Integrate Gemini Cloud Assist with third-party tools using MCP

This document shows you how to integrate Gemini Cloud Assist and Application Design Center with third-party tools—including Antigravity, Gemini CLI, and Cursor—using Model Context Protocol (MCP).

MCP lets you integrate Gemini Cloud Assist and App Design Center's agentic capabilities directly into your preferred third-party tools and custom workflows so that you can access cloud operations expertise exactly where you already work. For more details about MCP, reference Configure MCP for your AI application.

Gemini Cloud Assist MCP tools

Gemini Cloud Assist is an agent that is accessible through a set of MCP tools using the agents-as-tools pattern. Unlike tools that fetch data or do procedural processing, the Gemini Cloud Assist tools expose agentic functionality of all of Gemini Cloud Assist or of specific workflows. The agent invoked by MCP tool calls makes its own tool calls internally to Google Cloud. The following MCP tools are published for agents to consume:

  • ask_cloud_assist: This is the primary interface for Google Cloud assistance and for the Gemini Cloud Assist agent. All functionality of Gemini Cloud Assist is accessible through this tool and it encompasses the functionality of the other MCP tools.
  • design_infra: This supports workflows for designing and architecting infrastructure on Google Cloud. It works with App Design Center MCP server tools to manage your infrastructure design lifecycle—for example, run and remediate security findings, or deploy and remediate issues.
  • investigate_issue: This supports workflows for troubleshooting in Google Cloud. It can do quick troubleshooting or deeper troubleshooting through an Investigation resource.
  • invoke_operation: This supports workflows for creating, updating, and deleting resources in Google Cloud. This tool is only functional when Agent Actions are enabled. Write operations in Gemini Cloud Assist can only be executed through the invocation of this tool.
  • optimize_costs: This supports workflows for analyzing, tracking, and optimizing Google Cloud costs. It provides detailed breakdowns of spending and identifies opportunities for cost efficiency by finding idle or underutilized resources.

App Design Center MCP tools

These tools, often orchestrated by Gemini Cloud Assist's design_infra tool, manages infrastructure application lifecycles using App Design Center, including template management, security compliance and remediation, and deployment. The tools are:

  • setup_adc: Initializes the App Design Center environment. This is a one-time setup step that must be performed before other App Design Center tools can be used.
  • manage_application: Manages the deployment lifecycle of an application. Use this tool to deploy a verified design using the App Design Center or retrieve the status and details of an existing deployment. This is the final step that turns your design into a deployed infrastructure on Google Cloud.
  • manage_application_template: Manages the Infrastructure as Code (IaC) content of your infrastructure design. Use this to save the design as an App Design Center template, export the design as Terraform files, or update the design. For example, you can use this tool to update the design by modifying component settings and parameter configurations.
  • assess_best_practices: Performs a comprehensive security and configuration audit on your application design before deployment. It validates the design against Security Command Center frameworks and relevant controls, returning a report with actionable findings for remediation.
  • list_application_templates: Lists all available application designs.

These MCP tools shouldn't be treated as stable APIs or configured through procedural API clients. Tools from this list or their parameters might be renamed, re-defined, removed, or added over time to account for the evolving capabilities of Gemini Cloud Assist. The capabilities of Gemini Cloud Assist will remain stable so that client agents are able to consistently execute the same tasks, but the way in which agents interact with and invoke Gemini Cloud Assist through these tools will change over time.

Before you begin

  1. Grant access: To use MCP, you need to be granted an Identity and Access Management role that contains the mcp.googleapis.com/tools.call permission, such as one of the following Gemini Cloud Assist roles:

    • roles/geminicloudassist.user
    • roles/geminicloudassist.editor
    • roles/geminicloudassist.admin

  2. If you haven't already done so, install the gcloud CLI and its beta components.

  3. Enable the MCP service in your project with the following command:

    gcloud beta services mcp enable geminicloudassist.googleapis.com \
    --project=PROJECT_ID

    Replace PROJECT_ID with your Google Cloud project ID.

  4. Authenticate using Application Default Credentials by running the following command:

    gcloud auth application-default login

Configure MCP for your client

The client-agent configuration depends on which agent you are using. The following sections provide instructions to configure MCP for different clients.

To enable the full design-to-deploy workflow, you must configure your client to connect to both the Gemini Cloud Assist and App Design Center MCP servers and include the relevant SKILL.md file. The following snippets show the combined configuration.

Gemini CLI

Configure using an extension

Install the MCP server as a Gemini CLI extension using the following command:

gemini extensions install https://github.com/GoogleCloudPlatform/gemini-cloud-assist-mcp

Configure manually

  1. Configure the MCP server settings.

    Open your Gemini CLI user-scope settings file at ~/.gemini/settings.json or your workspace-scope settings at .gemini/settings.json. If the file doesn't exist, create it. Add or merge the following configuration block under the mcpServers key. Ensure the file uses valid JSON format. This configuration automatically uses your Application Default Credentials. 

