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

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

Step 1: Configure Azure Table connectivity for Trae

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

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

2. Select Azure Table from the Add Connection panel

3. Enter the necessary authentication properties to connect to Azure Table.

   Specify your AccessKey and your Account to connect. Set the Account property to the Storage Account Name and set AccessKey to one of the Access Keys. Either the Primary or Secondary Access Keys can be used. To obtain these values, navigate to the Storage Accounts blade in the Azure portal. You can obtain the access key by selecting your account and clicking Access Keys in the Settings section.

   
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 Azure Table connection configured and a PAT generated, Trae can now connect to Azure Table 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 Azure Table data through Connect AI.

Step 3: Query live Azure Table data from Trae

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

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

Get CData Connect AI

To access hundreds of SaaS, big data, and NoSQL sources directly from your cloud applications, try CData Connect AI today. Download a free 14-day trial of CData Connect AI, and our Support Team is available to help with any questions you have.