Learn how to use the Compute Engine remote MCP server to perform tasks such as creating and managing virtual machine instances, instance groups, disks, and snapshots.
    Console
    Start free

    Use the Compute Engine remote MCP server

    This document describes how to use the Compute Engine remote Model Context Protocol (MCP) server to connect to Compute Engine from AI applications such as Gemini CLI, ChatGPT, Claude, or in AI applications that you're developing. The Compute Engine remote MCP server provides a comprehensive set of capabilities that let LLM agents perform a range of infrastructure management tasks including the following:

    • Manage virtual machine (VM) instances.
    • Manage instance group managers and instance templates.
    • Manage disks and snapshots.
    • Retrieve information about reservations and commitments..

      Model Context Protocol (MCP) standardizes how large language models (LLMs) and AI applications or agents connect to external data sources. MCP servers let you use their tools, resources, and prompts to take actions and get updated data from their backend service.

      What's the difference between local and remote MCP servers?

      Local MCP servers
      Typically run on your local machine and use the standard input and output streams (stdio) for communication between services on the same device.
      Remote MCP servers
      Run on the service's infrastructure and offer an HTTP endpoint to AI applications for communication between the AI MCP client and the MCP server. For more information about MCP architecture, see MCP architecture .

      Google and Google Cloud remote MCP servers

      Google and Google Cloud remote MCP servers have the following features and benefits:
      • Simplified, centralized discovery.
      • Managed global or regional HTTP endpoints.
      • Fine-grained authorization.
      • Optional prompt and response security with Model Armor protection.
      • Centralized audit logging.

      For information about other MCP servers and information about security and governance controls available for Google Cloud MCP servers, see Google Cloud MCP servers overview .

      Before you begin

      1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

      2. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

        Roles required to select or create a project

        • Select a project : Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
        • Create a project : To create a project, you need the Project Creator role ( roles/resourcemanager.projectCreator ), which contains the resourcemanager.projects.create permission. Learn how to grant roles .

        Go to project selector

      3. Verify that billing is enabled for your Google Cloud project .

      4. Make sure that you have the following role or roles on the project: Compute Instance Admin (v1), Compute Security Admin, Service Account User, Service Usage Admin

        Check for the roles

        1. In the Google Cloud console, go to the IAM page.

          Go to IAM
        2. Select the project.

        3. In the Principal column, find all rows that identify you or a group that you're included in. To learn which groups you're included in, contact your administrator.

        4. For all rows that specify or include you, check the Role column to see whether the list of roles includes the required roles.

        Grant the roles

        1. In the Google Cloud console, go to the IAM page.

          Go to IAM
        2. Select the project.
        3. Click Grant access .

        4. In the New principals field, enter your user identifier. This is typically the email address for a Google Account.

        5. Click Select a role , then search for the role.
        6. To grant additional roles, click Add another role and add each additional role.
        7. Click Save .

      5. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

        Roles required to select or create a project

        • Select a project : Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
        • Create a project : To create a project, you need the Project Creator role ( roles/resourcemanager.projectCreator ), which contains the resourcemanager.projects.create permission. Learn how to grant roles .

        Go to project selector

      6. Verify that billing is enabled for your Google Cloud project .

      7. Make sure that you have the following role or roles on the project: Compute Instance Admin (v1), Compute Security Admin, Service Account User, Service Usage Admin

        Check for the roles

        1. In the Google Cloud console, go to the IAM page.

          Go to IAM
        2. Select the project.

        3. In the Principal column, find all rows that identify you or a group that you're included in. To learn which groups you're included in, contact your administrator.

        4. For all rows that specify or include you, check the Role column to see whether the list of roles includes the required roles.

        Grant the roles

        1. In the Google Cloud console, go to the IAM page.

          Go to IAM
        2. Select the project.
        3. Click Grant access .

