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435 lines
16 KiB
Markdown
435 lines
16 KiB
Markdown
# Google Cloud Tools
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{ title="This feature is currently available for Python. Java support is planned/ coming soon."}
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Google Cloud tools make it easier to connect your agents to Google Cloud’s
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products and services. With just a few lines of code you can use these tools to
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connect your agents with:
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* **Any custom APIs** that developers host in Apigee.
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* **100s** of **prebuilt connectors** to enterprise systems such as Salesforce,
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Workday, and SAP.
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* **Automation workflows** built using application integration.
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* **Databases** such as Spanner, AlloyDB, Postgres and more using the MCP Toolbox for
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databases.
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## Apigee API Hub Tools
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**ApiHubToolset** lets you turn any documented API from Apigee API hub into a
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tool with a few lines of code. This section shows you the step by step
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instructions including setting up authentication for a secure connection to your
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APIs.
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**Prerequisites**
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1. [Install ADK](../get-started/installation.md)
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2. Install the
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[Google Cloud CLI](https://cloud.google.com/sdk/docs/install?db=bigtable-docs#installation_instructions).
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3. [Apigee API hub](https://cloud.google.com/apigee/docs/apihub/what-is-api-hub)
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instance with documented (i.e. OpenAPI spec) APIs
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4. Set up your project structure and create required files
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```console
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project_root_folder
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`-- my_agent
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|-- .env
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|-- __init__.py
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|-- agent.py
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`__ tool.py
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```
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### Create an API Hub Toolset
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Note: This tutorial includes an agent creation. If you already have an agent,
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you only need to follow a subset of these steps.
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1. Get your access token, so that APIHubToolset can fetch spec from API Hub API.
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In your terminal run the following command
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```shell
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gcloud auth print-access-token
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# Prints your access token like 'ya29....'
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```
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2. Ensure that the account used has the required permissions. You can use the
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pre-defined role `roles/apihub.viewer` or assign the following permissions:
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1. **apihub.specs.get (required)**
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2. apihub.apis.get (optional)
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3. apihub.apis.list (optional)
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4. apihub.versions.get (optional)
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5. apihub.versions.list (optional)
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6. apihub.specs.list (optional)
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3. Create a tool with `APIHubToolset`. Add the below to `tools.py`
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If your API requires authentication, you must configure authentication for
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the tool. The following code sample demonstrates how to configure an API
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key. ADK supports token based auth (API Key, Bearer token), service account,
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and OpenID Connect. We will soon add support for various OAuth2 flows.
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```py
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from google.adk.tools.openapi_tool.auth.auth_helpers import token_to_scheme_credential
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from google.adk.tools.apihub_tool.apihub_toolset import APIHubToolset
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# Provide authentication for your APIs. Not required if your APIs don't required authentication.
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auth_scheme, auth_credential = token_to_scheme_credential(
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"apikey", "query", "apikey", apikey_credential_str
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)
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sample_toolset_with_auth = APIHubToolset(
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name="apihub-sample-tool",
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description="Sample Tool",
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access_token="...", # Copy your access token generated in step 1
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apihub_resource_name="...", # API Hub resource name
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auth_scheme=auth_scheme,
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auth_credential=auth_credential,
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)
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```
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For production deployment we recommend using a service account instead of an
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access token. In the code snippet above, use
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`service_account_json=service_account_cred_json_str` and provide your
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security account credentials instead of the token.
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For apihub\_resource\_name, if you know the specific ID of the OpenAPI Spec
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being used for your API, use
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`` `projects/my-project-id/locations/us-west1/apis/my-api-id/versions/version-id/specs/spec-id` ``.
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If you would like the Toolset to automatically pull the first available spec
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from the API, use
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`` `projects/my-project-id/locations/us-west1/apis/my-api-id` ``
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4. Create your agent file Agent.py and add the created tools to your agent
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definition:
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```py
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from google.adk.agents.llm_agent import LlmAgent
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from .tools import sample_toolset
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root_agent = LlmAgent(
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model='gemini-2.0-flash',
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name='enterprise_assistant',
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instruction='Help user, leverage the tools you have access to',
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tools=sample_toolset.get_tools(),
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)
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```
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5. Configure your `__init__.py` to expose your agent
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```py
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from . import agent
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```
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6. Start the Google ADK Web UI and try your agent:
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```shell
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# make sure to run `adk web` from your project_root_folder
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adk web
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```
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Then go to [http://localhost:8000](http://localhost:8000) to try your agent from the Web UI.
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---
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## Application Integration Tools
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With **ApplicationIntegrationToolset** you can seamlessly give your agents a
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secure and governed to enterprise applications using Integration Connector’s
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100+ pre-built connectors for systems like Salesforce, ServiceNow, JIRA, SAP,
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and more. Support for both on-prem and SaaS applications. In addition you can
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turn your existing Application Integration process automations into agentic
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workflows by providing application integration workflows as tools to your ADK
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agents.
