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147 lines
4.6 KiB
Markdown
147 lines
4.6 KiB
Markdown
---
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title: "Deploy ADK Agent and MCP Toolbox"
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type: docs
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weight: 4
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description: >
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How to deploy your ADK Agent to Vertex AI Agent Engine and connect it to an MCP Toolbox deployed on Cloud Run.
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sample_filters: ["Python", "ADK", "Agent"]
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is_sample: true
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---
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## Before you begin
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This guide assumes you have already done the following:
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1. Completed the [Python Quickstart
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(Local)](../documentation/getting-started/local_quickstart.md) and have a working ADK
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agent running locally.
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2. Installed the [Google Cloud CLI](https://cloud.google.com/sdk/docs/install).
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3. A Google Cloud project with billing enabled.
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## Step 1: Deploy MCP Toolbox to Cloud Run
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Before deploying your agent, your MCP Toolbox server needs to be accessible from
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the cloud. We will deploy MCP Toolbox to Cloud Run.
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Follow the [Deploy to Cloud Run](../documentation/deploy-to/cloud-run/_index.md) guide to deploy your MCP
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Toolbox instance.
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{{% alert title="Important" %}}
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After deployment, note down the Service URL of your MCP Toolbox Cloud Run
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service. You will need this to configure your agent.
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{{% /alert %}}
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## Step 2: Prepare your Agent for Deployment
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We will use the `agent-starter-pack` tool to enhance your local agent project
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with the necessary configuration for deployment to Vertex AI Agent Engine.
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1. Open a terminal and navigate to the **parent directory** of your agent
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project (the directory containing the `my_agent` folder).
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2. Run the following command to enhance your project:
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```bash
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uvx agent-starter-pack enhance --adk -d agent_engine
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```
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3. Follow the interactive prompts to configure your deployment settings. This
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process will generate deployment configuration files (like a `Makefile` and
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`Dockerfile`) in your project directory.
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4. Add `google-adk[toolbox]` as a dependency to the new project:
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```bash
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uv add google-adk[toolbox]
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```
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## Step 3: Configure Google Cloud Authentication
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Ensure your local environment is authenticated with Google Cloud to perform the
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deployment.
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1. Login with Application Default Credentials (ADC):
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```bash
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gcloud auth application-default login
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```
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2. Set your active project:
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```bash
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gcloud config set project <YOUR_PROJECT_ID>
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```
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## Step 4: Connect Agent to Deployed MCP Toolbox
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You need to update your agent's code to connect to the Cloud Run URL of your MCP
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Toolbox instead of the local address.
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1. Recall that you can find the Cloud Run deployment URL of the MCP Toolbox
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server using the following command:
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```bash
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gcloud run services describe toolbox --format 'value(status.url)'
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```
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2. Open your agent file (`my_agent/agent.py`).
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3. Update the `ToolboxToolset` initialization to point to your Cloud Run service URL. Replace the existing initialization code with the following:
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{{% alert color="info" title="Note" %}}
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Since Cloud Run services are secured by default, you also need to provide a workload identity.
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{{% /alert %}}
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```python
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from google.adk import Agent
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from google.adk.apps import App
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from google.adk.tools.toolbox_toolset import ToolboxToolset
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from toolbox_adk import CredentialStrategy
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# TODO(developer): Replace with your Toolbox Cloud Run Service URL
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TOOLBOX_URL = "https://your-toolbox-service-xyz.a.run.app"
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# Initialize the toolset with Workload Identity (generates ID token for the URL)
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toolset = ToolboxToolset(
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server_url=TOOLBOX_URL,
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credentials=CredentialStrategy.workload_identity(target_audience=TOOLBOX_URL)
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)
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root_agent = Agent(
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name='root_agent',
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model='gemini-2.5-flash',
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instruction="You are a helpful AI assistant designed to provide accurate and useful information.",
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tools=[toolset],
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)
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app = App(root_agent=root_agent, name="my_agent")
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```
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{{% alert title="Important" %}}
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Ensure that the `name` parameter in the `App` initialization matches the name of
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your agent's parent directory (e.g., `my_agent`).
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```python
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...
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app = App(root_agent=root_agent, name="my_agent")
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```
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{{% /alert %}}
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## Step 5: Deploy to Agent Engine
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Run the deployment command:
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```bash
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make deploy
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```
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This command will build your agent's container image and deploy it to Vertex AI.
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## Step 6: Test your Deployment
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Once the deployment command (`make deploy`) completes, it will output the URL
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for the Agent Engine Playground. You can click on this URL to open the
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Playground in your browser and start chatting with your agent to test the tools.
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For additional test scenarios, refer to the [Test deployed
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agent](https://google.github.io/adk-docs/deploy/agent-engine/#test-deployment)
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section in the ADK documentation. |