Build Hugging Face-Powered Applications in Gemini Code Assist with CData MCP Server

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 API Driver for MCP Server, configure the connection to Hugging Face, connect the MCP Server add-on to Gemini Code Assist, and query live Hugging Face 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 API Driver for MCP Server has been installed
4. Access to Hugging Face

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 API Driver for MCP Server

1. To begin, download the CData API Driver for MCP Server
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 MCP Server add-on is ready for configuration by connecting to Hugging Face.

Step 2: Configure the connection to Hugging Face

1. After installation, open the CData API Driver for MCP Server configuration wizard

   NOTE: If the wizard does not open automatically, search for "CData API Driver for MCP Server" 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_api") and click OK

4. Enter the appropriate connection properties in the configuration wizard

   HuggingFace Hub uses token-based authentication to enable access to its API. The API provides access to machine learning models, datasets, spaces, papers, and other resources on the HuggingFace Hub platform.

   Using API Key Authentication

   To authenticate to HuggingFace Hub, you will need to provide an API Key (Access Token). To obtain your access token:

   1. Log in to your HuggingFace account at https://huggingface.co
   2. Navigate to Settings > Access Tokens
   3. Click "New token" to create a new access token
   4. Select the appropriate permissions (read or write)
   5. Copy the token value

   After obtaining your access token, set the following connection properties:

   • AuthScheme: Set this to APIKey.
   • APIKey: Set this to your HuggingFace access token.

   Example connection string

   Profile=C:\profiles\HuggingFace.apip;ProfileSettings='APIKey=hf_xxxxxxxxxxxxxxxxxxxx';
   

   
5. Click Connect to authenticate with Hugging Face
6. Then, click Save Configuration to save the MCP Server add-on

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

Step 3: Connect the MCP Server 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 MCP Server add-on JAR file. The final argument must match the MCP configuration name saved in the wizard (e.g. "cdata_api").

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

Step 4: Query live Hugging Face 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 Hugging Face MCP Server add-on should appear with a green connection indicator
3. Ask questions about Hugging Face data using natural language. For example:

   "Provide the list of all tables available in my Hugging Face data connection."

   
4. Generate code that works with live Hugging Face data. For example:

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

   

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

Build with MCP Server. Deploy with CData Drivers.

Download MCP Server for free and give your AI tools schema-aware access to live Hugging Face data during development. When you're ready to move to production, CData Hugging Face 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.