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**Prerequisites**
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1. [Install ADK](../get-started/installation.md)
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2. An existing
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[Application Integration](https://cloud.google.com/application-integration/docs/overview)
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workflow or
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[Integrations Connector](https://cloud.google.com/integration-connectors/docs/overview)
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connection you want to use with your agent
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3. To use tool with default credentials: have Google Cloud CLI installed. See
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[installation guide](https://cloud.google.com/sdk/docs/install#installation_instructions)*.*
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*Run:*
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```shell
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gcloud config set project <project-id>
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gcloud auth application-default login
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gcloud auth application-default set-quota-project <project-id>
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```
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5. Set up your project structure and create required files
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```console
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project_root_folder
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|-- .env
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`-- my_agent
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|-- __init__.py
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|-- agent.py
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`__ tools.py
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```
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When running the agent, make sure to run adk web in project\_root\_folder
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### Use Integration Connectors
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Connect your agent to enterprise applications using
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[Integration Connectors](https://cloud.google.com/integration-connectors/docs/overview).
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**Prerequisites**
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1. To use a connector from Integration Connectors, you need to [provision](https://console.cloud.google.com/integrations)
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Application Integration in the same region as your connection by clicking on "QUICK SETUP" button.
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2. Go to [Connection Tool](https://console.cloud.google.com/integrations/templates/connection-tool/locations/us-central1)
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template from the template library and click on "USE TEMPLATE" button.
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3. Fill the Integration Name as **ExecuteConnection** (It is mandatory to use this integration name only) and
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select the region same as the connection region. Click on "CREATE".
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4. Publish the integration by using the "PUBLISH" button on the Application Integration Editor.
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**Steps:**
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1. Create a tool with `ApplicationIntegrationToolset` within your `tools.py` file
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```py
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from google.adk.tools.application_integration_tool.application_integration_toolset import ApplicationIntegrationToolset
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connector_tool = ApplicationIntegrationToolset(
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project="test-project", # TODO: replace with GCP project of the connection
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location="us-central1", #TODO: replace with location of the connection
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connection="test-connection", #TODO: replace with connection name
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entity_operations={"Entity_One": ["LIST","CREATE"], "Entity_Two": []},#empty list for actions means all operations on the entity are supported.
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actions=["action1"], #TODO: replace with actions
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service_account_credentials='{...}', # optional. Stringified json for service account key
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tool_name_prefix="tool_prefix2",
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tool_instructions="..."
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)
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```
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**Note:**
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* You can provide service account to be used instead of using default credentials by generating [Service Account Key](https://cloud.google.com/iam/docs/keys-create-delete#creating) and providing right Application Integration and Integration Connector IAM roles to the service account.
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* To find the list of supported entities and actions for a connection, use the connectors apis: [listActions](https://cloud.google.com/integration-connectors/docs/reference/rest/v1/projects.locations.connections.connectionSchemaMetadata/listActions) or [listEntityTypes](https://cloud.google.com/integration-connectors/docs/reference/rest/v1/projects.locations.connections.connectionSchemaMetadata/listEntityTypes)
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`ApplicationIntegrationToolset` now also supports providing auth_scheme and auth_credential for dynamic OAuth2 authentication for Integration Connectors. To use it, create a tool similar to this within your `tools.py` file:
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```py
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from google.adk.tools.application_integration_tool.application_integration_toolset import ApplicationIntegrationToolset
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from google.adk.tools.openapi_tool.auth.auth_helpers import dict_to_auth_scheme
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from google.adk.auth import AuthCredential
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from google.adk.auth import AuthCredentialTypes
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from google.adk.auth import OAuth2Auth
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oauth2_data_google_cloud = {
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"type": "oauth2",
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"flows": {
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"authorizationCode": {
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"authorizationUrl": "https://accounts.google.com/o/oauth2/auth",
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"tokenUrl": "https://oauth2.googleapis.com/token",
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"scopes": {
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"https://www.googleapis.com/auth/cloud-platform": (
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"View and manage your data across Google Cloud Platform"
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" services"
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),
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"https://www.googleapis.com/auth/calendar.readonly": "View your calendars"
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},
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}
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},
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}
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oauth_scheme = dict_to_auth_scheme(oauth2_data_google_cloud)
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auth_credential = AuthCredential(
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auth_type=AuthCredentialTypes.OAUTH2,
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oauth2=OAuth2Auth(
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client_id="...", #TODO: replace with client_id
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client_secret="...", #TODO: replace with client_secret
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),
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)
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connector_tool = ApplicationIntegrationToolset(
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project="test-project", # TODO: replace with GCP project of the connection
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location="us-central1", #TODO: replace with location of the connection
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connection="test-connection", #TODO: replace with connection name
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entity_operations={"Entity_One": ["LIST","CREATE"], "Entity_Two": []},#empty list for actions means all operations on the entity are supported.
