Build Adobe Analytics-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 Adobe Analytics, configuring the connection to Adobe Analytics, connecting the Code Assist MCP add-on to GitHub Copilot, and querying live Adobe Analytics data from within Visual Studio Code.

Prerequisites

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

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

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

Step 2: Configure the connection to Adobe Analytics

1. After installation, open the CData Code Assist MCP for Adobe Analytics configuration wizard

   NOTE: If the wizard does not open automatically, search for "CData Code Assist MCP for Adobe Analytics" 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_adobeanalytics") and click OK

4. Enter the appropriate connection properties in the configuration wizard

   Adobe Analytics uses the OAuth authentication standard. To authenticate using OAuth, create an app to obtain the OAuthClientId, OAuthClientSecret, and CallbackURL connection properties. See the "Getting Started" section of the help documentation for a guide.

   Retrieving GlobalCompanyId

   GlobalCompanyId is a required connection property. If you do not know your Global Company ID, you can find it in the request URL for the users/me endpoint on the Swagger UI. After logging into the Swagger UI Url, expand the users endpoint and then click the GET users/me button. Click the Try it out and Execute buttons. Note your Global Company ID shown in the Request URL immediately preceding the users/me endpoint.

   Retrieving Report Suite Id

   Report Suite ID (RSID) is also a required connection property. In the Adobe Analytics UI, navigate to Admin -> Report Suites and you will get a list of your report suites along with their identifiers next to the name.

   After setting the GlobalCompanyId, RSID and OAuth connection properties, you are ready to connect to Adobe Analytics.

   
5. Click Connect to authenticate with Adobe Analytics
6. Click Save & Test to finalize the connection

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: /.vscode/mcp.json
2. Add the JSON code shown below and save the file

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

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_adobeanalytics").

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

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

Step 4: Query live Adobe Analytics 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
2. Ask questions about your Adobe Analytics data using natural language. For example:

   "List all tables available in my Adobe Analytics data data connection."

   
3. Start building with natural language prompts:

   For my project, data from the AdsReport is very important. Pull data from the most important columns like Page and PageViews.
   

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

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