genai-toolbox/docs/en/how-to/deploy_adk_agent.md

title, type, weight, description

title	type	weight	description
Deploy ADK Agent and MCP Toolbox	docs	4	How to deploy your ADK Agent to Vertex AI Agent Engine and connect it to an MCP Toolbox deployed on Cloud Run.

Before you begin

This guide assumes you have already done the following:

1. Completed the Python Quickstart (Local) and have a working ADK agent running locally.
2. Installed the Google Cloud CLI.
3. A Google Cloud project with billing enabled.

Step 1: Deploy MCP Toolbox to Cloud Run

Before deploying your agent, your MCP Toolbox server needs to be accessible from the cloud. We will deploy MCP Toolbox to Cloud Run.

Follow the Deploy to Cloud Run guide to deploy your MCP Toolbox instance.

{{% alert title="Important" %}} After deployment, note down the Service URL of your MCP Toolbox Cloud Run service. You will need this to configure your agent. {{% /alert %}}

Step 2: Prepare your Agent for Deployment

We will use the agent-starter-pack tool to enhance your local agent project with the necessary configuration for deployment to Vertex AI Agent Engine.

1. Open a terminal and navigate to the parent directory of your agent project (the directory containing the my_agent folder).

2. Run the following command to enhance your project:

   uvx agent-starter-pack enhance --adk -d agent_engine
   

3. Follow the interactive prompts to configure your deployment settings. This process will generate deployment configuration files (like a Makefile and Dockerfile) in your project directory.

4. Add toolbox-core as a dependency to the new project:

   uv add toolbox-core
   

Step 3: Configure Google Cloud Authentication

Ensure your local environment is authenticated with Google Cloud to perform the deployment.

1. Login with Application Default Credentials (ADC):

   gcloud auth application-default login
   

2. Set your active project:

   gcloud config set project <YOUR_PROJECT_ID>
   

Step 4: Connect Agent to Deployed MCP Toolbox

You need to update your agent's code to connect to the Cloud Run URL of your MCP Toolbox instead of the local address.

1. Recall that you can find the Cloud Run deployment URL of the MCP Toolbox server using the following command:

   gcloud run services describe toolbox --format 'value(status.url)'
   

2. Open your agent file (my_agent/agent.py).

3. Update the ToolboxSyncClient initialization to use your Cloud Run URL.

   {{% alert color="info" %}} Since Cloud Run services are secured by default, you also need to provide an authentication token. {{% /alert %}}

   Replace your existing client initialization code with the following:

   from google.adk import Agent
   from google.adk.apps import App
   from toolbox_core import ToolboxSyncClient, auth_methods
   
   # TODO(developer): Replace with your Toolbox Cloud Run Service URL
   TOOLBOX_URL = "https://your-toolbox-service-xyz.a.run.app"
   
   # Initialize the client with the Cloud Run URL and Auth headers
   client = ToolboxSyncClient(
       TOOLBOX_URL, 
       client_headers={"Authorization": auth_methods.get_google_id_token(TOOLBOX_URL)}
   )
   
   root_agent = Agent(
       name='root_agent',
       model='gemini-2.5-flash',
       instruction="You are a helpful AI assistant designed to provide accurate and useful information.",
       tools=client.load_toolset(),
   )
   
   app = App(root_agent=root_agent, name="my_agent")
   

   {{% alert title="Important" %}} Ensure that the name parameter in the App initialization matches the name of your agent's parent directory (e.g., my_agent).

...

app = App(root_agent=root_agent, name="my_agent")

{{% /alert %}}

Step 5: Deploy to Agent Engine

Run the deployment command:

make backend

This command will build your agent's container image and deploy it to Vertex AI.

Step 6: Test your Deployment

Once the deployment command (make backend) completes, it will output the URL for the Agent Engine Playground. You can click on this URL to open the Playground in your browser and start chatting with your agent to test the tools.

For additional test scenarios, refer to the Test deployed agent section in the ADK documentation.