MCP Toolbox for Databases

--- title: "Deploy ADK Agent and MCP Toolbox" type: docs weight: 4 description: > 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)](../getting-started/local_quickstart.md) and have a working ADK agent running locally. 2. Installed the [Google Cloud CLI](https://cloud.google.com/sdk/docs/install). 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](deploy_toolbox.md) 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: ```bash 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: ```bash 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): ```bash gcloud auth application-default login ``` 2. Set your active project: ```bash 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: ```bash 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: ```python 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`). ```python ... app = App(root_agent=root_agent, name="my_agent") ``` {{% /alert %}} ## Step 5: Deploy to Agent Engine Run the deployment command: ```bash 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](https://google.github.io/adk-docs/deploy/agent-engine/#test-deployment) section in the ADK documentation.