As organizations adopt AI across their business, one challenge quickly emerges: how can AI securely access company-specific data without creating custom integrations for every application?
Gemini Enterprise addresses this challenge through support for the Model Context Protocol (MCP), an open standard that enables AI models to securely connect to business systems, databases, APIs, and internal tools. Whether you’re building enterprise AI workflows or developing custom AI applications, understanding MCP is an important step toward unlocking the full potential of Gemini Enterprise.
What Is the Model Context Protocol (MCP)?
The Model Context Protocol (MCP) is an open standard that provides a consistent way for AI models to communicate with external systems.
Rather than creating separate integrations for every application, MCP acts as a universal adapter between AI models and your organization’s infrastructure. This allows Gemini Enterprise to retrieve information securely while maintaining centralized authentication and governance.
Common Use Cases
- Retrieving information from internal databases (e.g., BigQuery, Cloud Spanner)
- Connecting to enterprise APIs and SaaS tools (e.g., Gong, Jira, Slack)
- Accessing business applications
- Querying company knowledge bases
- Executing approved business workflows
How to Connect a Custom MCP Server
Connecting Gemini Enterprise to your organization’s systems involves several structured configuration steps. For the latest requirements and configuration details, review Google Cloud’s custom MCP server setup guide before deployment.
1.Create OAuth Credentials:Google Cloud Console.
- Navigate to APIs & Services > Credentials inside your Google Cloud project.
- Click Create Credentials and select OAuth client ID.
- Choose Web Application as the application type.
- Set the Authorized Redirect URI to:
[https://vertexaisearch.cloud.google.com/oauth-redirect](https://vertexaisearch.cloud.google.com/oauth-redirect) - Save and securely store your newly generated Client ID and Client Secret.
2.Deploy Your MCP Server:Cloud Infrastructure.
Deploy your Python-based (using FastMCP wrappers) or Node.js server to a secure, public or private HTTPS endpoint. Ensure your server is configured to expose a StreamableHTTP endpoint, typically structured as:
[https://your-domain.com/mcp](https://your-domain.com/mcp)
3.Create a Data Store:Gemini Enterprise Page.
- In the Google Cloud Console, navigate to the Gemini Enterprise page.
- From the left navigation menu, click Data stores.
- Select Create Data Store.
- Search for and select Custom MCP Server from the sources list.
4.Configure the Connection:Authentication Settings.
Provide your integration details in the setup fields:
- HTTPS Server URL (e.g., [https://your-domain.com/mcp](https://your-domain.com/mcp))
- Authorization URL & Token URL (from your enterprise system’s Identity Provider)
- Client ID & Client Secret (created in Step 1)
- OAuth Scopes required by your external tool
5.Enable Actions:Console Admin Panel.
Once connected, your server’s capabilities are imported as actions:
- Open your custom MCP data store.
- Click Actions > Reload Custom Actions to query the server for its tool schemas.
- Review the discovered tools and manually toggle on the actions you want to make available to users (Gemini supports up to 100 enabled actions per data store).
6.Authorize Access:End-User Flow.
Before Gemini can call the newly enabled actions on behalf of a user:
- The user must enable the specific MCP-backed data store within Gemini Enterprise.
- Upon their first request triggering an action, the user must complete the OAuth sign-in flow to authenticate directly with your MCP server’s third-party Identity Provider (IdP) and authorize access.
Using the Gemini API with MCP
Developers building custom generative AI applications can orchestrate the interaction between the Gemini API and MCP directly:
- User Prompt: A user submits a query to your custom application.
- Context Request: Your application intercepts the request and queries your deployed MCP server for resources or tool execution.
- Data Retrieval: The remote MCP server securely pulls information from your databases, enterprise APIs, or internal directories.
- Context Integration: The MCP server returns structured, real-time context back to your application.
- Model Generation: The application passes both the user’s original prompt and the retrieved context to the Gemini API, generating a highly accurate, grounded response.
Why Use MCP with Gemini Enterprise?
By pairing Gemini Enterprise with the Model Context Protocol, organizations build AI experiences that are secure, governed, and highly context-aware.
- Secure Grounding: Keep private corporate data isolated while allowing models to query it dynamically.
- Unified Standard: No need to build custom API connectors for every new model or app; build once on MCP and reuse.
- Centralized Governance: Leverage OAuth 2.0, Cloud IAM policies, and features like Model Armor to control exactly who can invoke which actions.
Get Started with Gemini Enterprise
Gemini Enterprise becomes even more valuable when it can securely access the systems your organization relies on every day. Model Context Protocol provides a standardized, scalable way to connect AI with your enterprise data, enabling more accurate responses and more powerful workflows.
If you’re planning to deploy Gemini Enterprise or explore advanced integrations like MCP servers, HiView Solutions can help with licensing, implementation, configuration, and adoption so your organization can maximize the value of Google’s AI platform.
Learn more about Gemini Enterprise for Google Workspace today!