        4. In the New principals field, enter your user identifier. This is typically the email address for a Google Account.

        5. Click Select a role , then search for the role.
        6. To grant additional roles, click Add another role and add each additional role.
        7. Click Save .
      8. Enable the Compute Engine API.

        Enable the Compute Engine API

      Required roles

      To get the permissions that you need to to enable the Compute Engine remote MCP server, ask your administrator to grant you the Service Usage Admin ( roles/serviceusage.serviceUsageAdmin ) IAM role on your Google Cloud project. For more information about granting roles, see Manage access to projects, folders, and organizations .

      You might also be able to get the required permissions through custom roles or other predefined roles .

      Roles for using the service

      To get the permission that you need to to make calls to the remote MCP server tools, ask your administrator to grant you the MCP Tool User ( roles/mcp.toolUser ) IAM role on your Google Cloud project. For more information about granting roles, see Manage access to projects, folders, and organizations .

      This predefined role contains the mcp.tools.call permission, which is required to to make calls to the remote MCP server tools.

      You might also be able to get this permission with custom roles or other predefined roles .

      You also need the roles and permissions required to perform the Compute Engine operations. For more information, see Compute Engine roles and permissions .

      Enable or disable the Compute Engine MCP server

      You can enable or disable the Compute Engine MCP server in a project with the gcloud beta services mcp enable command. For more information, see the following sections.

      Enable the Compute Engine MCP server in a project

      If you're using different projects for your client credentials, such as service account keys, OAuth client ID or API keys, and for hosting your resources, then you must enable the Compute Engine service and the Compute Engine remote MCP server on both projects.

      To enable the Compute Engine MCP server in your Google Cloud project, run the following command:

       gcloud  
beta  
services  
mcp  
 enable 
  
 SERVICE 
  
 \ 
  
--project = 
 PROJECT_ID

      Replace the following:

      • PROJECT_ID : the Google Cloud project ID.
      • SERVICE : compute.googleapis.com , the global service name for Compute Engine.

      The Compute Engine remote MCP server is enabled for use in your Google Cloud Project. If the Compute Engine service isn't enabled for your Google Cloud project, you're prompted to enable the service before enabling the Compute Engine remote MCP server.

      As a security best practice, we recommend that you enable MCP servers only for the services required for your AI application to function.

      Disable the Compute Engine MCP server in a project

      To disable the Compute Engine MCP server in your Google Cloud project, run the following command:

       gcloud  
beta  
services  
mcp  
disable  
 SERVICE 
  
 \ 
  
--project = 
 PROJECT_ID

      The Compute Engine MCP server is disabled for use in your Google Cloud project.

      Authentication and authorization

      Compute Engine MCP servers use the OAuth 2.0 protocol with Identity and Access Management (IAM) for authentication and authorization. All Google Cloud identities are supported for authentication to MCP servers.

      We recommend that you create a separate identity for agents that are using MCP tools so that access to resources can be controlled and monitored. For more information about authentication, see Authenticate to MCP servers .

      Compute Engine MCP OAuth scopes

      OAuth 2.0 uses scopes and credentials to determine if an authenticated principal is authorized to take a specific action on a resource. For more information about OAuth 2.0 scopes at Google, read Using OAuth 2.0 to access Google APIs .

      Compute Engine has the following MCP tool OAuth scopes:

      Scope URI for gcloud CLI Description
      https://www.googleapis.com/auth/compute.read-only Only allows access to read data.
      https://www.googleapis.com/auth/compute.read-write Allows access to read and modify data.

      Additional scopes might be required on the resources accessed during a tool call. To view a list of scopes required for Compute Engine, see Compute Engine API .

      Configure an MCP client to use the Compute Engine MCP server

      AI applications and agents, such as Claude or Gemini CLI, can instantiate an MCP client that connects to a single MCP server. An AI application can have multiple clients that connect to different MCP servers. To connect to a remote MCP server, the MCP client must know the remote MCP server's URL.

      In your AI application, look for a way to connect to a remote MCP server. You are prompted to enter details about the server, such as its name and URL.

