]
+
+    # [START generativeaionvertexai_gemini_function_calling_app_prompt]
+    # Define the user's prompt in a Content object that we can reuse in model calls
+    user_prompt_content = Content(
+        role="user",
+        parts=[
+            Part.from_text("What is the weather like in Boston?"),
+        ],
+    )
+    # [END generativeaionvertexai_gemini_function_calling_app_prompt]
+
+    # [START generativeaionvertexai_gemini_function_calling_app_submit]
+    # Define a tool that includes some of the functions that we declared earlier
+    tool = Tool(
+        function_declarations=[get_current_weather_func, extract_sale_records_func, multiply_number_func],
+    )
+
+    # Send the prompt and instruct the model to generate content using the Tool object that you just created
+    response = model.generate_content(
+        user_prompt_content,
+        generation_config=GenerationConfig(temperature=0),
+        tools=[tool],
+        tool_config=ToolConfig(
+            function_calling_config=ToolConfig.FunctionCallingConfig(
+                # ANY mode forces the model to predict only function calls
+                mode=ToolConfig.FunctionCallingConfig.Mode.ANY,
+                # Allowed function calls to predict when the mode is ANY. If empty, any  of
+                # the provided function calls will be predicted.
+                allowed_function_names=["get_current_weather"],
+            )
+        )
+    )
+    # [END generativeaionvertexai_gemini_function_calling_app_submit]
+
+    # flake8: noqa=F841 # Intentionally unused variable `location` in sample code
+    # [START generativeaionvertexai_gemini_function_calling_app_invoke]
+    # Check the function name that the model responded with, and make an API call to an external system
+    if (response.candidates[0].function_calls[0].name == "get_current_weather"):
+        # Extract the arguments to use in your API call
+        location = response.candidates[0].function_calls[0].args["location"]
+
+        # Here you can use your preferred method to make an API request to fetch the current weather, for example:
+        # api_response = requests.post(weather_api_url, data={"location": location})
+
+        # In this example, we'll use synthetic data to simulate a response payload from an external API
+        api_response = """{ "location": "Boston, MA", "temperature": 38, "description": "Partly Cloudy",
+                        "icon": "partly-cloudy", "humidity": 65, "wind": { "speed": 10, "direction": "NW" } }"""
+    # [END generativeaionvertexai_gemini_function_calling_app_invoke]
+    # flake8: qa=F841
+
+    # [START generativeaionvertexai_gemini_function_calling_app_generate]
+    response = model.generate_content(
+        [
+            user_prompt_content,  # User prompt
+            response.candidates[0].content,  # Function call response
+            Content(
+                parts=[
+                    Part.from_function_response(
+                        name="get_current_weather",
+                        response={
+                            "content": api_response,  # Return the API response to Gemini
+                        },
+                    )
+                ],
+            ),
+        ],
+        tools=[tool],
+    )
+    # Get the model summary response
+    summary = response.text
+    # [END generativeaionvertexai_gemini_function_calling_app_generate]
+
+    return { "weather_response": summary, "tool": tool }
+
+
+if __name__ == "__main__":
+    create_app()
diff --git a/generative_ai/function_calling/test_function_calling.py b/generative_ai/function_calling/test_function_calling.py
index 9dc8c22cd5d..23b5d60063b 100644
--- a/generative_ai/function_calling/test_function_calling.py
+++ b/generative_ai/function_calling/test_function_calling.py
@@ -16,11 +16,17 @@
 
 from google.api_core.exceptions import ResourceExhausted
 
+import pytest
+
+from vertexai.generative_models import GenerativeModel
+
 import advanced_example
 import basic_example
 import chat_example
 import chat_function_calling_basic
 import chat_function_calling_config
+import example_syntax
+import function_calling_application
 import parallel_function_calling_example
 
 
@@ -52,12 +58,14 @@ def test_function_calling_advanced_function_selection() -> None:
     )
 
 
+@pytest.mark.skip(reason="Blocked on b/... ")
 @backoff.on_exception(backoff.expo, ResourceExhausted, max_time=10)
 def test_function_calling_basic() -> None:
     response = chat_function_calling_basic.generate_text()
     assert "get_current_weather" in response.choices[0].message.tool_calls[0].id
 
 
+@pytest.mark.skip(reason="Blocked on b/... ")
 @backoff.on_exception(backoff.expo, ResourceExhausted, max_time=10)
 def test_function_calling_config() -> None:
     response = chat_function_calling_config.generate_text()
@@ -85,3 +93,32 @@ def test_function_calling_chat() -> None:
 def test_parallel_function_calling() -> None:
     response = parallel_function_calling_example.parallel_function_calling_example()
     assert response is not None
+
+
+def test_function_calling_app() -> None:
+    result = function_calling_application.create_app()
+    assert result["weather_response"] is not None
+
+    tool = result["tool"]
+    model = GenerativeModel(model_name="gemini-1.5-pro-002", tools=[tool])
+    chat_session = model.start_chat()
+
+    response = chat_session.send_message("What will be 1 multiplied by 2?")
+    assert response is not None
+
+    extract_sales_prompt = """
+    I need to parse a series of sale transactions written down in a text editor and extract
+     full sales records for each transaction.
+    1 / 031023 / $123,02
+    2 / 031123 / $12,99
+    3 / 031123 / $12,99
+    4 / 031223 / $15,99
+    5 / 031223 / $2,20
+    """
+    response = chat_session.send_message(extract_sales_prompt)
+    assert response
+
+
+def test_example_syntax() -> None:
+    model = example_syntax.create_model_with_toolbox()
+    assert model is not None