    {
  "mcpServers": {
    "gemini_cloud_assist": {
      "httpUrl": "https://geminicloudassist.googleapis.com/mcp",
      "authProviderType": "google_credentials",
      "oauth": {
        "scopes": ["https://www.googleapis.com/auth/cloud-platform"]
      },
      "timeout": 600000
    },
    "application_design_center": {
      "httpUrl": "https://designcenter.googleapis.com/mcp",
      "authProviderType": "google_credentials",
      "oauth": {
        "scopes": ["https://www.googleapis.com/auth/cloud-platform"]
      },
      "timeout": 600000
    }
  }
}

  2. Create and populate your skill files.

    Create a SKILL.md file in one of the following directories. Copy and paste the markdown content for your chosen skill from the skill reference section of this document into the file.

    • Workspace scope: .gemini/skills/<skill-name>/
    • User scope: ~/.gemini/skills/<skill-name>/

    The following is an example directory structure:

    .gemini/
└── skills/
    ├── design-and-deploy/
    │   └── SKILL.md
    └── google-gemini-cloud-assist/
        └── SKILL.md

  3. Verify your skills.

    Once you have modified your settings.json and SKILL.md files, restart Gemini CLI. Use the following commands to verify:

    • /skills list: To verify your local skills have been successfully discovered.
    • /mcp list: To verify the Gemini Cloud Assist and Application Design Center MCP servers are connected.

Antigravity

  1. Install the extension.

    In Antigravity, open the Extensions marketplace. Search for the Gemini Cloud Assist extension, click Install, and then click Refresh.

  2. Configure the MCP servers manually.

    Open the Manage MCP Servers pane and click View raw config to edit the mcp_config.json file. Add or merge the following configuration block. This setup uses yuor Application Default Credentials for authentication.

    {
  "mcpServers": {
    "gemini_cloud_assist": {
      "serverUrl": "https://geminicloudassist.googleapis.com/mcp",
      "headers": {},
      "authProviderType": "google_credentials"
    },
    "application_design_center": {
      "serverUrl": "https://designcenter.googleapis.com/mcp",
      "headers": {},
      "authProviderType": "google_credentials"
    }
  }
}

  3. Verify your setup.

    Once you have modified your mcp_config.json and SKILL.md files, restart Antigravity. In the Agent pane, use the following slash commands to verify the connection:

    • /skills list: Verifies that your local skills have been discovered.
    • /mcp list: Verifies that the Gemini Cloud Assist and Application Design Center MCP servers are connected.

Cursor

  1. In your Google Cloud project, create an OAuth 2.0 client ID for a desktop app.

  2. Configure URI://anysphere.cursor-mcp/oauth/callback as the redirect URL. For more information, see the Cursor documentation.

  3. Add or merge the following configuration block:

       {
     "mcpServers": {
       "gemini_cloud_assist": {
         "url": "https://geminicloudassist.googleapis.com/mcp",
         "auth": {
           "CLIENT_ID": "${env:OAUTH_CLIENT_ID}",
           "CLIENT_SECRET": "${env:OAUTH_CLIENT_SECRET}",
           "scopes": ["https://www.googleapis.com/auth/cloud-platform"]
         }
       }
     }
   }

Gemini Cloud Assist skills

The Gemini Cloud Assist and App Design Center MCP tools use agent skills to instruct your agent. The skill guides your agent to perform the following tasks:

  • Chain together multiple tools into a workstream.
  • Process relevant local information, such as Terraform deployment code, to reason about your local context.
  • Respond to explicit invocations when you reference the skill directly.

When using interactive agents like Antigravity or Gemini CLI, you can integrate Gemini Cloud Assist MCP tools with other agents and workflows. Because these tools provide broad capabilities that might overlap with your existing specialized tools, use the following strategies to optimize for your specific environment and use case:

  1. Customize the skill definition: Update the SKILL.md file to specify the intents and use cases for which you want to invoke Gemini Cloud Assist. This ensures the agent is only triggered for tasks where its broad cloud expertise is required.
  2. Invoke skills explicitly: Many interactive agents allow you to control when a skill is triggered. If your agent supports it, you can configure the skill to require manual invocation using slash commands (/) or at-mentions (@). This approach allows you to precisely choose when to use Gemini Cloud Assist versus another specialized tool.

Gemini Cloud Assist skill reference

We recommend the following SKILL.md file on any agents that are configured with Gemini Cloud Assist MCP tools. This file guides your agent on how to use the tools appropriately.

What's next