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googleapis

gcloud MCP server

Publishergoogleapis
Repositorygcloud-mcp
LanguageTypeScript
Forks
82
Stars
910
Available tools
0
Transport typestdio
Categories
LicenseApache-2.0
Links
  • Connect tools to AI workflows

    Google Cloud MCP exposes MCP capabilities that can be used by compatible AI clients and agents.

  • 0 available tools

    Browse the callable actions below, including names and descriptions when provided by the server.

  • Ready-to-copy setup

    Use the installation snippets to configure this server in your preferred MCP client.

  • Open source signals

    910 stars and 82 forks from the linked repository.

gcloud-mcp Servers CI License

gcloud MCP Server ☁️

The gcloud Model Context Protocol (MCP) server enables AI assistants to easily interact with the Google Cloud environment using the gcloud CLI. With the gcloud MCP server you can:

  • Interact with Google Cloud using natural language. Describe the outcome you want instead of memorizing complex command syntax, flags, and arguments.
  • Automate and simplify complex workflows. Chain multiple cloud operations into a single, repeatable command to reduce manual effort and the chance of error.
  • Lower the barrier to entry for cloud management. Empower team members who are less familiar with gcloud to perform powerful actions confidently and safely.

📡 Available MCP Servers

This repository also hosts other MCP servers in addition to the gcloud MCP server. An up to date list is below, and links to other Google Cloud MCP servers hosted outside of this repo are here.

MCP Server NameDescriptionPackage NameVersion
gcloudInteract with Google Cloud via the gcloud CLI using natural language prompts.gcloud-mcpVersion
observabilityAccess Google Cloud Observability APIs to query logs, metrics, and traces.observability-mcpVersion
storageInteract with Google Cloud Storage for bucket and object management.storage-mcpVersion
backupdrInteract with Google Cloud Backup and Disaster Recovery.backupdr-mcpVersion

🚀 Getting Started

Prerequisites

✨ Set up your MCP server

Gemini CLI and Gemini Code Assist

To integrate MCP servers with Gemini CLI or Gemini Code Assist, run the setup command below from your home directory for MCP server listed in the table. This will install the MCP server as a Gemini CLI extension. for the current user, making it available for all your projects.

shell
npx @google-cloud/[PACKAGE_NAME] init --agent=gemini-cli

For example, for the gcloud-mcp:

shell
npx @google-cloud/gcloud-mcp init --agent=gemini-cli

After the initialization process, you can verify that the gcloud-mcp server is configured correctly by running the following command:

gemini mcp list

> ✓ gcloud: npx -y @google-cloud/gcloud-mcp (stdio) - Connected

For other AI clients

To use MCP servers in this repo with other clients, add the following snippet to their respective JSON configuration files for each MCP server:

json
"[SERVER_NAME]": {
  "command": "npx",
  "args": ["-y", "@google-cloud/[PACKAGE_NAME]"]
}

For example, for gcloud:

json
"gcloud": {
  "command": "npx",
  "args": ["-y", "@google-cloud/gcloud-mcp"]
}

Instructions for popular tools:

  • Claude Desktop: Open Claude > Settings > Developer > Edit Config and edit claude_desktop_config.json.
  • Cline: Click the MCP Servers icon, then Configure MCP Servers to edit cline_mcp_settings.json.
  • Cursor: Edit .cursor/mcp.json for a single project or ~/.cursor/mcp.json for all projects.
  • Gemini CLI (Manual Setup): If not using extensions, edit .gemini/settings.json for a single project or ~/.gemini/settings.json for all projects.

