Integrate Google's Vertex AI Agent with Live JD Edwards 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 JD Edwards 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 JD Edwards 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 JD Edwards data through Connect AI.

Step 1: Configure JD Edwards connectivity for Vertex AI

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

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

2. Select JD Edwards from the Add Connection panel

3. Enter the necessary authentication properties to connect to JD Edwards.

   The driver connects to JD Edwards through your Application Interface Services (AIS) Server. Set the following connection properties:

   • URL: The base HTTPS URL of your AIS Server (e.g., https://jde-ais.example.com:8300).
   • User: Your JD Edwards username.
   • Password: Your JD Edwards password.
   • Environment (optional): The JD Edwards environment to use (e.g., PD920 for production or DV920 for development). If not specified, the AIS Server's default environment is used.
   • Role (optional): The JD Edwards role for the session. If not specified, the AIS Server's default role is used.
   • DeviceName (optional): An identifier for the connecting device or application, used for auditing and logging on the AIS Server.
   • Jasserver (optional): The specific Java Application Server (JAS) instance to route requests through, useful in clustered environments.

   Choosing Which Data Is Exposed

   JD Edwards organizes tables and business views by System Code, and the driver exposes each System Code as its own schema. Use these properties to control which schemas are available:

   • DataModel: One or more ERP modules (comma-separated) whose System Codes are exposed as schemas, or All to expose every System Code in the connected instance. Defaults to FinancialManagement.
   • SystemCodes: A comma-separated list of additional System Codes to expose alongside those from DataModel (e.g., 42,43).

   When you connect, the driver sends your credentials to the AIS Server to obtain a session token and caches it. The driver requests a new token automatically before the session expires.

   
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 JD Edwards connection configured and a PAT generated, Vertex AI can now connect to JD Edwards 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 JD Edwards data metadata through remote MCP tools.

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