Integrate Google's Vertex AI Agent with Live PingOne Data via CData Connect AI

Vertex AI provides a development ecosystem for building AI agents using the Agent Development Kit (ADK). ADK enables developers to create tool-augmented agents that can reason, take actions, and interact with external systems through structured tool interfaces. These agents can be tested locally in the ADK Web interface and extended with advanced logic for enterprise workflows.

By integrating Vertex AI ADK with CData Connect AI through the built-in MCP (Model Context Protocol) Server, your agents gain the ability to query, analyze, and act on live PingOne data in real time. This connection bridges Google's agent-building framework with the governed enterprise connectivity of CData Connect AI, ensuring every request runs securely against authorized data sources without manual data movement.

This article outlines the steps to configure PingOne connectivity in Connect AI, generate the required authentication token, configure the Vertex AI ADK environment, and verify that your agent can successfully communicate with live PingOne data through Connect AI.

Step 1: Configure PingOne connectivity for Vertex AI

Connectivity to PingOne from Vertex AI is made possible through CData Connect AI's Remote MCP Server. To interact with PingOne data from Vertex AI, start by creating and configuring a PingOne connection in CData Connect AI.

1. Log into Connect AI, click Sources, and then click Add Connection

2. Select PingOne from the Add Connection panel

3. Enter the necessary authentication properties to connect to PingOne.

   To connect to PingOne, configure these properties:

   • Region: The region where the data for your PingOne organization is being hosted.
   • AuthScheme: The type of authentication to use when connecting to PingOne.
   • Either WorkerAppEnvironmentId (required when using the default PingOne domain) or AuthorizationServerURL, configured as described below.

   Configuring WorkerAppEnvironmentId

   WorkerAppEnvironmentId is the ID of the PingOne environment in which your Worker application resides. This parameter is used only when the environment is using the default PingOne domain (auth.pingone). It is configured after you have created the custom OAuth application you will use to authenticate to PingOne, as described in Creating a Custom OAuth Application in the Help documentation.

   First, find the value for this property:

   1. From the home page of your PingOne organization, move to the navigation sidebar and click Environments.
   2. Find the environment in which you have created your custom OAuth/Worker application (usually Administrators), and click Manage Environment. The environment's home page displays.
   3. In the environment's home page navigation sidebar, click Applications.
   4. Find your OAuth or Worker application details in the list.
   5. Copy the value in the Environment ID field. It should look similar to:
      

      WorkerAppEnvironmentId='11e96fc7-aa4d-4a60-8196-9acf91424eca'

   Now set WorkerAppEnvironmentId to the value of the Environment ID field.

   Configuring AuthorizationServerURL

   AuthorizationServerURL is the base URL of the PingOne authorization server for the environment where your application is located. This property is only used when you have set up a custom domain for the environment, as described in the PingOne platform API documentation. See Custom Domains.

   Authenticating to PingOne with OAuth

   PingOne supports both OAuth and OAuthClient authentication. In addition to performing the configuration steps described above, there are two more steps to complete to support OAuth or OAuthCliet authentication:

   • Create and configure a custom OAuth application, as described in Creating a Custom OAuth Application in the Help documentation.
   • To ensure that the driver can access the entities in Data Model, confirm that you have configured the correct roles for the admin user/worker application you will be using, as described in Administrator Roles in the Help documentation.
   • Set the appropriate properties for the authscheme and authflow of your choice, as described in the following subsections.

   OAuth (Authorization Code grant)

   Set AuthScheme to OAuth.

   Desktop Applications

   Get and Refresh the OAuth Access Token

   After setting the following, you are ready to connect:

   • InitiateOAuth: GETANDREFRESH. To avoid the need to repeat the OAuth exchange and manually setting the OAuthAccessToken each time you connect, use InitiateOAuth.
   • OAuthClientId: The Client ID you obtained when you created your custom OAuth application.
   • OAuthClientSecret: The Client Secret you obtained when you created your custom OAuth application.
   • CallbackURL: The redirect URI you defined when you registered your custom OAuth application. For example: https://localhost:3333

   When you connect, the driver opens PingOne's OAuth endpoint in your default browser. Log in and grant permissions to the application. The driver then completes the OAuth process:

   1. The driver obtains an access token from PingOne and uses it to request data.
   2. The OAuth values are saved in the location specified in OAuthSettingsLocation, to be persisted across connections.

   The driver refreshes the access token automatically when it expires.

   For other OAuth methods, including Web Applications, Headless Machines, or Client Credentials Grant, refer to the Help documentation.

   
4. Click Save & Test
5. Navigate to the Permissions tab and update user-based permissions

Add a Personal Access Token

A Personal Access Token (PAT) is used to authenticate the connection to Connect AI from Vertex AI. It is best practice to create a separate PAT for each integration to maintain granular access control.

1. Click the gear icon () at the top right of the Connect AI app to open Settings
2. On the Settings page, go to the Access Tokens section and click Create PAT
3. Give the PAT a descriptive name and click Create

4. Copy the token when displayed and store it securely. It will not be shown again

With the PingOne connection configured and a PAT generated, Vertex AI can now connect to PingOne data through Connect AI.

Step 2: Install required dependencies

Enable the necessary Google Cloud APIs so Vertex AI ADK can run Gemini models, build agent environments, and access supporting services inside your Google Cloud project. These APIs provide the backend capabilities that ADK relies on during development and execution.

