Use the Datastream remote MCP server

This document shows you how to use the Datastream remote Model Context Protocol (MCP) server to connect with AI applications including Gemini CLI, ChatGPT, Claude, and custom applications you are developing. The Datastream remote MCP server lets you manage and monitor your Datastream resources, such as streams, connection profiles, and stream objects, from your AI application. .

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

  1. 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.

  2. 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

  3. Verify that billing is enabled for your Google Cloud project.

  4. 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

  5. Verify that billing is enabled for your Google Cloud project.

  6. Enable the Datastream API.

Required roles

To get the permissions that you need to to use the Datastream remote MCP server, ask your administrator to grant you the following IAM roles on your Google Cloud project:

  • make MCP tool calls
  • Datastream resources: Datastream Viewer (roles/datastream.viewer) or Datastream Admin (roles/datastream.admin)

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

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

Required permissions

The following permissions are required to to use the Datastream remote MCP server:

  • make MCP tool calls
  • Datastream resources:
    • datastream.streams.list
    • datastream.streams.get
    • datastream.streams.run
    • datastream.streams.delete

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

Enable or disable the Datastream MCP server

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

Authentication and authorization

Datastream 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.

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. Datastream does not accept API keys for authentication to MCP servers. For more information about authentication, see Authenticate to MCP servers.

Datastream 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.

Datastream has the following MCP tool OAuth scopes:

Scope URI for gcloud CLI Description
https://www.googleapis.com/auth/cloud-platform Allows access to all Google Cloud resources, including Datastream.

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

Configure an MCP client to use the Datastream MCP server

AI applications and agents, such as Claude or Gemini CLI, 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 the remote MCP server's URL.

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 Datastream MCP server, enter the following as required:

  • Server name: Datastream MCP server
  • Server URL or Endpoint: datastream.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 OAuth 2.0 scope that you want to use when connecting to the Datastream MCP server.

For host-specific guidance about setting up and connecting to MCP server, see the following:

For more general guidance, see the following resources:

Available tools

MCP tools that are read-only have the MCP attribute mcp.tool.isReadOnly set to true. You might want to only allow read-only tools in certain environments through your organization policy.

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

List tools

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

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

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

Example use cases

The following are example use cases for the Datastream MCP server:

  • List, get, start, and delete streams in your project.
  • List connection profiles to verify connectivity settings for sources and destinations.
  • List and get details of specific stream objects to track replication progress and status.
  • Use the get_operation tool to poll the status of operations such as starting or deleting a stream.

Example prompts:

  • "List all running Datastream streams in project PROJECT_ID and location LOCATION."
  • "What's the status of the Datastream stream STREAM_ID in LOCATION?"
  • "Start the Datastream stream STREAM_ID in LOCATION."
  • "List the objects being replicated by the stream projects/PROJECT_ID/locations/LOCATION/streams/STREAM_ID."
  • "Check the replication status for the source table TABLE_NAME in the stream projects/PROJECT_ID/locations/LOCATION/streams/STREAM_ID."

In the prompts, replace the following:

  • PROJECT_ID with your Google Cloud project identifier.
  • LOCATION with the location of your Google Cloud project.
  • STREAM_ID with your Datastream stream identifier.
  • TABLE_NAME with the name of your source table.

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 that's 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 deploy 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

  1. 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

  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.

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

If 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.

Organization level MCP control

You can create custom organization policies to control the use of MCP servers in your Google Cloud organization by using the gcp.managed.allowedMCPService constraint. For more information and example use cases, see Google Cloud MCP servers Access control with IAM.

What's next