Use the App Topology remote MCP server

This document shows you how to use the App Topology remote Model Context Protocol (MCP) server to connect with AI applications including Gemini CLI, ChatGPT, Claude, and custom applications you are developing. App Topology helps you to correlate observability, DevOps, and security data to help you to understand the compliance and security posture of your agentic resources and troubleshoot issues. The App Topology remote MCP server is enabled when you enable the App Topology 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

Set up App Topology.

Required roles

This section describes roles to use the MCP server and suggested roles to access the underlying data that you are querying.

Roles for the MCP server

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

• Make MCP tool calls: MCP Tool User (roles/mcp.toolUser) on the projects where you want to use App Topology
• Run queries: App Topology Viewer (roles/apptopology.viewer) on the projects where you want to use App Topology

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

These predefined roles contain the permissions required to use the App Topology 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 App Topology MCP server:

• Make MCP tool calls: mcp.tools.call
• Get domains:
  • apptopology.domains.get
  • apptopology.domains.list
• Get schemas: apptopology.schemas.get
• Get discovered resource data: apptopology.discoveredResourcesTopologies.generate
• Get DevOps domain data: apptopology.devOpsDomainTopologies.generate
• Get Security domain data: apptopology.securityDomainTopologies.generate
• Get SRE domain data (all supported data): apptopology.sreDomainTopologies.generate

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

Authentication and authorization

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

App Topology 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 Topology has the following MCP tool OAuth scopes:

Scope URI for gcloud CLI	Description
https://www.googleapis.com/auth/apptopology.read-only	Allows access to read data.

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

Configure an MCP client to use the App Topology 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 Topology MCP server, enter the following information as required:

• Server name: App Topology MCP server
• Server URL or Endpoint: https://apptopology.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: https://www.googleapis.com/auth/apptopology.read-only. 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

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

List tools

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

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

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

Use App Topology tools

The App Topology MCP tools are designed to be used in sequence by AI agents:.

1. The list_domains tool lists domains that are available for querying in the specified project. The SRE domain includes all resource data that App Topology supports.

2. The get_schema and explore_schema tools let an agent get the supported nodes, edges, and their properties in a specified domain. Getting the schema for is necessary context for an agent to build a valid query with the generate_discovered_resources_topology tool.

3. The generate_discovered_resource_topology tool takes a recursive graph query as input and returns a topology as an adjacency list. The nodes, edges, and properties in the tool request must be valid in the schema for the domain you are querying.

   For an overview of query structure and query limitations, see About queries.

Sample use cases

You can use natural language prompts to help you to run queries.

• Get traffic for an App Hub application: "Show me all services and workloads that send or receive traffic from other services and workloads in project PROJECT_ID."
• Get agent traffic: "Show me all agents that send or receive traffic from other agents or MCP servers in project PROJECT_ID"
• List service accounts that can impersonate a service account with broad permissions: "Show me Service Accounts which have Owner/Editor/Viewer role on project PROJECT_ID that can be impersonated by a service account which has a Key."
• List vulnerabilities for a Kubernetes deployment: "Show me vulnerabilities in Kubernetes deployments with CVE ID CVE-ID in project PROJECT_ID."
• Validate provenance by listing artifacts with a specific SLSA level: "Show me all workloads and services that are using an artifact that is of SLSA level SLSA_LEVEL in project PROJECT_ID."

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

The App Topology MCP server uses cross-jurisdictional routing When you enable Model Armor, the MCP server sends all requests to Model Armor for screening. Cross-jurisdictional routing might break existing data residency compliance commitments for in-use and in-transit data. For more information about the behavior of other 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 Topology MCP reference documentation.
• Learn more about Google Cloud MCP servers.