Integrating Claude Code with Databricks Data via CData Connect AI

Claude Code is an AI-powered development environment that brings intelligent code generation, automation, and interactive reasoning directly into your workflow. By integrating it with CData Connect AI, you can enable Claude Code to securely access, query, and interact with live enterprise data, such as Databricks, through a standardized MCP tool interface.

CData Connect AI is a managed MCP platform that exposes your enterprise data sources through the Model Context Protocol (MCP). This allows Claude Code to work with catalogs, schemas, tables, metadata, and SQL-enabled data access from more than 350 data sources, without requiring ETL pipelines or custom integration code.

This article explains how to register the CData Connect AI MCP endpoint in Claude Code, configure your Databricks or other data source connection, and begin issuing real-time data queries directly from the coding environment. We explore how Claude Code uses the built-in MCP tools, such as getCatalogs, getSchemas, getTables, and queryData to help you write, debug, and automate development workflows powered by live Databricks data securely and interactively.

Getting Started

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Prerequisites

• An account in CData Connect AI
• A Claude Code account.
• Visual Studio Code installed on your system.

Step 1: Configure Databricks connectivity for Claude Code

For Claude Code to access Databricks, create a connection to Databricks in CData Connect AI. This connection is then exposed to Claude Code using the remote MCP server.

1. Log in to Connect AI click Sources, and then click + Add Connection
2. From the available data sources, choose Databricks
3. Enter the necessary authentication properties to connect to Databricks

   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).
   
4. Click Save & Test
5. Once authenticated, open the Permissions tab in the Databricks connection and configure user-based permissions as required

Generate a Personal Access Token (PAT)

Claude Code authenticates to Connect AI using an account email and a Personal Access Token (PAT). Creating separate PATs for each integration is recommended to maintain access control granularity.

1. In Connect AI, select the Gear icon in the top-right to open Settings
2. Under Access Tokens, select Create PAT
3. Provide a descriptive name for the token and select Create
4. Copy the token and store it securely. The PAT will only be visible during creation

With the Databricks connection configured and a PAT generated, Claude Code is prepared to connect to Databricks data through the CData MCP server.

Step 2: Install Claude Code

Claude Code is distributed as an npm package. You can install it globally.

To install Claude Code on your system, open PowerShell, Terminal, or CMD as an Administrator and run:

npm install -g @anthropic-ai/claude-code

Verify the installation using the following command:

npm list -g @anthropic-ai/claude-code

Expected output should be:

C:\Users\User\AppData\Roaming
pm
`-- @anthropic-ai/[email protected]

Step 3: Authenticate Claude Code with Claude.ai

Link your local Claude Code environment with your Claude.ai account to enable secure access. In the terminal, run:

claude login

Claude Code outputs a URL, like:

Please visit https://claude.ai/login?code=

Follow these steps:

1. Click the URL or paste it into your browser.
2. Log in to Claude.ai.
3. Claude.ai displays a verification code.
4. Return to your terminal and enter/paste the provided verification code when prompted.

Once verified, you'll need to authenticate with Claude Code using an authentication code. Once done, your terminal should display:

You're all set up for Claude Code.

Claude Code is now linked to your Claude.ai account.

Step 4: Create a Claude Code project

To set up a workspace where Claude Code can store MCP configuration files, start by creating a new directory:

mkdir ClaudeCode
cd ClaudeCode

Now, open it in Visual Studio Code:

code .

Step 5: Launch Claude Code and register the CData Connect AI MCP server

Before Claude Code can interact with Databricks, you must register your CData Connect AI MCP endpoint. Claude Code uses this remote MCP server to securely access metadata, schemas, tables, and live query results.

Now register the CData Connect AI MCP server by running the following command in your Claude Code project directory:

claude mcp add connectmcp https://mcp.cloud.cdata.com/mcp \
  --transport http \
  --header "Authorization: Basic base64encoded(EMAIL:PAT)" \
  --header "Content-Type: application/json"

Once added, verify that Claude recognizes your MCP server:

claude mcp list

If successful, you should see:

connectmcp: https://mcp.cloud.cdata.com/mcp (HTTP) - ✓ OK

Start the Claude Code assistant and verify that it detects your MCP server. To run, use the given command:

claude

Once Claude Code loads, you should see:

Loaded MCP Server: connectmcp

This confirms that Claude Code is now connected to your CData Connect AI instance.

Step 6: Explore Databricks metadata

You can now use Claude Code's natural-language interface to list catalogs, schemas, and tables in Databricks. Ask Claude:

List all Databricks catalogs using getCatalogs.

Claude automatically calls the appropriate MCP tool when you issue a request.

Try additional queries such as:

• "Show the available schemas."
• "List all tables in the Databricks connection."
• "Retrieve the top 10 records from the Account table."

Claude Code uses the following MCP tools to interact with Databricks in real time:

• getCatalogs
• getSchemas
• getTables
• queryData

These tools allow Claude Code to retrieve metadata and query live Databricks data.

Step 7: Generate code and automation workflows

Use real Databricks metadata to build working scripts directly inside your IDE.

Example prompt:

Write a Python script that queries Salesforce Contacts where LastName starts with 'A' using the MCP queryData tool.

Claude Code writes accurate code because it has:

• direct access to Databricks schemas
• live query testing
• metadata introspection

All delivered through CData Connect AI.

Step 8: Build data-driven development workflows

Use Claude Code to generate, refine, and automate code that works with your Databricks data using CData Connect AI.

With the CData Connect AI integration in place, Claude Code can help you build development workflows that rely on your Databricks data. Although Claude Code does not include built-in real-time data connectivity, your configured MCP connection through CData Connect AI provides it with access to the metadata and query results for your request.

You can use Claude Code to automate tasks such as:

• generating scripts for data exploration
• creating integration test scaffolding
• validating queries against your Databricks schema
• producing code for data extraction or transformation workflows

In this setup, Claude Code acts as an intelligent coding assistant that uses live Databricks data from CData Connect AI to help you write and refine data-driven logic.

Optional: Manage MCP integrations

Add, remove, or inspect MCP servers in your project.

List MCP servers using the following command:

claude mcp list

To remove one, use:

claude mcp remove connectmcp

Modify the config by editing:

.claude/mcp.json

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