Integrate Trae with Live JD Edwards Data via CData Connect AI

Yazhini G
Yazhini G
Technical Marketing Engineer
Use the CData Connect AI Remote MCP Server to enable Trae to securely access and query live JD Edwards data from within the AI-powered IDE.

Trae is an AI-powered integrated development environment (IDE) that pairs a familiar editor with agent modes such as Builder and SOLO. It supports the Model Context Protocol (MCP), so you can add external tools and data sources and give the agent access to live data.

By integrating Trae with CData Connect AI through the built-in MCP Server, Trae gains governed, real-time access to live JD Edwards data. You can list catalogs, explore schemas, and query records from JD Edwards data using natural language prompts, with all data access running securely against authorized sources.

This article explains how to configure JD Edwards connectivity in Connect AI, generate the required personal access token, install Trae, add the Connect AI MCP Server, configure an LLM model, and verify the integration by querying live JD Edwards data from the Trae agent.

Step 1: Configure JD Edwards connectivity for Trae

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

  1. Log into Connect AI, click Sources, and then click Add Connection
  2. Adding a connection in Connect AI
  3. Select JD Edwards from the Add Connection panel
  4. Selecting data source
  5. 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.

    Configuring a connection (Salesforce is shown)
  6. Click Save & Test
  7. Navigate to the Permissions tab and update user-based permissions
  8. Updating permissions

Add a Personal Access Token

A Personal Access Token (PAT) is used to authenticate the connection to Connect AI from Trae. 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. Creating a new PAT
  5. 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, Trae can now connect to JD Edwards data through Connect AI.

Step 2: Install Trae and configure the Connect AI MCP Server

Next, install Trae, add the Connect AI Remote MCP Server, and configure an LLM model so the agent can discover and call live data tools through Connect AI.

  1. Download and install the Trae IDE, then launch the application
  2. Switch to SOLO mode using the toggle at the top left, or press Ctrl + Alt + \ Switching to SOLO mode in Trae
  3. Click Toggle AI Sidebar to open the chat panel Opening the AI sidebar in Trae
  4. Open Settings, then select MCP from the left menu Navigating to MCP settings
  5. Click Add Manually Adding an MCP server manually
  6. In the Configure Manually dialog, paste the following configuration and click Confirm:
    {
      "mcpServers": {
        "cdata-connect-ai": {
          "type": "streamable-http",
          "url": "https://mcp.cloud.cdata.com/mcp",
          "headers": {
            "Authorization": "Basic your_base64_encoded_email_PAT"
          }
        }
      }
    }
    		

    Note: Trae will use Basic authentication with Connect AI. Combine your Connect AI user email and the PAT you created earlier in the format email:PAT, base64 encode the combined string, and prefix it with Basic. For example, given [email protected]:ABC123...XYZ789, the Authorization header value becomes something like: Basic dXNlckBkb21haW4uY29tOkFCQzEyMy4uLlhZWjc4OQ==

    Configuring the Connect AI MCP Server

Configure an LLM model

Trae requires at least one LLM model to power the agent's reasoning. Add a model so the agent can interpret prompts and call MCP tools through Connect AI.

  1. Return to Settings and select Models
  2. Click Add Model, choose a provider such as OpenAI, Anthropic, or Google, select a model, enter your API key, and click Add Model Adding an LLM model and API key

With the MCP server added and an LLM model configured, Trae is ready to query live JD Edwards data through Connect AI.

Step 3: Query live JD Edwards data from Trae

With the integration complete, use the Trae agent to interact with live JD Edwards data through natural language prompts handled by the configured LLM.

  1. In the chat panel, type @ and select Builder with MCP. Confirm that cdata-connect-ai is listed under Tools - MCP Selecting the Builder with MCP agent
  2. Enter a prompt to interact with your data, for example:
    • List all catalogs in cdata-connect-ai
    • Show the available schemas and tables for JD Edwards
    • Query the top 5 records from a table in JD Edwards data
  3. Trae calls the Connect AI MCP Server and returns live results from JD Edwards data Querying live data from the Trae agent

At this point, the Trae agent communicates with the Connect AI MCP Server and retrieves live JD Edwards data through remote MCP tools directly from the IDE.

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