Google Cloud Tools

Google Cloud tools make it easier to connect your agents to Google Cloud’s products and services. With just a few lines of code you can use these tools to connect your agents with:

• Millions of custom APIs that developers host in Apigee.
• 100s of prebuilt connectors to enterprise systems such as Salesforce, Workday, and SAP.
• Automation workflows built using application integration.
• Databases such as Spanner, AlloyDB, Postgres and more using the MCP Toolbox for databases.

Apigee API Hub Tools

ApiHubToolset lets you turn any documented API from Apigee API hub into a tool with a few lines of code. This section shows you the step by step instructions including setting up authentication for a secure connection to your APIs.

Prerequisites

1. Install ADK
2. Install the Google Cloud CLI.
3. Apigee API hub instance with documented (i.e. OpenAPI spec) APIs
4. Set up your project structure and create required files

project_root_folder  |  `-- my_agent      |-- .env      |-- __init__.py      |-- agent.py      `__ tool.py 

Create an API Hub Toolset

Note: This tutorial includes an agent creation. If you already have an agent, you only need to follow a subset of these steps.

1. Get your access token, so that APIHubToolset can fetch spec from API Hub API. In your terminal run the following command

   gcloud auth print-access-token # Prints your access token like 'ya29....' 

2. Ensure that the account used has the required permissions. You can use the pre-defined role roles/apihub.viewer or assign the following permissions:

   1. apihub.specs.get (required)
   2. apihub.apis.get (optional)
   3. apihub.apis.list (optional)
   4. apihub.versions.get (optional)
   5. apihub.versions.list (optional)
   6. apihub.specs.list (optional)

3. Create a tool with APIHubToolset. Add the below to tools.py

   If your API requires authentication, you must configure authentication for the tool. The following code sample demonstrates how to configure an API key. ADK supports token based auth (API Key, Bearer token), service account, and OpenID Connect. We will soon add support for various OAuth2 flows.

   from google.adk.tools.openapi_tool.auth.auth_helpers import token_to_scheme_credential from google.adk.tools.apihub_tool.apihub_toolset import APIHubToolset  # Provide authentication for your APIs. Not required if your APIs don't required authentication. auth_scheme, auth_credential = token_to_scheme_credential(     "apikey", "query", "apikey", apikey_credential_str )  sample_toolset_with_auth = APIHubToolset(     name="apihub-sample-tool",     description="Sample Tool",     access_token="...",  # Copy your access token generated in step 1     apihub_resource_name="...", # API Hub resource name     auth_scheme=auth_scheme,     auth_credential=auth_credential, ) 

   For production deployment we recommend using a service account instead of an access token. In the code snippet above, use service_account_json=service_account_cred_json_str and provide your security account credentials instead of the token.

   For apihub_resource_name, if you know the specific ID of the OpenAPI Spec being used for your API, use `projects/my-project-id/locations/us-west1/apis/my-api-id/versions/version-id/specs/spec-id`. If you would like the Toolset to automatically pull the first available spec from the API, use `projects/my-project-id/locations/us-west1/apis/my-api-id`

4. Create your agent file Agent.py and add the created tools to your agent definition:

   from google.adk.agents.llm_agent import LlmAgent from .tools import sample_toolset  root_agent = LlmAgent(     model='gemini-2.0-flash',     name='enterprise_assistant',     instruction='Help user, leverage the tools you have access to',     tools=sample_toolset.get_tools(), ) 

5. Configure your `__init__.py` to expose your agent

   from . import agent 

6. Start the Google ADK Web UI and try your agent:

   # make sure to run `adk web` from your project_root_folder adk web 

Then go to http://localhost:8000 to try your agent from the Web UI.

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Application Integration Tools

With ApplicationIntegrationToolset you can seamlessly give your agents a secure and governed to enterprise applications using Integration Connector’s 100+ pre-built connectors for systems like Salesforce, ServiceNow, JIRA, SAP, and more. Support for both on-prem and SaaS applications. In addition you can turn your existing Application Integration process automations into agentic workflows by providing application integration workflows as tools to your ADK agents.

Prerequisites

1. Install ADK
2. An existing Application Integration workflow or Integrations Connector connection you want to use with your agent
3. To use tool with default credentials: have Google Cloud CLI installed. See installation guide.

