Use the Secure Source Manager MCP server

This document shows you how to use the Secure Source Manager remote Model Context Protocol (MCP) server to connect with AI applications including Gemini CLI, ChatGPT, Claude, and custom applications you are developing. The Secure Source Manager remote MCP server lets your AI applications and coding assistants interact directly with your source code repositories, branch protection rules, issues, and pull requests. The Secure Source Manager remote MCP server is enabled when you enable the Secure Source Manager 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

Before you can use the Secure Source Manager MCP server, you must have a Secure Source Manager instance and repository created. For instructions, see Create an instance and Create a repository.

Required roles

To get the permissions that you need to use the Secure Source Manager MCP server, ask your administrator to grant you the following IAM roles on the project, instance, and repository where you want to use the Secure Source Manager MCP server:

Make MCP tool calls: MCP Tool User (roles/mcp.toolUser)
Access instances: Secure Source Manager Instance Accessor (roles/securesourcemanager.instanceAccessor)
Read repositories and pull requests: Secure Source Manager Repo Reader (roles/securesourcemanager.repoReader)
Create pull requests and comments: Secure Source Manager Repo Writer (roles/securesourcemanager.repoWriter)
Manage repositories and branch rules: Secure Source Manager Repo Admin (roles/securesourcemanager.repoAdmin)

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

These predefined roles contain the permissions required to use the Secure Source Manager 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 Secure Source Manager 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 Secure Source Manager 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 Secure Source Manager MCP server requires a principal for Identity and Access Management (IAM) control and doesn't accept API keys. We recommend that you create a separate identity for agents that use MCP tools so that you can control and monitor access to resources.

For more information about authentication, see Authenticate to MCP servers.

Secure Source Manager 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, see Using OAuth 2.0 to access Google APIs.

Secure Source Manager has the following MCP tool OAuth scopes:

Scope URI for Google Cloud CLI	Description
`https://www.googleapis.com/auth/cloud-platform`	Full access to all Google Cloud resources.

Additional permissions might be required on the resources accessed during a tool call. To view a list of roles and permissions required for Secure Source Manager, see Access control with IAM.

Configure an MCP client to use the Secure Source Manager 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.

Server name: Secure Source Manager MCP server
Server URL or Endpoint: The Secure Source Manager MCP server only supports regional endpoints. Use the regional endpoint for your instance location: https://securesourcemanager.LOCATION.rep.googleapis.com/mcp, where LOCATION is the supported region where your instance is located.
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 OAuth 2.0 scope that you want to use when connecting to the Secure Source Manager MCP server.

Toolset endpoints

The Secure Source Manager MCP server offers specialized toolset endpoints so you can limit the tools exposed to your AI agent:

Code review: https://securesourcemanager.LOCATION.rep.googleapis.com/mcp/code_review
Pull requests: https://securesourcemanager.LOCATION.rep.googleapis.com/mcp/pull_request
Branch rules: https://securesourcemanager.LOCATION.rep.googleapis.com/mcp/branch_rule
Repositories: https://securesourcemanager.LOCATION.rep.googleapis.com/mcp/repository
Instances: https://securesourcemanager.LOCATION.rep.googleapis.com/mcp/instance
Hooks: https://securesourcemanager.LOCATION.rep.googleapis.com/mcp/hook

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 Secure Source Manager MCP server, see the Secure Source Manager MCP reference.

List tools

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

POST /mcp HTTP/1.1
Host: securesourcemanager.LOCATION.rep.googleapis.com
Content-Type: application/json

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

Example use cases

Connecting an AI agent or IDE assistant to the Secure Source Manager MCP server lets you inspect source code repositories, automate pull request workflows, and manage governance rules using natural language.

You can use the Secure Source Manager MCP server for the following use cases:

Automate code reviews and manage pull requests: Let an AI agent inspect open pull requests, analyze modified files and diffs, and create, resolve, or batch-submit line-specific review comments.
Manage repositories and instances: Retrieve repository details, inspect Secure Source Manager instances, or create and configure source code repositories across your organization.
Inspect and configure branch governance and webhooks: Query, create, or update branch protection rules to enforce security policies on critical branches, or manage repository webhooks for CI/CD integrations.

Example prompts

The following are example prompts you can use with your connected AI application:

Review pull requests and diffs: "List all open pull requests in repository my-repo and summarize the modified files and diffs in PR #12."
Submit code review comments: "Add a review comment on line 45 of auth.go in pull request #12 suggesting error handling improvements."
Inspect branch protection: "Show the branch protection rules for the main branch in repository my-repo."
Configure branch governance: "Create a branch protection rule for repository my-repo that requires approval before merging into main."
Inspect repositories and instances: "List all Secure Source Manager repositories in instance my-instance and display their details."

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 Secure Source Manager MCP server uses regional endpoints with data residency-compliant routing. When you enable Model Armor, the MCP server sends all requests to Model Armor because the Secure Source Manager MCP server is only available in jurisdictions where Model Armor is available. 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

Enable the Model Armor API.
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
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:

Install the Google Cloud CLI, and then sign in to the gcloud CLI with your federated identity. After signing in, initialize the Google Cloud CLI by running the following command:
```
gcloud init
```
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 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.

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": "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 IAM 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

Review the Secure Source Manager MCP reference documentation.
Learn more about Google Cloud MCP servers.
Learn more about Access control with IAM.