"A search query to learn about Google Cloud services" matching MCP servers:

Open-source MCP server for managing Hetzner Cloud infrastructure with two management layers: * Layer 1 — Hetzner Cloud API (35 tools): Server power control, metrics, snapshots, backups, firewalls, DNS zones and records, rescue mode, server rebuild and rescale. Works even when the server OS is unresponsive. * Layer 2 — SSH (25 tools): Service management (systemd), Nginx config and reload,

Installs an agent on Linux hosts to allow shell commands, file edits, and service management through Claude.ai or ChatGPT via a secure outbound WebSocket connection.

Provides ChatGPT with a persistent, VM-like Linux engineering workstation inside Docker, with root access, development tools, and a private nested Docker daemon.

Enables AI agents to control and interact with a local Windows computer through authenticated MCP tools, including file operations, shell commands, process management, HTTP requests, and system/media inspection.

Enables AI agents to securely connect to and manage remote servers via SSH, with tools for running commands, file operations, log inspection, service management, and system monitoring under configurable access controls and audit logging.

Persistent, secure LXC sandbox environments for AI agents with native MCP support.

Enables interaction with Google Cloud Platform services through gcloud CLI commands via a Cloud Run deployed MCP server. Supports executing gcloud commands and managing GCP resources through natural language.

Enables LLMs to create, destroy, and manage AWS EC2 instances, including running commands via interactive shells.

A Model Context Protocol server that allows language models to manage Hetzner Cloud resources through structured functions, including servers, volumes, firewalls, and SSH keys.

A lightweight service that enables AI assistants to execute AWS CLI commands through the Model Context Protocol (MCP), allowing AI tools to retrieve AWS documentation and interact with AWS services.

Manages AWS, Azure, and Hetzner Cloud resources, and executes remote SSH commands via MCP integration.

MCP server for the Synapse ecosystem that exposes the Synapse Memory API, Browser Proxy, and SSH Proxy as MCP tools, enabling multi-tenant, stateful interactions via stdio or HTTP/SSE.

A local MCP runtime for macOS and Windows that enables ChatGPT to read, edit, search, run tests, and capture screenshots in your local project folder while keeping your source code on your machine.

Provides secure filesystem access for AI models through the Model Context Protocol with strict path validation, file operations, directory management, and system command execution within predefined directories.

A simple MCP server that exposes a terminal tool, allowing AI agents to execute shell commands.

A passive MCP server that exposes a toolbox of executable tools (shell, network, HTTP, AI search, SSH, S3 file operations) to autonomous agents via Streamable HTTP, with strong security features including Docker sandboxing and WAF.

The m2m-mcp-server-ssh-client provides a unified MCP server interface, securely connecting via SSH to the m2m-mcp-server-ssh-server back-end which aggregates MCP servers. This setup offers secure, single-point access to diverse remote tools using robust SSH key-based authentication.

An MCP server that enables AI assistants to execute shell commands and manage files on remote servers via SSH. It supports recursive directory uploads, archive extraction, and direct file creation from text content.

A self-hosted MCP server that enables AI coding agents to read, edit, search, and run code in local projects with human review loops and policy controls.

Enables MCP-compatible AI clients to connect to a Windows machine for file operations, PowerShell execution, Git inspection, task management, memory, and skills.