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actions=["GET_calendars/%7BcalendarId%7D/events"], #TODO: replace with actions. this one is for list events
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service_account_credentials='{...}', # optional. Stringified json for service account key
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tool_name_prefix="tool_prefix2",
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tool_instructions="...",
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auth_scheme=oauth_scheme,
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auth_credential=auth_credential
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)
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```
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2. Add the tool to your agent. Update your `agent.py` file
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```py
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from google.adk.agents.llm_agent import LlmAgent
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from .tools import connector_tool
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root_agent = LlmAgent(
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model='gemini-2.0-flash',
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name='connector_agent',
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instruction="Help user, leverage the tools you have access to",
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tools=[connector_tool],
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)
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```
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3. Configure your `__init__.py` to expose your agent
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```py
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from . import agent
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```
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4. Start the Google ADK Web UI and try your agent.
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```shell
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# make sure to run `adk web` from your project_root_folder
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adk web
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```
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Then go to [http://localhost:8000](http://localhost:8000), and choose
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my\_agent agent (same as the agent folder name)
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### Use App Integration Workflows
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Use existing
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[Application Integration](https://cloud.google.com/application-integration/docs/overview)
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workflow as a tool for your agent or create a new one.
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**Steps:**
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1. Create a tool with `ApplicationIntegrationToolset` within your `tools.py` file
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```py
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integration_tool = ApplicationIntegrationToolset(
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project="test-project", # TODO: replace with GCP project of the connection
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location="us-central1", #TODO: replace with location of the connection
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integration="test-integration", #TODO: replace with integration name
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triggers=["api_trigger/test_trigger"],#TODO: replace with trigger id(s). Empty list would mean all api triggers in the integration to be considered.
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service_account_credentials='{...}', #optional. Stringified json for service account key
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tool_name_prefix="tool_prefix1",
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tool_instructions="..."
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)
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```
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Note: You can provide service account to be used instead of using default
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credentials by generating [Service Account Key](https://cloud.google.com/iam/docs/keys-create-delete#creating) and providing right Application Integration and Integration Connector IAM roles to the service account.
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2. Add the tool to your agent. Update your `agent.py` file
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```py
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from google.adk.agents.llm_agent import LlmAgent
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from .tools import integration_tool, connector_tool
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root_agent = LlmAgent(
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model='gemini-2.0-flash',
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name='integration_agent',
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instruction="Help user, leverage the tools you have access to",
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tools=[integration_tool],
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)
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```
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3. Configure your \`\_\_init\_\_.py\` to expose your agent
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```py
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from . import agent
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```
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4. Start the Google ADK Web UI and try your agent.
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```shell
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# make sure to run `adk web` from your project_root_folder
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adk web
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```
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Then go to [http://localhost:8000](http://localhost:8000), and choose
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my\_agent agent (same as the agent folder name)
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---
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## Toolbox Tools for Databases
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[MCP Toolbox for Databases](https://github.com/googleapis/genai-toolbox) is an
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open source MCP server for databases. It was designed with enterprise-grade and
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production-quality in mind. It enables you to develop tools easier, faster, and
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more securely by handling the complexities such as connection pooling,
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authentication, and more.
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Google’s Agent Development Kit (ADK) has built in support for Toolbox. For more
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information on
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[getting started](https://googleapis.github.io/genai-toolbox/getting-started) or
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[configuring](https://googleapis.github.io/genai-toolbox/getting-started/configure/)
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Toolbox, see the
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[documentation](https://googleapis.github.io/genai-toolbox/getting-started/introduction/).
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### Configure and deploy
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Toolbox is an open source server that you deploy and manage yourself. For more
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instructions on deploying and configuring, see the official Toolbox
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documentation:
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* [Installing the Server](https://googleapis.github.io/genai-toolbox/getting-started/introduction/#installing-the-server)
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* [Configuring Toolbox](https://googleapis.github.io/genai-toolbox/getting-started/configure/)
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### Install client SDK
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ADK relies on the `toolbox-core` python package to use Toolbox. Install the
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package before getting started:
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```shell
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pip install toolbox-core
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```
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### Loading Toolbox Tools
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Once you’re Toolbox server is configured and up and running, you can load tools
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from your server using ADK:
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```python
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from google.adk.agents import Agent
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from toolbox_core import ToolboxSyncClient
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toolbox = ToolboxSyncClient("https://127.0.0.1:5000")
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# Load a specific set of tools
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tools = toolbox.load_toolset('my-toolset-name'),
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# Load single tool
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tools = toolbox.load_tool('my-tool-name'),
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root_agent = Agent(
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...,
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tools=tools # Provide the list of tools to the Agent
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)
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```
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### Advanced Toolbox Features
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Toolbox has a variety of features to make developing Gen AI tools for databases.
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For more information, read more about the following features:
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* [Authenticated Parameters](https://googleapis.github.io/genai-toolbox/resources/tools/#authenticated-parameters): bind tool inputs to values from OIDC tokens automatically, making it easy to run sensitive queries without potentially leaking data
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* [Authorized Invocations:](https://googleapis.github.io/genai-toolbox/resources/tools/#authorized-invocations) restrict access to use a tool based on the users Auth token
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* [OpenTelemetry](https://googleapis.github.io/genai-toolbox/how-to/export_telemetry/): get metrics and tracing from Toolbox with OpenTelemetry
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