Compute Engine Managed MCP Extension
Preview: This product is subject to the "Pre-GA Offerings Terms" in the
General Service Terms section of the
Service Specific Terms.
Pre-GA products and features are available "as is" and might have limited
support. For more information, see the
launch stage descriptions.
The Compute Engine managed MCP extension 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.
- Retrieve information about instance group managers, instance templates,
disks, snapshots, reservations and commitments.
Why use the Compute Engine managed MCP server?
Google and Google Cloud
managed MCP servers can be used in
your AI applications with enterprise-ready governance, security, and access
control.
Before you begin
In the Google Cloud console, on the
project selector page,
select or create a Google Cloud project.
Note: If you don't plan to keep the resources that you create in this
procedure, create a project instead of selecting an existing project.
After you finish these steps, you can delete the project, removing all
resources associated with the project.
Get your administrator to grant you the
MCP Tool User role
(roles/mcp.toolUser) on the Google Cloud project. If you created a new
project, then you already have the required permissions.
Ensure your administrator has enabled the
Compute Engine API
on the Google Cloud project.
This extension uses Google Application Default Credentials (ADC) to perform
authentication. To login with ADC, run the following command in your terminal:
gcloud auth application-default login
For additional details, see the
ADC documentation.
To see a complete list of available tools and their schemas, see the
Compute Engine MCP reference.
Sample 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
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 take with MCP tools. To minimize and manage these risks,
Google Cloud offers defaults 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.
Quotas and limits
The Compute Engine MCP server doesn't have its own quotas. There is no limit on
the number of call that can be made to the MCP server. You are still subject to
the quotas enforced by the APIs called by the MCP server tools.
Reference and resources
