Build Azure Analysis Services-Powered Applications in Gemini Code Assist with CData Code Assist MCP

Gemini Code Assist is an AI-powered coding companion that integrates intelligent code generation into everyday development workflows. With support for MCP, Gemini Code Assist can connect to live enterprise data sources directly from Visual Studio Code, enabling natural language interaction with structured data without switching context or manually writing data access code.

Model Context Protocol (MCP) is an open standard for connecting LLM clients to external services through structured tool interfaces. MCP servers expose capabilities such as schema discovery and live querying, allowing AI agents to retrieve and reason over real-time data safely and consistently.

This guide explains how to install the CData Code Assist MCP for Azure Analysis Services, configure the connection to Azure Analysis Services, connect the Code Assist MCP add-on to Gemini Code Assist, and query live Azure Analysis Services data from within the editor.

Prerequisites

Before starting, ensure the following requirements are met:

1. Visual Studio Code is installed on the machine
2. Gemini Code Assist extension is enabled in Visual Studio Code
3. CData Code Assist MCP for Azure Analysis Services has been installed
4. Access to Azure Analysis Services

Note: Gemini Code Assist must already be set up and functional in Visual Studio Code before configuring MCP servers. MCP servers are accessed when Gemini Code Assist is running in Agent mode.

Step 1: Download and install the CData Code Assist MCP for Azure Analysis Services

1. To begin, download the CData Code Assist MCP for Azure Analysis Services 👁 Downloading the CData Code Assist MCP add-on installer
   
2. Find and double-click the installer to begin the installation
3. Run the installer and follow the prompts to complete the installation 👁 Completing the CData Code Assist MCP add-on installation
   

When the installation is complete, the Code Assist MCP add-on is ready for configuration by connecting to Azure Analysis Services.

Step 2: Configure the connection to Azure Analysis Services

1. After installation, open the CData Code Assist MCP for Azure Analysis Services configuration wizard 👁 The CData Code Assist MCP add-on configuration wizard
   

   NOTE: If the wizard does not open automatically, search for "CData Code Assist MCP for Azure Analysis Services" in the Windows search bar and open the application.

   👁 Opening the CData Code Assist MCP add-on configuration wizard
   
2. In MCP Configuration > Configuration Name, either select an existing configuration or choose to create a new one 👁 Creating a new configuration
   
3. Name the configuration (e.g. "cdata_aas") and click OK 👁 Naming the configuration
   

4. Enter the appropriate connection properties in the configuration wizard

   To connect to Azure Analysis Services, set the Url property to a valid server, for instance, asazure://southcentralus.asazure.windows.net/server, in addition to authenticating. Optionally, set Database to distinguish which Azure database on the server to connect to.

   Azure Analysis Services uses the OAuth authentication standard. OAuth requires the authenticating user to interact with Azure Analysis Services using the browser. You can connect without setting any connection properties for your user credentials. See the Help documentation for more information.

   👁 Configuring the Code Assist MCP add-on connection
   
5. Click Connect to authenticate with Azure Analysis Services through OAuth
6. Then, click Save Configuration to save the Code Assist MCP add-on 👁 Saving the Code Assist MCP add-on configuration
   

This process creates a .mcp configuration file that Gemini Code Assist will reference when launching the Code Assist MCP add-on. With the Code Assist MCP add-on configured, it is ready to connect to Gemini Code Assist.

Step 3: Connect the Code Assist MCP add-on to Gemini Code Assist

1. Ensure Visual Studio Code is installed and the Gemini Code Assist extension is enabled
2. From the configuration wizard, click Next after saving and testing the connection 👁 Selecting 'Next'
   
3. Select Gemini Code Assist from the AI MCP Tool dropdown 👁 Choosing Gemini Code Assist as the AI MCP tool
   
4. Click Copy JSON to copy the generated MCP configuration to the clipboard 👁 Copying the MCP configuration
   
5. Paste the copied JSON into the appropriate configuration file based on the desired scope:
   • User-level: Configuration applies across all projects for the current user
   • Workspace-level: Configuration applies only to the current workspace or project

   NOTE: The configuration includes the path to Java 17+ executable and the CData Code Assist MCP add-on JAR file. The final argument must match the MCP configuration name saved in the wizard (e.g. "cdata_aas").

6. Save the configuration file and restart Visual Studio Code if necessary

Step 4: Query live Azure Analysis Services data in Gemini Code Assist

1. Open Visual Studio Code and select Gemini Code Assist in the activity bar 👁 Open Gemini Code Assist
   
2. Enter /mcp in the chat prompt to verify the connection status. The Azure Analysis Services Code Assist MCP add-on should appear with a green connection indicator 👁 Verify the connection status
   
3. Ask questions about Azure Analysis Services data using natural language. For example:

   "Provide the list of all tables available in my Azure Analysis Services data connection."

   👁 Querying live data from Gemini Code Assist
   
4. Generate code that works with live Azure Analysis Services data. For example:

   "Write a function to retrieve records from the Customer table where Country matches a given value."

   👁 Generating function for live data from Gemini Code Assist
   

Gemini Code Assist is now fully integrated with the CData Code Assist MCP add-on and can use the MCP tools exposed to explore schemas, execute live queries against Azure Analysis Services, and generate data-aware code.

Build with Code Assist MCP. Deploy with CData Drivers.

Download Code Assist MCP for free and give your AI tools schema-aware access to live Azure Analysis Services data during development. When you're ready to move to production, CData Azure Analysis Services Drivers deliver the same SQL-based access with enterprise-grade performance, security, and reliability.

Visit the CData Community to share insights, ask questions, and explore what's possible with MCP-powered AI workflows.