For Visual Studio Code edit the .vscode/mcp.json file in your workspace for a single project or your global user settings file for all projects:

json
"servers": {
  "[SERVER_NAME]": {
    "command": "npx",
    "args": ["-y", "@google-cloud/[PACKAGE_NAME]"]
  }
}

For example, for gcloud and observability:

json
"servers": {
  "gcloud": {
    "command": "npx",
    "args": ["-y", "@google-cloud/gcloud-mcp"]
  },
  "observability": {
    "command": "npx",
    "args": ["-y", "@google-cloud/observability-mcp"]
  },
}

🛠 Local Development

For more information regarding installing the repository locally, please see development.md

🧰 Available MCP Tools

MCP ServerToolDescription
gcloudrun_gcloud_commandExecutes a gcloud command. Some commands have been restricted from execution by the agent. See MCP Permissions for more information.
observabilitylist_log_entriesLists log entries from a project.
list_log_namesLists log names from a project.
list_bucketsLists log buckets from a project.
list_viewsLists log views from a project.
list_sinksLists log sinks from a project.
list_log_scopesLists log scopes from a project.
list_metric_descriptorsLists metric descriptors for a project.
list_time_seriesLists time series data for a given metric.
list_alert_policiesLists the alert policies in a project.
list_tracesSearches for traces in a project.
get_traceGets a specific trace by id in a project.
list_group_statsLists the error groups for a project.
storagelist_objectsLists objects in a GCS bucket.
read_object_metadataReads comprehensive metadata for a specific object.
read_object_contentReads the content of a specific object.
delete_objectDeletes a specific object from a bucket.
write_objectWrites a new object to a bucket.
update_object_metadataUpdates the custom metadata of an existing object.
copy_objectCopies an object from one bucket to another.
move_objectMoves an object from one bucket to another.
upload_objectUploads a file to a GCS bucket.
download_objectDownloads an object from GCS to a local file.
list_bucketsLists all buckets in a project.
create_bucketCreates a new bucket.
delete_bucketDeletes a bucket.
get_bucket_metadataGets comprehensive metadata for a specific bucket.
update_bucket_labelsUpdates labels for a bucket.
get_bucket_locationGets the location of a bucket.
view_iam_policyViews the IAM policy for a bucket.
check_iam_permissionsTests IAM permissions for a bucket.
get_metadata_table_schemaChecks if GCS insights service is enabled and returns the BigQuery table schema for a given insights dataset configuration.
execute_insights_queryExecutes a BigQuery SQL query against an insights dataset and returns the result.
list_insights_configsLists the names of all Storage Insights dataset configurations for a given project.
backupdrlist_backup_vaultsLists all backup vaults in a given project and location.
get_backup_vaultGets details of a specific backup vault.
list_backup_plansLists all backup plans in a given project and location.
get_backup_planGets details of a specific backup plan.
list_backup_plan_associationsLists all associations between backup plans and resources.
get_backup_plan_associationGets details of a specific backup plan association.
list_datasourcesLists all data sources within a backup vault.
get_datasourceGets details of a specific data source.
list_backupsLists all backups for a given data source.
get_backupGets details of a specific backup.
find_protectable_resourcesDiscovers resources (VMs, Disks, SQL) that can be protected.
get_backupdr_operationRetrieves the status of a long-running BackupDR operation.
get_csql_operationRetrieves the status of a long-running Cloud SQL operation.
create_backup_vaultCreates a new backup vault in a specified location.
create_backup_planCreates a new backup plan with defined rules and retention.
update_backup_planModifies an existing backup plan.
create_backup_plan_associationAssociates a resource with a backup plan to start protection.
restore_backupRestores a backup to a target Compute Engine instance or disk.
csql_restoreRestores a Cloud SQL backup to a target instance.
delete_backup_vaultDeletes a backup vault.
delete_backup_planDeletes a backup plan.
delete_backup_plan_associationRemoves protection from a resource by deleting its association.
delete_backupDeletes a specific backup from a vault.

🔑 MCP Permissions

The permissions of the gcloud MCP are directly tied to the permissions of the active gcloud account. To restrict permissions and operate with the principle of least privilege, you can authorize as a service account using impersonation and assign the service account a role with limited permissions.

By default, the gcloud MCP prevents execution of gcloud commands that don't make sense for AI agents. This is done to restrict commands that can run arbitrary inputs and initiate interactive sessions. See here for the list of denied commands.

💫 Other Google Cloud MCP Servers

Google Cloud offers these other servers:

👥 Contributing

We welcome contributions! Whether you're fixing bugs, sharing feedback, or improving documentation, your contributions are welcome. Please read our Contributing Guide to get started.

