"How to add documentation to a project or system" 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.

Enables system automation and control including command execution, process management, network tools, environment variables, disk usage, and service status.

Enables AI to execute registered system commands across platforms using a stable program + args interface, without requiring shell-specific syntax or tool discovery.

A comprehensive Model Context Protocol server that enables AI assistants to interact with and manage Windows systems, providing capabilities for file system operations, process management, system information retrieval, registry operations, service management, network diagnostics, and performance monitoring.

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.

Enables MCP clients to execute commands, edit files, run Git workflows, and perform validated independent reviews in real development environments through Docker-based runtimes with separated developer and auditor permissions.

Enables LLMs to monitor system health, manage processes, execute shell commands, and inspect environment variables and directories.

Enables AI agents to monitor system resources and control media, brightness, volume, and other Windows PC functions through natural language commands.

MCP server that enables AI models to open the Windows Notepad application via function calling, demonstrating the complete MCP tool call workflow.

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.

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

A tutorial MCP server for learning the Model Context Protocol by building file and system tools. Provides hands-on experience creating custom tools that enable AI models to interact with files and execute system commands.

Enables AI agents to interact with the Linux desktop through desktop notifications, interactive dialogs, shell command execution, and privileged command execution.

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.

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.

Provides 180+ tools for full system control including terminal, Git, Docker, Kubernetes, VMs, BIOS, kernel, cybersecurity, AI, and cloud, designed to work with MCP clients like Cursor and Claude Desktop.

Your sysadmin co-pilot — an AI that administers Linux through typed, approval-gated, Ed25519-audited actions instead of shell strings. Reference implementation of the LACS standard.

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.