      For the Compute Engine MCP server, enter the following as required:

      • Server name: Compute Engine MCP server
      • Server URLor Endpoint: compute.googleapis.com/mcp
      • Transport: HTTP
      • Authentication details: Depending on how you want to authenticate, you can enter your Google Cloud credentials, your OAuth Client ID and secret, or an agent identity and credentials. For more information on authentication, see Authenticate to MCP servers .

      For host-specific guidance about setting up and connecting to MCP server, see the following:

      For more general guidance, see the following resources:

      Available tools

      To view details of available MCP tools and their descriptions for the Compute Engine MCP server, see the Compute Engine MCP reference .

      List tools

      Use the MCP inspector to list tools, or send a tools/list HTTP request directly to the Compute Engine remote MCP server. The tools/list method doesn't require authentication.

       POST /mcp HTTP/1.1
Host: compute.googleapis.com
Content-Type: application/json

{
  "jsonrpc": "2.0",
  "method": "tools/list",
}

      Example use cases

      The following sample use cases describe how you can use the Compute Engine MCP server to manage Compute Engine resources:

      • Inspect and manage resources. For example, to understand resource allocation and configuration in your project, you can list all compute instances. You can also find all running compute instances in a zone that have a specific accelerator attached, and show their location and name for resource management.
      • Clean up unused resources to reduce operational costs. For example, identify and clean up disk snapshots in a zone that are no longer associated with a source disk, or identify and delete stopped VM instances that have costly GPU resources attached.
      • Optimize instance performance. For example, resize an under-provisioned VM instance to a larger machine type in the same family, and confirm the successful update.
      • Provision specialized VMs for AI workloads with zone flexibility. For example, create a VM instance with a specific GPU accelerator attached, in any zone in a specified region where it is available.
      • Troubleshoot and validate instance configurations. For example, retrieve configuration details for a specific VM instance where the job is frozen, reboot it, and confirm the underlying accelerator and disk are attached.

      Sample prompts

      The following are sample prompts that you can use to perform tasks by using the Compute Engine MCP server:

      • List all VMs in PROJECT_ID , including the VM name and zone.
      • Show the instance details for VM_NAME .
      • In REGION , find all disk snapshots for which the source disk no longer exists.
      • Change the machine type of VM_NAME to the next largest machine type in the same machine family, send notification when it's back online, and confirm the new machine type.
      • Find all running VMs in REGION with NVIDIA accelerators, and show the zone and name for these VMs.
      • Create a VM in ZONE with an NVIDIA T4 accelerator attached. Name the VM my-nvidiat4-vm .
      • Find all stopped VMs in REGION with NVIDIA Tesla T4 accelerators, and delete them.

      Replace the following:

      • PROJECT_ID : the Google Cloud project ID.
      • REGION : the name of the region where your resources exist.
      • ZONE : the name of the zone where your VMs exist.
      • VM_NAME : the name of your VM instance.

      Optional security and safety configurations

      MCP introduces new security risks and considerations due to the wide variety of actions that you can do with the MCP tools. To minimize and manage these risks, Google Cloud offers default settings and customizable policies to control the use of MCP tools in your Google Cloud organization or project.

      For more information about MCP security and governance, see AI security and safety .

      Use Model Armor

      Model Armor is a Google Cloud service that's designed to enhance the security and safety of your AI applications. It works by proactively screening LLM prompts and responses, protecting against various risks and supporting responsible AI practices. Whether you deploy AI in your cloud environment, or on external cloud providers, Model Armor can help you prevent malicious input, verify content safety, protect sensitive data, maintain compliance, and enforce your AI safety and security policies consistently across your diverse AI landscape.

      Model Armor is only available in specific regional locations. If Model Armor is enabled for a project, and a call to that project comes from an unsupported region, Model Armor makes a cross-regional call. For more information, see Model Armor locations .

