How to Connect Amazon S3 Data to Gemini Enterprise via CData Connect AI

Gemini Enterprise is Google's enterprise AI assistant, available as part of Google Workspace. With native support for Custom MCP Server data stores, Gemini Enterprise can be extended to query and act on live enterprise data via the Model Context Protocol (MCP). When combined with CData Connect AI Remote MCP, Gemini Enterprise can interact with Amazon S3 data in real time using natural language — without data replication or custom integration logic.

CData Connect AI offers a dedicated cloud-to-cloud interface for connecting to Amazon S3 data via a single managed MCP endpoint. The CData Connect AI Remote MCP Server enables secure communication between Gemini Enterprise and Amazon S3, allowing users to ask questions and take actions on live Amazon S3 data through natural language prompts.

This article explains how to connect Gemini Enterprise to live Amazon S3 data through CData Connect AI by creating a Custom MCP Server data store — giving users access to Amazon S3 data directly from the Gemini Enterprise chat interface.

Prerequisites

1. A CData Connect AI account with at least one active connection (e.g., Amazon S3)
2. A Gemini Enterprise account (trial available)
3. A Google Cloud project with billing enabled
4. The Google Cloud CLI installed and configured
5. In your Google Cloud account:
   • Override the organization policy for Custom MCP data stores (learn more).
   • Grant the Discovery Engine Editor role to the administrator (learn more).

Step 1: Configure Amazon S3 connectivity for Gemini Enterprise

Connectivity to Amazon S3 from Gemini Enterprise is made possible through CData Connect AI Remote MCP. To interact with Amazon S3 data from Gemini Enterprise, start by creating and configuring a Amazon S3 connection in CData Connect AI.

1. Log into Connect AI, click Sources, and then click Add Connection
👁 Adding a Connection

2. Select "Amazon S3" from the Add Connection panel
👁 Selecting a data source

3. Enter the necessary authentication properties to connect to Amazon S3.

   To authorize Amazon S3 requests, provide the credentials for an administrator account or for an IAM user with custom permissions. Set AccessKey to the access key Id. Set SecretKey to the secret access key.

   Note: You can connect as the AWS account administrator, but it is recommended to use IAM user credentials to access AWS services.

   For information on obtaining the credentials and other authentication methods, refer to the Getting Started section of the Help documentation.

   👁 Configuring a connection (Salesforce is shown)
   
4. Click Save & Test
5. Navigate to the Permissions tab in the Add Amazon S3 Connection page and update the User-based permissions. 👁 Updating permissions
   

Create an OAuth App in CData Connect AI

Gemini Enterprise uses OAuth 2.0 Authorization Code with PKCE to authenticate users against the CData Connect AI MCP Server. This requires creating a user-based OAuth App in your CData Connect AI account.

1. Click the Gear icon () in the top-right corner of Connect AI to open Settings.
2. Navigate to OAuth Apps and click + Create App. The Create OAuth App dialog appears.
3. Enter the following settings:
   • Name — Enter a descriptive name (e.g., GeminiEnterpriseOAuth).
   • Authentication Flow — Select User-based (Authorization Code).
   • Callback URL — Enter https://vertexaisearch.cloud.google.com/oauth-redirect.
   👁 Creating a new OAuth App in CData Connect AI
   
4. Click Confirm. CData Connect AI creates the OAuth App and generates a Client ID and Client Secret.
5. Copy both the Client ID and Client Secret values. You will need them in Step 5. 👁 Copying the Client ID and Client Secret
   

With the connection configured and an OAuth App created, we are ready to create the custom MCP server data store in Gemini Enterprise.

Step 2: Create the custom MCP server data store

1. Open Gemini Enterprise and navigate to the Data stores screen.
2. Click Create data store.
3. On the Select a data source page, enter Custom MCP Server in the Search sources field. The Custom MCP Server card displays.
4. Click Add MCP server. The MCP Server Configuration page displays.
5. In the Authentication settings section, enter values in the following required fields:
   • MCP Server URL: https://mcp.cloud.cdata.com/mcp
   • Authorization URL: https://cloud-login.cdata.com/authorize
   • Token URL: https://cloud-login.cdata.com/oauth/token
   • Client ID and Client Secret: From the OAuth App created in Step 1
   👁 Entering MCP Server configuration details
   
6. Click Login, and complete the sign-in.
7. Click Continue, and the Advanced options section opens.

8. In the MCP Server Description field, enter a description that helps Gemini Enterprise understand what the server does and when to use it. For more information, see Write effective MCP server descriptions and instructions.

9. Click Continue.

10. In the Configure your data connector section, select the Location of your data connector from the Multi-region field list.

11. In Your data connector name, enter a name for your data store.

12. Click Create. Gemini Enterprise creates your data store and displays your data stores on the Data Stores page.

    Note: By default, no tools or actions from your custom MCP servers are enabled. You must enable the tools or actions.

Step 3: Enable actions

After creating the custom MCP server data store, you must enable at least one tool or action before it can be used in Gemini Enterprise.

1. Go to your custom MCP server data store.

2. Open the Actions tab and select Reload custom actions to reauthenticate.

   Note: This action performs a tools/list call on the MCP server to retrieve available tools, which are then displayed on the screen.

3. Select the actions to enable.
4. Click Enable actions. 👁 Enabling actions
   

Step 4: Connect the MCP server data store to a Gemini Enterprise app

After creating the custom MCP server data store and enabling actions, you must connect the data store to a Gemini Enterprise app before it can be used.

1. In the Google Cloud console, go to the Gemini Enterprise page.
2. From the navigation menu, click Apps.
3. Select the Gemini Enterprise app where you want to connect your data store.
4. From the navigation menu of the app, click Connected data sources.
5. Click Add existing data stores and select your data store.
6. Click Connect. 👁 Connecting data store
   

Step 5: Query live Amazon S3 data with natural language

With the data store connected, Gemini Enterprise users can interact with live Amazon S3 data using natural language from the Gemini Enterprise web application. Each user authenticates with their own Connect AI credentials via the OAuth flow on first use.

1. Open Gemini Enterprise, click Connections and authorize CData Connect AI. 👁 Authorizing CData Connect AI
   
2. Ask natural language questions about your Amazon S3 data:
   • "Show me all Amazon S3 data from the last 30 days"
   • "What are the top records in Amazon S3 data by revenue?"
   • "List all active Amazon S3 data and their current status"
   • "Summarize Amazon S3 data activity for this quarter"
3. The agent automatically discovers available connections in Connect AI, identifies the most relevant Amazon S3 connection, generates SQL, and returns results — all without requiring the user to write queries or understand the underlying data structure.

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