📄 Important Notes

This repository is currently in preview and may see breaking changes. This repository provides a solution, not an officially supported Google product. It is not covered under Google Cloud Terms of Service. It may break when the MCP specification, other SDKs, or when other solutions and products change. See also our Security Policy.

Use Google Cloud MCP MCP with multiple AI models

TypingMind connects MCP tools at the workspace level, so once Google Cloud MCP is connected, you can use it with different AI models in TypingMind instead of setting it up separately for each model. This MCP runs locally through the TypingMind MCP connector on your device.

Setup guide to use the local connector

Use this when the MCP server needs access to local files, apps, or private resources on your computer.

1

Open the MCP settings

In TypingMind, go to Settings, Advanced Settings, then Model Context Protocol and choose Setup Connector.

  1. Open TypingMind in your browser.
  2. Click the Settings icon.
  3. Go to Advanced Settings.
  4. Open the Model Context Protocol section.
  5. Click Setup Connector and choose This Device.
TypingMind MCP connector setup screen with This Device selected
2

Run the connector command

Choose This Device, copy the command from TypingMind, and run it in Terminal. Keep the process running while you use MCP.

  1. Copy the setup command shown by TypingMind.
  2. Open Terminal on macOS or Windows Terminal on Windows.
  3. Paste and run the command.
  4. Approve the package install if Terminal asks you to proceed.
  5. Keep the Terminal window running while using MCP tools.
3

Add Google Cloud MCP as a server

When the connector status is Ready, click Edit Servers and paste the MCP server configuration.

  1. Wait until the connector status shows Ready.
  2. Click Edit Servers.
  3. Paste the Google Cloud MCP MCP server configuration.
  4. Save the server list.
  5. Refresh if you want to confirm the connector is still ready.
TypingMind MCP settings showing active server and Edit Servers button
{
  "mcpServers": {
    "gcloud-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "<mcp-server-package>"
      ]
    }
  }
}
4

Use it across models

Save the server list, open Plugins, enable the Google Cloud MCP MCP tools, then select any supported AI model in TypingMind and use the tools in chat or assign them to an AI agent.

  1. Open the Plugins page in TypingMind.
  2. Enable the Google Cloud MCP MCP tools.
  3. Start a chat and choose the AI model you want to use.
  4. Use the MCP tools in chat or assign them to an AI agent.
  5. Switch to another AI model whenever needed without reconnecting MCP.
TypingMind chat using enabled MCP tools with a selected AI model
Can you use Google Cloud MCP to help me with this task?
Google Cloud MCP
Sure. I read it.
Here is what I found using Google Cloud MCP.

Frequently asked questions

What is the Google Cloud MCP MCP server used for?

Google Cloud MCP is an MCP server that lets compatible AI clients connect to external tools and context. In TypingMind, you can add this MCP server once and make its tools available in your AI workspace.

Can I use Google Cloud MCP MCP with multiple AI models in TypingMind?

Yes. TypingMind connects MCP tools at the workspace level, so you can use Google Cloud MCP with different AI models such as Claude, ChatGPT, Gemini, or other models you have configured in TypingMind without setting up the MCP server separately for each model.

Why use Google Cloud MCP MCP with TypingMind?

TypingMind is one of the best frontends for LLM chat because it brings multiple AI models, prompts, plugins, AI agents, API keys, and MCP tools into one workspace. With Google Cloud MCP connected, you can use its MCP tools across your preferred models while keeping your chat workflow organized in TypingMind.

How do I connect Google Cloud MCP MCP to TypingMind?

Google Cloud MCP runs through the TypingMind local MCP connector. This is best when the MCP server needs access to local files, desktop apps, command-line tools, or private resources on your computer.

What tools does Google Cloud MCP MCP provide in TypingMind?

Google Cloud MCP exposes MCP capabilities that can be enabled from the TypingMind Plugins page and used in chat or assigned to AI agents.

Do I need to share my API keys with TypingMind to use Google Cloud MCP MCP?

No. TypingMind is local-first and lets you keep your model providers, API keys, prompts, and MCP configuration under your control. If Google Cloud MCP requires authentication, add the required headers, OAuth settings, or local configuration for that MCP server when you create the connection.

Related MCP Servers

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