Run :

```shell gcloud config set project gcloud auth application-default login gcloud auth application-default set-quota-project <project-id> ``` 

1. Set up your project structure and create required files

   project_root_folder |-- .env `-- my_agent     |-- __init__.py     |-- agent.py     `__ tools.py 

When running the agent, make sure to run adk web in project_root_folder

Use Integration Connectors

Connect your agent to enterprise applications using Integration Connectors.

1. To use a connector from Integration Connectors, you need to provision Application Integration in the same region as your connection, import and publish Connection Tool from the template library.

2. Create a tool with ApplicationIntegrationToolset

   from google.adk.tools.application_integration_tool.application_integration_toolset import ApplicationIntegrationToolset  connector_tool = ApplicationIntegrationToolset(     project="test-project", # TODO: replace with GCP project of the connection     location="us-central1", #TODO: replace with location of the connection     connection="test-connection", #TODO: replace with connection name     entity_operations={"Entity_One": ["LIST","CREATE"], "Entity_Two": []},#empty list for actions means all operations on the entity are supported.     actions=["action1"], #TODO: replace with actions     service_account_credentials='{...}', # optional     tool_name="tool_prefix2",     tool_instructions="..." ) 

   Note: You can provide service account to be used instead of using default credentials To find the list of supported entities and actions for a connection, use the connectors apis: listActions, listEntityTypes

3. Add the tool to your agent. Update your agent.py file

   from google.adk.agents.llm_agent import LlmAgent from .tools import connector_tool  root_agent = LlmAgent(     model='gemini-2.0-flash',     name='connector_agent',     instruction="Help user, leverage the tools you have access to",     tools=connector_tool.get_tools(), ) 

4. Configure your `__init__.py` to expose your agent

   from . import agent 

5. Start the Google ADK Web UI and try your agent.

   # make sure to run `adk web` from your project_root_folder adk web 

Then go to http://localhost:8000, and choose my_agent agent (same as the agent folder name)

Use App Integration Workflows

Use existing Application Integration workflow as a tool for your agent.

1. Create a tool with ApplicationIntegrationToolset

   integration_tool = ApplicationIntegrationToolset(     project="test-project", # TODO: replace with GCP project of the connection     location="us-central1", #TODO: replace with location of the connection     integration="test-integration", #TODO: replace with integration name     trigger="api_trigger/test_trigger",#TODO: replace with trigger id     service_account_credentials='{...}', #optional     tool_name="tool_prefix1",     tool_instructions="..." ) 

   Note: You can provide service account to be used instead of using default credentials

2. Add the tool to your agent. Update your agent.py file

   from google.adk.agents.llm_agent import LlmAgent from .tools import integration_tool, connector_tool  root_agent = LlmAgent(     model='gemini-2.0-flash',     name='integration_agent',     instruction="Help user, leverage the tools you have access to",     tools=integration_tool.get_tools(), ) 

3. Configure your `__init__.py` to expose your agent

   from . import agent 

4. Start the Google ADK Web UI and try your agent.

   # make sure to run `adk web` from your project_root_folder adk web 

   Then go to http://localhost:8000, and choose my_agent agent (same as the agent folder name)

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Toolbox Tools for Databases

MCP Toolbox for Databases is an open source MCP server for databases. It was designed with enterprise-grade and production-quality in mind. It enables you to develop tools easier, faster, and more securely by handling the complexities such as connection pooling, authentication, and more.

Google’s Agent Development Kit (ADK) has built in support for Toolbox. For more information on getting started or configuring Toolbox, see the documentation.

Configure and deploy

Toolbox is an open source server that you deploy and manage yourself. For more instructions on deploying and configuring, see the official Toolbox documentation:

• Installing the Server
• Configuring Toolbox

Install client SDK

ADK relies on the toolbox-langchain python package to use Toolbox. Install the package before getting started:

pip install toolbox-langchain langchain 

Loading Toolbox Tools

Once you’ve Toolbox server is configured and up and running, you can load tools from your server using the ADK:

from google.adk.tools.toolbox_tool import ToolboxTool  toolbox = ToolboxTool("https://127.0.0.1:5000")  # Load a specific set of tools tools = toolbox.get_toolset(toolset_name='my-toolset-name'), # Load single tool tools = toolbox.get_tool(tool_name='my-tool-name'),  root_agent = Agent(     ...,     tools=tools # Provide the list of tools to the Agent  ) 

Advanced Toolbox Features

Toolbox has a variety of features to make developing Gen AI tools for databases. For more information, read more about the following features:

• Authenticated Parameters: bind tool inputs to values from OIDC tokens automatically, making it easy to run sensitive queries without potentially leaking data
• Authorized Invocations: restrict access to use a tool based on the users Auth token
• OpenTelemetry: get metrics and tracing from Toolbox with OpenTelemetry