Build Databricks-Powered Applications in GitHub Copilot with CData Code Assist MCP

GitHub Copilot is an AI-powered coding assistant that integrates directly into Visual Studio Code and other IDEs. With support for MCP, GitHub Copilot can connect to local tools and enterprise data sources, enabling natural language interaction with live systems during development.

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.

In this article, we guide you through installing the CData Code Assist MCP for Databricks, configuring the connection to Databricks, connecting the Code Assist MCP add-on to GitHub Copilot, and querying live Databricks data from within Visual Studio Code.

Getting Started

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Prerequisites

• Visual Studio Code is installed on your machine
• GitHub Copilot Chat extension is enabled in Visual Studio Code
• CData Code Assist MCP for Databricks has been installed

Step 1: Download and install the CData Code Assist MCP for Databricks

1. To begin, download the CData Code Assist MCP for Databricks 👁 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, you are ready to configure your Code Assist MCP add-on by connecting to Databricks.

Step 2: Configure the connection to Databricks

1. After installation, open the CData Code Assist MCP for Databricks 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 Databricks" in the Windows search bar and open the application.

   👁 Opening the CData Code Assist MCP add-on configuration wizard (Google Sheets is shown).
   
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_databricks") and click OK 👁 Naming the configuration
   

4. Enter the appropriate connection properties in the configuration wizard

   To connect to a Databricks cluster, set the properties as described below.

   Note: The needed values can be found in your Databricks instance by navigating to Clusters, and selecting the desired cluster, and selecting the JDBC/ODBC tab under Advanced Options.

   • Server: Set to the Server Hostname of your Databricks cluster.
   • HTTPPath: Set to the HTTP Path of your Databricks cluster.
   • Token: Set to your personal access token (this value can be obtained by navigating to the User Settings page of your Databricks instance and selecting the Access Tokens tab).
   👁 Configuring the Code Assist MCP add-on connection
   
5. Click Connect to authenticate with Databricks
6. Click Save & Test to finalize the connection 👁 Saving and testing the Code Assist MCP add-on configuration
   

This process creates a .mcp configuration file that GitHub Copilot will reference when launching the Code Assist MCP add-on. Now with your Code Assist MCP add-on configured, you are ready to connect it to GitHub Copilot.

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

1. Download and install Visual Studio Code and enable the GitHub Copilot Chat extension

Option 1: Manually add the MCP configuration

1. Open or create the mcp.json file:
   • For global configuration: %%APPDATA%%/Roaming/Code/User/mcp.json
   • For project-specific configuration:
2. Add the JSON code shown below and save the file

{
 "servers": {
 "cdata_databricks": {
 "command": "C:\Program Files\CData\CData Code Assist MCP for Databricks\jre\bin\java.exe",
 "args": [
 "-Dfile.encoding=UTF-8",
 "-jar",
 "C:\Program Files\CData\CData Code Assist MCP for Databricks\lib\cdata.mcp.databricks.jar",
 "cdata_databricks"
 ]
 }
 }
}

NOTE: The command value should point to your Java 17+ java.exe executable, and the JAR path should point to the installed CData Code Assist MCP add-on .jar file. The final argument must match the MCP configuration name you saved in the CData configuration wizard (e.g. "cdata_databricks").

Option 2: Copy the MCP configuration from the CData Code Assist MCP for Databricks UI

1. After saving and testing your connection in the configuration wizard, click Next 👁 Selecting 'Next'
   
2. Select Github Copilot from the AI MCP Tool dropdown 👁 Choosing GitHub Copilot as the AI MCP tool
   
3. Follow the MCP Client Instructions to create the required configuration file 👁 MCP configuration instructions for GitHub Copilot
   
4. Copy the displayed JSON code and paste it into your configuration file

Step 4: Query live Databricks data in GitHub Copilot

1. Launch Visual Studio Code and open the GitHub Copilot Chat interface. Select the tool icon to enable the configured Code Assist MCP add-on 👁 Enabling the CData Code Assist MCP add-on in GitHub Copilot
   
2. Ask questions about your Databricks data using natural language. For example:

   "List all tables available in my Databricks data data connection."

   👁 Querying live data from GitHub Copilot
   
3. Start building with natural language prompts:

   For my project, data from the Customers is very important. Pull data from the most important columns like City and CompanyName.
   

GitHub Copilot is now fully integrated with CData Code Assist MCP for Databricks and can use the MCP tools to explore schemas and execute live queries against Databricks.

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 Databricks data during development. When you're ready to move to production, CData Databricks 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.