      Enable Model Armor

      To enable Model Armor on your

      Google Cloud project, run the following gcloud CLI command:

       gcloud  
services  
 enable 
  
modelarmor.googleapis.com  
 \ 
  
--project = 
 PROJECT_ID

      Replace PROJECT_ID with your Google Cloud project ID.

      Configure protection for Google and Google Cloud remote MCP servers

      To protect your MCP tool calls and responses, you create a Model Armor floor setting and then enable MCP content security for your project. A floor setting defines the minimum security filters that apply across the project. This configuration applies a consistent set of filters to all MCP tool calls and responses within the project.

      1. Set up a Model Armor floor setting with MCP sanitization enabled. For more information, see Configure Model Armor floor settings .

        See the following example command:

        gcloud  
model-armor  
floorsettings  
update  
 \ 
--full-uri = 
 'projects/ PROJECT_ID 
/locations/global/floorSetting' 
  
 \ 
--enable-floor-setting-enforcement = 
TRUE  
 \ 
--add-integrated-services = 
GOOGLE_MCP_SERVER  
 \ 
--google-mcp-server-enforcement-type = 
INSPECT_AND_BLOCK  
 \ 
--enable-google-mcp-server-cloud-logging  
 \ 
--malicious-uri-filter-settings-enforcement = 
ENABLED  
 \ 
--add-rai-settings-filters = 
 '[{"confidenceLevel": "HIGH", "filterType": "DANGEROUS"}]'

        Replace PROJECT_ID with your Google Cloud project ID.

        Note the following settings:

        • INSPECT_AND_BLOCK : The enforcement type that inspects content for the Google MCP server and blocks prompts and responses that match the filters.
        • ENABLED : The setting that enables a filter or enforcement.
        • HIGH : The confidence level for the Responsible AI - Dangerous filter settings. You can modify this setting, though lower values might result in more false positives. For more information, see Configure floor settings .

      2. For your project, enable Model Armor protection for remote MCP servers.

        gcloud  
beta  
services  
mcp  
content-security  
add  
modelarmor.googleapis.com  
--project = 
 PROJECT_ID

        Replace PROJECT_ID with your Google Cloud project ID. After you run this command, Model Armor sanitizes all MCP tool calls and responses from the project, regardless of where the calls and responses originate.

      3. To confirm that Google MCP traffic is sent to Model Armor, run the following command:

         gcloud  
beta  
services  
mcp  
content-security  
get  
--project = 
 PROJECT_ID

        Replace PROJECT_ID with the Google Cloud project ID.

      Disable Model Armor in a project

      To disable Model Armor on a Google Cloud project, run the following command:

       gcloud  
beta  
services  
mcp  
content-security  
remove  
modelarmor.googleapis.com  
 \ 
  
--project = 
 PROJECT_ID

      Replace PROJECT_ID with the Google Cloud project ID.

      Google MCP traffic won't be scanned by Model Armor for the specified project.

      Disable scanning MCP traffic with Model Armor

      If you want to use Model Armor in a project, and you want to stop scanning Google MCP traffic with Model Armor, run the following command:

       gcloud  
model-armor  
floorsettings  
update  
 \ 
  
--full-uri = 
 'projects/ PROJECT_ID 
/locations/global/floorSetting' 
  
 \ 
  
--remove-integrated-services = 
GOOGLE_MCP_SERVER

      Replace PROJECT_ID with the Google Cloud project ID.

      Model Armor won't scan MCP traffic in the project.

      Control MCP use with IAM deny policies

      Identity and Access Management (IAM) deny policies help you secure Google Cloud remote MCP servers. Configure these policies to block unwanted MCP tool access.

      For example, you can deny or allow access based on:

      • The principal.
      • Tool properties like read-only.
      • The application's OAuth client ID.

      For more information, see Control MCP use with Identity and Access Management .

      What's next
    Create a Mobile Website
    View Site in Mobile | Classic
    Share by: