Integrate Live Azure Table Data in the Windsurf IDE via CData Connect AI

Windsurf is an AI-native IDE built around Cascade, an autonomous coding agent that understands project context and executes multi-step tasks directly inside the editor. Cascade supports the Model Context Protocol (MCP), allowing the agent to discover and call external tools and data sources without leaving the development environment.

By integrating Windsurf with CData Connect AI through the built-in MCP server, the Cascade agent gains governed, real-time access to live Azure Table data. This enables developers to list catalogs, inspect schemas, and query records from Azure Table data within the IDE using natural language prompts.

This article explains how to configure Azure Table connectivity in Connect AI, generate the required personal access token, configure the Connect AI MCP Server in Windsurf, and verify the integration by querying live Azure Table data from the Cascade chat.

Step 1: Configure Azure Table connectivity for Windsurf

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

1. Log into Connect AI, click Sources, and then click Add Connection
👁 Adding a connection in Connect AI

2. Select Azure Table from the Add Connection panel
👁 Selecting data source

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.

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

Add a Personal Access Token

A Personal Access Token (PAT) is used to authenticate the connection to Connect AI from Windsurf. 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
👁 Creating a new PAT

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, Windsurf can now connect to Azure Table data.

Step 2: Configure Connect AI MCP in Windsurf

Next, configure the Connect AI Remote MCP Server in Windsurf so that the Cascade agent can discover and call live data tools through Connect AI.

1. Download and install the Windsurf IDE
2. Open Windsurf, click your profile icon in the top right, and select Windsurf Settings 👁 Opening Windsurf Settings from the profile menu
   
3. Under the Cascade section, locate MCP Servers and click Open MCP Registry 👁 Opening the MCP Registry from Cascade settings
   
4. In the MCP Marketplace, click Add custom MCP in the top right 👁 Adding a custom MCP server
   
5. This opens the mcp_config.json file. Paste the following JSON:

   {
    "mcpServers": {
    "cdata-mcp": {
    "serverUrl": "https://mcp.cloud.cdata.com/mcp",
    "headers": {
    "Authorization": "Basic your_base64_encoded_email_PAT",
    "Content-Type": "application/json"
    }
    }
    }
   }
   		

   Note: Windsurf 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==

   👁 Pasting Connect AI MCP Server configuration
   
6. Save the mcp_config.json file and return to the MCP Registry
7. Under Installed, confirm that cdata-mcp is listed and marked as Enabled 👁 Confirming cdata-mcp is installed and enabled
   

With the MCP server registered and enabled, Windsurf is ready to query live Azure Table data through Connect AI.

Step 3: Query live Azure Table data from Windsurf

With the integration complete, use the Cascade chat panel in Windsurf to interact with live Azure Table data through natural language prompts.

1. On the top bar of Windsurf, switch from Editor to Agent to open a new Cascade chat
2. At the bottom of the chat panel, confirm that the cdata-mcp server is listed and the toggle is enabled 👁 Enabling the cdata-mcp server in the Cascade chat
   
3. Start interacting with the agent by entering prompts like:
   • List all catalogs in my cdata-mcp connection
   • Show the available schemas and tables for Azure Table
   • Query the top 5 records from a table in Azure Table data
4. The Cascade agent calls the Connect AI MCP Server and returns live results from Azure Table data 👁 Querying live data from Cascade in Windsurf
   

At this point, your Windsurf IDE communicates with the Connect AI MCP Server and retrieves live Azure Table data through remote MCP directly from the editor.

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