Integrate Google's Vertex AI Agent with Live SAP Ariba Source Data via CData Connect AI

Yazhini G
Yazhini G
Technical Marketing Engineer
Leverage the CData Connect AI Remote MCP Server to enable Vertex AI ADK agents to securely access and act on live SAP Ariba Source data within intelligent workflows.

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 SAP Ariba Source 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 SAP Ariba Source 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 SAP Ariba Source data through the CData MCP Server.

Step 1: Configure SAP Ariba Source connectivity for Vertex AI

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

  1. Log into Connect AI, click Sources, and then click Add Connection
  2. Select SAP Ariba Source from the Add Connection panel
  3. Enter the necessary authentication properties to connect to SAP Ariba Source.

    In order to connect with SAP Ariba Source, set the following:

    • API: Specify which API you would like the provider to retrieve SAP Ariba data from. Select the Supplier, Sourcing Project Management, or Contract API based on your business role (possible values are SupplierDataAPIWithPaginationV4, SourcingProjectManagementAPIV2, or ContractAPIV1).
    • DataCenter: The data center where your account's data is hosted.
    • Realm: The name of the site you want to access.
    • Environment: Indicate whether you are connecting to a test or production environment (possible values are TEST or PRODUCTION).

    If you are connecting to the Supplier Data API or the Contract API, additionally set the following:

    • User: Id of the user on whose behalf API calls are invoked.
    • PasswordAdapter: The password associated with the authenticating User.

    If you're connecting to the Supplier API, set ProjectId to the Id of the sourcing project you want to retrieve data from.

    Authenticating with OAuth

    After setting connection properties, you need to configure OAuth connectivity to authenticate.

    • Set AuthScheme to OAuthClient.
    • Register an application with the service to obtain the APIKey, OAuthClientId and OAuthClientSecret.

      For more information on creating an OAuth application, refer to the Help documentation.

    Automatic OAuth

    After setting the following, you are ready to connect:

      APIKey: The Application key in your app settings. OAuthClientId: The OAuth Client Id in your app settings. OAuthClientSecret: The OAuth Secret in your app settings.

    When you connect, the provider automatically completes the OAuth process:

    1. The provider obtains an access token from SAP Ariba and uses it to request data.
    2. The provider refreshes the access token automatically when it expires.
    3. The OAuth values are saved in memory relative to the location specified in OAuthSettingsLocation.
  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 SAP Ariba Source connection configured and a PAT generated, Vertex AI can now connect to SAP Ariba Source data through the CData MCP Server.

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:
    3. 				mkdir -p ~/adk_agents/cdata_mcp_agent
				cd ~/adk_agents/cdata_mcp_agent
  3. Create and activate a Python virtual environment:
    4. 		python3 -m venv .venv
		source .venv/bin/activate
  4. Install the required ADK and MCP packages:
    5. 				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 the CData MCP Server.

  1. Create agent.py file and paste the code below in it
    2. 		
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
    3. 		
		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 the CData MCP Server. 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
    5. 		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:
    2. 				cd ~/adk_agents
  2. Launch ADK Web:
    3. 				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 SAP Ariba Source data metadata through remote MCP tools.

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