Use the App Optimize API remote MCP server

Preview

This feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms, and the Additional Terms for Generative AI Preview Products. Pre-GA features are available "as is" and might have limited support. For more information, see the launch stage descriptions.

This document shows you how to use the App Optimize API remote Model Context Protocol (MCP) server to connect with AI applications including Gemini CLI, ChatGPT, Claude, and custom applications you are developing. The App Optimize API remote MCP server helps you to understand your Google Cloud resource spending and usage. You can get reports with cost and utilization data for analysis and integration with your other data. The App Optimize remote MCP server is enabled when you enable the App Optimize API.

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.

Stateless core

With MCP version 2026-07-28, MCP changes from a bidirectional, stateful protocol to a stateless protocol. Each MCP request is self-describing and can be routed using headers. There isn't a need for the initialize/initialized handshake or Mcp-Session-Id because each request includes all the information needed in HTTP headers or the _meta parameter. MCP servers can request additional information required by a tool through multi-round-trip requests (MRTR).

To help route and process requests without parsing the request body, some MCP headers are required, including the following:

• Headers that are required by the MCP specification such as the protocol version header and standard request headers.
• Custom headers that are defined by the MCP server. These headers are mirrored into HTTP headers from the tool's input schema using the x-mcp-header property. For example, an MCP server might define a custom header to specify the Google Cloud region or project ID.

For more information about MCP architecture, see the MCP version 2026-07-28 specification and key changes.

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. Identify projects with active Google Cloud resources to analyze. App Optimize APIneeds billing and utilization data to produce meaningful results. Reports run against new or empty projects will be empty.

   You can use the MCP server in one project that analyzes data from another project or from applications in single-project or folder-level boundaries. To get data for App Hub application, which can be made up of multiple projects, you must have the required monitoring and billing permissions on all of the application's associated projects to create the report.

Sign in to your Google Account.

If you don't already have one, sign up for a new account.

Install the Google Cloud CLI.

If you're using an external identity provider (IdP), you must first sign in to the gcloud CLI with your federated identity.

To initialize the gcloud CLI, run the following command:

gcloud init

Verify that you have the permissions required to complete this guide.

Verify that billing is enabled for your Google Cloud project.

Enable the App Optimize API, if it is not already enabled:

Roles required to enable APIs

To enable APIs, you need the serviceusage.services.enable permission. If you created the project, then you likely already have this permission through the Owner role (roles/owner). Otherwise, you can get this permission through the Service Usage Admin role (roles/serviceusage.serviceUsageAdmin). Learn how to grant roles.

gcloud services enable appoptimize

Install the Google Cloud CLI.

If you're using an external identity provider (IdP), you must first sign in to the gcloud CLI with your federated identity.

To initialize the gcloud CLI, run the following command:

gcloud init

Verify that you have the permissions required to complete this guide.

Verify that billing is enabled for your Google Cloud project.

Enable the App Optimize API, if it is not already enabled:

Roles required to enable APIs

To enable APIs, you need the serviceusage.services.enable permission. If you created the project, then you likely already have this permission through the Owner role (roles/owner). Otherwise, you can get this permission through the Service Usage Admin role (roles/serviceusage.serviceUsageAdmin). Learn how to grant roles.

gcloud services enable appoptimize

Required roles

To get the permissions that you need to use the App Optimize API MCP server, ask your administrator to grant you the following IAM roles:

• Make MCP tool calls: MCP Tool User (roles/mcp.toolUser) on the project where you want to use the App Optimize API MCP server.
• Use the App Optimize API MCP server: App Optimize Admin (roles/appoptimize.admin) on the project where you are using the MCP server.
• Get cost data: A role with the billing.resourceCosts.get permission, such as Cloud Hub Operator (roles/cloudhub.operator), Viewer (roles/viewer), or a custom role. on the projects that contain your Google Cloud resources.
• Get utilization data: Monitoring Viewer (roles/monitoring.viewer) on the projects that contain your Google Cloud resources.
• Get data for App Hub applications: App Hub Viewer (roles/apphub.viewer) on the project with your application data. For an app-enabled folder, this is the management 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.

The Cloud Hub Operator (roles/cloudhub.operator) role includes the required permissions for getting cost data, utilization data, and App Hub application data.

Authentication and authorization

The App Optimize remote MCP server uses 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.

The App Optimize API MCP server requires a principal for IAM control and doesn't accept API keys for authentication.

App Optimize API 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.

App Optimize API has the following MCP tool OAuth scopes:

Scope URI for gcloud CLI	Description
https://www.googleapis.com/auth/appoptimize	Only allows access to create and read reports.

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

Configure an MCP client to use the App Optimize API MCP server

AI applications and agents, such as Claude or Antigravity, 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. If your application isn't listed in the client-specific guidance, then you can use the following information to connect from most applications.

