Use the Cloud Location Finder remote MCP server

This document shows you how to use the Cloud Location Finder remote Model Context Protocol (MCP) server to connect with AI applications including Gemini CLI, ChatGPT, Claude, and custom applications you are developing. The Cloud Location Finder remote MCP server allows large language model (LLM)-powered agents to securely retrieve cloud location data and perform actions using standard MCP tools. This built-in integration enables agents to reason about latency, sustainability, and compliance boundaries when managing multi-cloud or hybrid deployments. The Cloud Location Finder remote MCP server is enabled when you enable the Cloud Location Finder 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.

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

Required roles

To get the permissions that you need to use the Cloud Location Finder MCP server, ask your administrator to grant you the following IAM roles on the project where you want to use the Cloud Location Finder MCP server:

For more information about granting roles, see Manage access to projects, folders, and organizations.

These predefined roles contain the permissions required to use the Cloud Location Finder MCP server. To see the exact permissions that are required, expand the Required permissions section:

Required permissions

The following permissions are required to use the Cloud Location Finder MCP server:

  • Make MCP tool calls: mcp.tools.call

You might also be able to get these permissions with custom roles or other predefined roles.

Authentication and authorization

The Cloud Location Finder 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.

The Cloud Location Finder remote MCP server requires authentication using OAuth 2.0. It does not accept API keys.

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.

Cloud Location Finder 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.

Cloud Location Finder requires the following MCP tool OAuth scope:

Scope URI for gcloud CLI Description
https://www.googleapis.com/auth/cloud-platform Allows full access to all Google Cloud services.

Additional scopes might be required on the resources accessed during a tool call.

Configure an MCP client to use the Cloud Location Finder 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 Cloud Location Finder MCP server, enter the following information as required:

  • Server name: Cloud Location Finder MCP server
  • Server URL or Endpoint: https://cloudlocationfinder.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 Cloud Location Finder MCP server requires the https://www.googleapis.com/auth/cloud-platform scope.

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:

Available tools

To view details of available MCP tools and their descriptions for the Cloud Location Finder MCP server, see the Cloud Location Finder MCP reference.

List tools

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

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

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

Example use cases

The following are example use cases for the Cloud Location Finder MCP server. These examples show the types of prompts you can provide to your agent.

Proximity and latency

Prompt: "Find the nearest Google Cloud zone to the AWS us-east-1 region."

This prompts the agent to use the search_cloud_locations tool to filter for zones that are closest to the aws-us-east-1 location based on network latency:

Sustainability

Prompt: "List Google Cloud regions that have a carbon free energy percentage of 80% or higher."

This prompts the agent to use the list_cloud_locations tool to filter regions with a carbon_free_energy_percentage >= 80:

Compliance setup

Prompt: "Find all cloud locations in Europe to ensure data residency compliance."

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.

Control MCP use with Identity and Access Management 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