Build Azure Data Lake Storage-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 walks through installing the CData Code Assist MCP for Azure Data Lake Storage, configuring the connection to Azure Data Lake Storage, connecting the Code Assist MCP add-on to Gemini Code Assist, and querying live Azure Data Lake Storage 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 Data Lake Storage has been installed
4. Access to Azure Data Lake Storage

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 Data Lake Storage

1. To begin, download the CData Code Assist MCP for Azure Data Lake Storage
2. Find and double-click the installer to begin the installation
3. Run the installer and follow the prompts to complete the installation

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

Step 2: Configure the connection to Azure Data Lake Storage

1. After installation, open the CData Code Assist MCP for Azure Data Lake Storage configuration wizard

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

   
2. In MCP Configuration > Configuration Name, either select an existing configuration or choose to create a new one
3. Name the configuration (e.g. "cdata_adls") and click OK

4. Enter the appropriate connection properties in the configuration wizard

   Authenticating to a Gen 1 DataLakeStore Account

   Gen 1 uses OAuth 2.0 in Entra ID (formerly Azure AD) for authentication.

   For this, an Active Directory web application is required. You can create one as follows:

   1. Sign in to your Azure Account through the .
   2. Select "Entra ID" (formerly Azure AD).
   3. Select "App registrations".
   4. Select "New application registration".
   5. Provide a name and URL for the application. Select Web app for the type of application you want to create.
   6. Select "Required permissions" and change the required permissions for this app. At a minimum, "Azure Data Lake" and "Windows Azure Service Management API" are required.
   7. Select "Key" and generate a new key. Add a description, a duration, and take note of the generated key. You won't be able to see it again.

   To authenticate against a Gen 1 DataLakeStore account, the following properties are required:

   • Schema: Set this to ADLSGen1.
   • Account: Set this to the name of the account.
   • OAuthClientId: Set this to the application Id of the app you created.
   • OAuthClientSecret: Set this to the key generated for the app you created.
   • TenantId: Set this to the tenant Id. See the property for more information on how to acquire this.
   • Directory: Set this to the path which will be used to store the replicated file. If not specified, the root directory will be used.

   Authenticating to a Gen 2 DataLakeStore Account

   To authenticate against a Gen 2 DataLakeStore account, the following properties are required:

   • Schema: Set this to ADLSGen2.
   • Account: Set this to the name of the account.
   • FileSystem: Set this to the file system which will be used for this account.
   • AccessKey: Set this to the access key which will be used to authenticate the calls to the API. See the property for more information on how to acquire this.
   • Directory: Set this to the path which will be used to store the replicated file. If not specified, the root directory will be used.
   
5. Click Connect to authenticate with Azure Data Lake Storage through OAuth
6. Then, click Save Configuration to save the Code Assist MCP add-on

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
3. Select Gemini Code Assist from the AI MCP Tool dropdown
4. Click Copy JSON to copy the generated MCP configuration to the clipboard
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_adls").

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

Step 4: Query live Azure Data Lake Storage data in Gemini Code Assist

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

   "Provide the list of all tables available in my Azure Data Lake Storage data connection."

   
4. Generate code that works with live Azure Data Lake Storage data. For example:

   "Write a function to retrieve records from the Resources table where FullPath matches a given value."

   

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 Data Lake Storage, 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 Data Lake Storage data during development. When you're ready to move to production, CData Azure Data Lake Storage 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.