"A guide to managing GitLab projects" matching MCP servers:

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.

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.

Enables MCP agents to automate real GUI applications on headless desktops, providing background mouse/keyboard control, window/process management, screenshots, and safe human handoff without disturbing the user's desktop.

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

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.

MCP server that gives AI agents isolated, named Linux computers with stable IDs and lifecycle control. Supports multiple backends like Docker, Fly Machines, Modal, and E2B, with a dashboard for managing sandboxes.

MCP server for managing Ubuntu servers over SSH. Enables Claude to check system health, inspect services, tail logs, review updates, and run commands remotely.

Runs builds, tests, and benchmarks on a remote compute node via Tailscale and SSH, syncing projects with rsync.

Enables managing multiple remote Linux hosts over SSH via MCP, with command execution, SFTP file operations, and Windows DPAPI-protected credentials.

MCP server for managing Proxmox VE resources, VM/CT lifecycle, snapshots, backups, and clones via natural language.

SSH MCP is a Model Context Protocol tool for managing and interacting with multiple virtual machines over SSH. It simplifies executing commands on remote servers using the standard SSH config file format.

More than just an MCP server that covers the entire range of Docker management tasks, it helps you create workflows to easily manage your Docker environments at scale. It allows you manage multiple local or remote (TCP, TLS, SSH) docker instances and exposes things like logs and stats as resources so that you can monitor and triage all of them.

Enables AI coding agents to safely execute code, run tests, and build projects inside disposable Docker sandboxes, protecting the host machine through enforced isolation, filesystem snapshots, and network controls.

An MCP server that implements Claude Code-like functionality, allowing the AI to analyze codebases, modify files, execute commands, and manage projects through direct file system interactions.

Enables AI assistants to control Windows PCs by executing shell commands, managing files, capturing screenshots, and monitoring system information through a comprehensive set of MCP tools.