1. Visit the Google Cloud Console
2. Click the Project Picker at the top of the page and choose New Project

3. Create the project and note the Project ID. Save this ID later for environment configuration
4. From the left navigation menu, open APIs & Services and then choose Enabled APIs & Services
5. Click Enable Apis and services

6. Enable the following APIs:
   • Vertex AI API
   • Cloud Build API
   • Artifact Registry API
   • Service Networking API
   • Cloud Logging API

With these services enabled, your Google Cloud project is prepared for Vertex AI ADK development and local tool execution.

Prepare the Vertex AI ADK project folder

Create the project directory and set up the Python environment. This step prepares a clean workspace where ADK installs correctly and loads your agent without dependency conflicts.

1. Open Google Cloud console and select the Cloud Shell. Make sure to select the project you created from the project picker.

2. Create the ADK project directories:

				mkdir -p ~/adk_agents/cdata_mcp_agent
				cd ~/adk_agents/cdata_mcp_agent 
			

3. Create and activate a Python virtual environment:

		python3 -m venv .venv
		source .venv/bin/activate
	

4. Install the required ADK and MCP packages:

				python -m pip install --upgrade pip
				python -m pip install google-adk
				python -m pip install mcp
				python -m pip install --upgrade "google-cloud-aiplatform[agent_engines]"
			

Step 3: Create the ADK Agent files

Define the agent modules so ADK recognizes your agent. This structure allows Vertex AI to load your MCP configuration and register the tools exposed by Connect AI.

1. Create agent.py file and paste the code below in it

		
import os
import base64
import logging
 
from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import MCPToolset
from google.adk.tools.mcp_tool.mcp_session_manager import StreamableHTTPConnectionParams
 
# ---------- Logging ----------
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
 
# ---------- CData MCP config ----------
CDATA_MCP_URL = os.environ.get("CDATA_MCP_URL", "https://mcp.cloud.cdata.com/mcp")
CDATA_USER_ID = os.environ.get("CDATA_USER_ID")
CDATA_PAT = os.environ.get("CDATA_PAT")
 
tools = []
 
if not (CDATA_USER_ID and CDATA_PAT):
    logger.warning(
        "CData MCP credentials not set (CDATA_USER_ID or CDATA_PAT missing); "
        "starting agent WITHOUT MCP tools."
    )
else:
    # Basic auth header: base64("user:pat")
    basic_auth_bytes = f"{CDATA_USER_ID}:{CDATA_PAT}".encode("utf-8")
    basic_auth_header = base64.b64encode(basic_auth_bytes).decode("utf-8")
 
    try:
        logger.info("Initializing CData MCPToolset against %s", CDATA_MCP_URL)
 
        tools.append(
            MCPToolset(
                connection_params=StreamableHTTPConnectionParams(
                    url=CDATA_MCP_URL,
                    headers={
                        "Authorization": f"Basic {basic_auth_header}",
                        # ADK handles content-type etc. internally;
                        # we just pass auth headers.
                    },
                ),
            )
        )
 
        logger.info("CData MCPToolset initialized successfully.")
    except Exception as e:
        logger.exception("Failed to initialize CData MCPToolset")
 
# ---------- Root agent ----------
root_agent = LlmAgent(
    model="gemini-2.0-flash",
    name="cdata_mcp_agent",
    instruction=(
        "You are a data assistant. Use the CData MCP tools (if available) to "
        "list connections, list catalogs/schemas/tables, and run SQL-style queries."
    ),
    tools=tools,
)
	

2. Create another file __int__.py and paste the code below in it

		
		from .agent import root_agent
		__all__ = ["root_agent"]

Export environment variables

Export the required environment variables to authenticate to Connect AI. These values enable the agent to initialize the MCP toolset and communicate with Connect AI. Before you do that obtain a Google API key so ADK can authenticate to Gemini models. This key enables the agent to run LLM reasoning and route tool calls correctly inside the Vertex AI ADK environment.

1. Visit the Google AI Studio API Key page
2. Click Create API Key. Provide a name for the api-key and choose or create a project if you do not have one

3. Click on Create a key and then copy the API key
4. Return to the Google Cloud Shell page and run the environment variable exports. Replace "your_cdata_email", "your_pat", "your-project-id", and "your_google_api_key" with your values

		export CDATA_MCP_URL="https://mcp.cloud.cdata.com/mcp"
		export CDATA_USER_ID="your_cdata_email"
		export CDATA_PAT="your_pat"
		export GOOGLE_API_KEY="your_google_api_key"
		export VERTEXAI_PROJECT="your-project-id"
		export VERTEXAI_LOCATION="us-central1"
	

Step 4: Launch the ADK web interface

Start the ADK Web interface to load your agent. The interface initializes the runtime and makes your MCP-enabled agent available for interactive testing.

1. Move to the parent folder:

				cd ~/adk_agents
			

2. Launch ADK Web:

				adk web .
			

Step 5: Select your agent and test MCP connectivity

Select your agent from the ADK Web interface. ADK loads the MCP tools and prepares the environment so you issue live MCP queries.

1. Open the ADK Web UI from the browser tab
2. Select cdata_mcp_agent from the agent dropdown

3. Enter list catalogs in the chat panel. ADK returns a live list of your Connect AI connections

At this point, your Vertex AI ADK agent communicates with the CData Connect AI MCP Server and retrieves live PingOne data metadata through remote MCP tools.

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