Integrate Trae with Live Confluence Data via CData Connect AI

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 Confluence data. You can list catalogs, explore schemas, and query records from Confluence data using natural language prompts, with all data access running securely against authorized sources.

This article explains how to configure Confluence 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 Confluence data from the Trae agent.

Step 1: Configure Confluence connectivity for Trae

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

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

2. Select Confluence from the Add Connection panel

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

   Obtaining an API Token

   An API token is necessary for account authentication. To generate one, login to your Atlassian account and navigate to API tokens > Create API token. The generated token will be displayed.

   Connect Using a Confluence Cloud Account

   To connect to a Cloud account, provide the following (Note: Password has been deprecated for connecting to a Cloud Account and is now used only to connect to a Server Instance.):

   • User: The user which will be used to authenticate with the Confluence server.
   • APIToken: The API Token associated with the currently authenticated user.
   • Url: The URL associated with your JIRA endpoint. For example, https://yoursitename.atlassian.net.

   Connect Using a Confluence Server Instance

   To connect to a Server instance, provide the following:

   • User: The user which will be used to authenticate with the Confluence instance.
   • Password: The password which will be used to authenticate with the Confluence server.
   • Url: The URL associated with your JIRA endpoint. For example, https://yoursitename.atlassian.net.
   
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 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. Copy the token when displayed and store it securely. It will not be shown again

With the Confluence connection configured and a PAT generated, Trae can now connect to Confluence 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 + \
3. Click Toggle AI Sidebar to open the chat panel
4. Open Settings, then select MCP from the left menu
5. Click Add 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==

   

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

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

Step 3: Query live Confluence data from Trae

With the integration complete, use the Trae agent to interact with live Confluence 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
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 Confluence
   • Query the top 5 records from a table in Confluence data
3. Trae calls the Connect AI MCP Server and returns live results from Confluence data

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

Get CData Connect AI

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