In your AI application, look for a way to add or connect to a remote MCP server. For the App Optimize API MCP server, enter the following information as required:

• Server name: App Optimize API MCP server
• Server URL or Endpoint: https://appoptimize.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 about authentication, see Authenticate to MCP servers.
• OAuth scope: the App Optimize API OAuth scope for connecting to the App Optimize API MCP server. To learn more about OAuth 2.0 scopes, see the OAuth scope overview.

Redirect URIs

For web-based applications, and some desktop applications, you must allowlist a redirect URI when you create a client ID and secret for authentication. Redirect URIs are used by the authorization server to send tokens to your application. Your application's documentation should specify the redirect URI that you must use. Custom redirect URIs aren't supported.

For application-specific guidance about setting up and connecting to MCP server, see Client-specific guidance.

For more general guidance, see the following resources:

• Connect to remote MCP servers.
• Configure MCP in an AI application.

Available tools

The App Optimize API MCP server lets you create and read reports with cost and utilization data for a specified project or App Hub application.

To view details of available MCP tools and their descriptions for the App Optimize API MCP server, see the App Optimize API MCP reference.

List tools

Use the MCP inspector to list tools, or send a tools/list HTTP request directly to the App Optimize API remote MCP server. The tools/list method requires authentication.

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

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

Example use cases

When you connect am AI application or agent to the App Optimize API MCP server, you can use prompts to answer questions about costs and resource utilization.

Sample prompts

• "Which products are costing me the most in my project?"
• "Show me the top 5 resources that cost me the most last month."
• "Which idle VMs are the most expensive?"
• "Show me the cost of my most underutilized resources last month."
• "How much did application-A cost me in the last 7 days?"
• "How much did I spend on data transfer last week in my project?"
• "Which 5 BigQuery datasets or jobs had the highest storage costs in the last 30 days?"

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 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 are deploying 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.

When Model Armor is enabled with logging enabled, Model Armor logs the entire payload. This might expose sensitive information in your logs.

MCP request routing to Model Armor

Model Armor is available in certain regions. When Model Armor is enabled and you use an MCP server in a jurisdiction that Model Armor doesn't support, the routing behavior of the call might be different for different MCP servers and might break data residency compliance for in-use and in-transit data. For more information about the behavior of individual MCP servers, see Model Armor supported products.

Enable Model Armor

You must enable Model Armor APIs before you can use Model Armor.

Console

1. Enable the Model Armor API, if it is not already enabled.

   Roles required to enable APIs

   To enable APIs, you need the serviceusage.services.enable permission. If you created the project, then you likely already have this permission through the Owner role (roles/owner). Otherwise, you can get this permission through the Service Usage Admin role (roles/serviceusage.serviceUsageAdmin). Learn how to grant roles.

   Enable the API

2. Select the project where you want to activate Model Armor.

gcloud

Before you begin, follow these steps using the Google Cloud CLI with the Model Armor API:

1. In the Google Cloud console, activate Cloud Shell.

   Activate Cloud Shell

   At the bottom of the Google Cloud console, a Cloud Shell session starts and displays a command-line prompt. Cloud Shell is a shell environment with the Google Cloud CLI already installed and with values already set for your current project. It can take a few seconds for the session to initialize.

2. Run the following command to set the API endpoint for the Model Armor service.

   gcloud config set api_endpoint_overrides/modelarmor "https://modelarmor.LOCATION.rep.googleapis.com/"

   Replace LOCATION with the region where you want to use Model Armor.

Configure protection for Google and Google Cloud remote MCP servers

To help protect your MCP tool calls and responses you can use Model Armor floor settings. 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.

Tip: Don't enable the prompt injection and jailbreak filter unless your MCP traffic carries natural language data.

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

Note: If the agent and the MCP server are in different projects, you can create floor settings in both projects (the client project and the resource project). In this case, Model Armor is invoked twice, once for each project.

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": "MEDIUM_AND_ABOVE", "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.
• MEDIUM_AND_ABOVE: 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 Model Armor confidence levels.

Disable scanning MCP traffic with Model Armor

To stop Model Armor from automatically scanning traffic to and from Google MCP servers based on the project's floor settings, 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 doesn't automatically apply the rules defined in this project's floor settings to any Google MCP server traffic.

Model Armor floor settings and general configuration can impact more than just MCP. Because Model Armor integrates with services like Vertex AI, any changes you make to floor settings can affect traffic scanning and safety behaviors across all integrated services, not just MCP.

Control MCP use with Identity and Access Management deny policies

Identity and Access Management (IAM) deny policies and allow policies help you secure Google Cloud and Google MCP servers.

You can combine multiple criteria to build customized security and governance policies by allowing or denying access based on the following:

• The principal.
• Tool properties like the read-only attribute.
• The service name or tool name.
• The application's OAuth client ID.

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

What's next

• Read the App Optimize API MCP reference documentation.
• Learn more about Google Cloud MCP servers.