Use the Spanner remote MCP server

Model Context Protocol (MCP) standardizes the way large language models (LLMs) and AI applications or agents connect to outside data sources. MCP servers let you use their tools, resources, and prompts to take actions and get updated data from their backend service.

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 on MCP architecture, see MCP architecture.

This document describes how to use the Spanner remote Model Context Protocol (MCP) server to connect to Spanner from AI applications such as Gemini CLI, agent mode in Gemini Code Assist, Claude Code, or from AI applications you're developing.

For information on the Spanner local MCP server, see Spanner MCP server on GitHub.

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

Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

Roles required to select or create a project

Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
Create a project: To create a project, you need the Project Creator role (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

Go to project selector

Verify that billing is enabled for your Google Cloud project.

In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

Roles required to select or create a project

Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
Create a project: To create a project, you need the Project Creator role (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

Go to project selector

Verify that billing is enabled for your Google Cloud project.

Enable the Spanner API.
Roles required to enable APIs
To enable APIs, you need the Service Usage Admin IAM role (roles/serviceusage.serviceUsageAdmin), which contains the serviceusage.services.enable permission. Learn how to grant roles.
Enable the API

For new projects, the Spanner API is automatically enabled.

Required roles

To get the permissions that you need to enable the Spanner MCP server, ask your administrator to grant you the following IAM roles on the project where you want to enable the Spanner MCP server:

Service Usage Admin (roles/serviceusage.serviceUsageAdmin)
Make MCP tool calls: MCP Tool User (roles/mcp.toolUser)
Use Spanner MCP tools: Cloud Spanner Admin (roles/spanner.admin)

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

These predefined roles contain the permissions required to enable the Spanner MCP server. To see the exact permissions that are required, expand the Required permissions section:

Required permissions

The following permissions are required to enable the Spanner MCP server:

serviceusage.mcppolicy.get
serviceusage.mcppolicy.update
Make MCP tool calls: mcp.tools.call
Use Spanner MCP tools:
- spanner.instances.create
- spanner.instances.get
- spanner.databases.create
- spanner.databases.update
- spanner.sessions.create
- spanner.instanceOperations.get
- spanner.databases.getDdl
- spanner.databases.select
- spanner.databases.write

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

Enable or disable the Spanner MCP server

You can enable or disable the Spanner MCP server in a project with the gcloud beta services mcp enable command. For more information, see the following sections.

Enable the Spanner MCP server in a project

If you are using different projects for your client credentials, such as service account keys, OAuth client ID or API keys, and for hosting your resources, then you must enable the Spanner service and the Spanner remote MCP server on both projects.

To enable the Spanner MCP server in your Google Cloud project, run the following command:

gcloud beta services mcp enable spanner.googleapis.com \
    --project=PROJECT_ID

Replace the following:

PROJECT_ID: the Google Cloud project ID.

The Spanner remote MCP server is enabled for use in your Google Cloud Project. If the Spanner service isn't enabled for your Google Cloud project, you are prompted to enable the service before enabling the Spanner remote MCP server.

As a security best practice, we recommend that you enable MCP servers only for the services required for your AI application to function.

Disable the Spanner MCP server in a project

To disable the Spanner MCP server in your Google Cloud project, run the following command:

gcloud beta services mcp disable spanner.googleapis.com \
    --project=PROJECT_ID

The Spanner MCP server is disabled for use in your Google Cloud Project.

Authentication and authorization

Spanner MCP servers use 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 Spanner remote MCP server doesn't accept API keys.

We recommend creating a separate identity for agents using MCP tools so that access to resources can be controlled and monitored. For more information on authentication, see Authenticate to MCP servers.

Spanner 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.

Spanner has the following MCP tool OAuth scopes:

Scope URI for gcloud CLI	Description
`https://www.googleapis.com/auth/spanner.admin`	Allows access to administer your Spanner instances and databases.
`https://www.googleapis.com/auth/spanner.data`	Allows access to view and manage data in a Spanner database.

For more information about these scopes, see Spanner API.

Configure an MCP client to use the Spanner MCP server

AI applications and agents, such as Gemini CLI or Claude, 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. To connect to a remote MCP server, the MCP client must know at a minimum the URL of the remote MCP server.

In your AI application, look for a way to connect to a remote MCP server. You are prompted to enter details about the server, such as its name and URL.

For the Spanner MCP server, enter the following as required:

Server name: Spanner MCP server
Server URL or Endpoint: spanner.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 on authentication, see Authenticate to MCP servers.
OAuth scope: the OAuth 2.0 scope that you want to use when connecting to the Spanner MCP server.

For host specific guidance, see the following:

For more general guidance, see the following resources:

Available tools

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

List tools

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

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

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

Sample use cases

The following are sample use cases for the Spanner MCP server.

Application development with Spanner

An application developer can use the Spanner MCP server to provision resources, create databases, and populate sample data.

Sample prompt: Create a regional Spanner instance in the PROJECT_ID project in the us-central1 regional instance configuration. Create a database for tracking inventory and populate 5 sample products.

Replace PROJECT_ID with your Google Cloud project ID.

Workflow:

The workflow for developing an application might look like the following:

The agent calls the create_instance tool to provision a new Spanner instance using the specified instance configuration. The agent might invoke the get_operation tool to verify if the instance is ready to be used.
The agent calls the create_database tool for creating a new database with the required schema. The agent might call the get_operation tool to check the status of the database creation operation.
The agent can use a combination of create_session, execute_sql, and the commit tools to insert sample data.
Optionally, the agent can call the execute_sql tool to query and validate the sample data creation.

Operational insights and database configuration management

Spanner administrators can use the Spanner MCP server to gather information about Spanner instances and databases using tools like list_instances, get_instance, list_databases, and get_database_ddl.

Sample prompts:

List all Spanner instances in the current project.
List all databases in the current Spanner instance.
Show the schema for the current Spanner database.

Optional security and safety configurations

MCP introduces new security risks and considerations due to the wide variety of actions that can be taken with MCP tools. To minimize and manage these risks, Google Cloud offers default 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.

Model Armor is only available in specific regional locations. If Model Armor is enabled for a project, and a call to that project comes from an unsupported region, Model Armor makes a cross-regional call. For more information, see Model Armor locations.

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 Service Usage Admin IAM role (roles/serviceusage.serviceUsageAdmin), which contains the serviceusage.services.enable permission. 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:

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.
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 protect your MCP tool calls and responses, you create a Model Armor floor setting and then enable MCP content security for your project. 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.

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": "HIGH", "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.
- HIGH: 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 Configure floor settings.
For your project, enable Model Armor protection for remote MCP servers.
```
gcloud beta services mcp content-security add modelarmor.googleapis.com --project=PROJECT_ID
```
Replace PROJECT_ID with your Google Cloud project ID. After you run this command, Model Armor sanitizes all MCP tool calls and responses from the project, regardless of where the calls and responses originate.
To confirm that Google MCP traffic is sent to Model Armor, run the following command:
```
gcloud beta services mcp content-security get --project=PROJECT_ID
```
Replace PROJECT_ID with the Google Cloud project ID.

Disable scanning MCP traffic with Model Armor

If you want to use Model Armor in a project, and you want to stop scanning Google MCP traffic with Model Armor, 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 won't scan MCP traffic in the project.

Control MCP use with IAM 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

Read the Spanner MCP reference documentation.
Learn more about Google Cloud MCP servers.