"Information about the Justfile Command-Line Tool" matching MCP servers:

A secure Model Context Protocol server that allows AI models to safely interact with Windows command-line functionality, enabling controlled execution of system commands, project creation, and system information retrieval.

Allows AI assistants to safely execute common Unix/macOS terminal commands through a controlled interface with comprehensive security features.

Enables execution of shell commands, directory navigation, and interactive program automation through an enhanced command-line interface. Provides tools for running commands, managing working directories, and handling interactive programs using expect/send sequences.

A proxy server that enables AI assistants to run and interact with command-line applications like Expo through the Model Context Protocol (MCP), capturing logs and allowing keypress forwarding.

Enables executing bash commands with foreground/background support, timeouts, environment control, and OpenTelemetry observability.

A Model Context Protocol server that enables remote execution of shell commands across different operating systems, automatically handling platform-specific differences between Windows and Unix-like systems.

Provides tools for executing shell commands both synchronously and asynchronously with real-time output streaming and process management capabilities. It enables users to start background tasks, monitor progress, and manage long-running processes via Stdio or HTTP transports.

SSH automation MCP server that enables Claude and ChatGPT to execute commands, manage files, install packages, and control services on remote servers over SSH — supporting password, key, and agent authentication.

A Model Context Protocol (MCP) SSH client server that provides autonomous SSH operations for GitHub Copilot and VS Code. Enable natural language SSH automation without manual prompts or GUI interactions.

Lets an LLM see what's on your screen by capturing an entire monitor or a specific application window.

Provides comprehensive system diagnostics and hardware analysis through 10 specialized tools for troubleshooting and environment monitoring. Offers targeted information gathering for CPU, memory, network, storage, processes, and security analysis across Windows, macOS, and Linux platforms.

Provides real-time system metrics and information through a Model Context Protocol interface, enabling access to CPU usage, memory statistics, disk information, network status, and running processes.

Provides tools to monitor host system health including CPU load, disk usage, and network status while enabling file system management tasks like searching and moving files. It includes built-in safety guards to prevent operations on critical system directories.

A secure Model Context Protocol server that allows AI assistants to execute terminal commands with controlled directory access and command permissions. It features a robust security architecture including whitelisting, session IDs, and categorized command levels to ensure safe system interaction.

Give any LLM agent a real Android or iPhone. 62 MCP tools: tap, swipe, type, screenshot, screen-tree reading, app launch, camera, TTS, crash reports, batched execution. Android via ADB, iPhone via WebDriverAgent, on-device inference, Docker+KVM emulators. Works with Claude Code, Cursor, LangChain, LlamaIndex, and any MCP client. MIT.

An MCP server that enables users to execute arbitrary shell commands on their local machine and receive the output. It provides a terminal tool for running system commands through MCP-compatible clients using the Python SDK.

Provides safe, controlled access to network and system diagnostic tools including ping, nmap, dig, traceroute, curl, and whois for troubleshooting connectivity, scanning ports, and performing DNS lookups through whitelisted commands with security validation.

Enables AI assistants to interact with physical serial port devices across platforms (Windows COM/Linux tty) with support for asynchronous communication, URC pattern recognition, and structured logging.

Enables AI models to perform file system operations (reading, creating, and listing files) on a local file system through a standardized Model Context Protocol interface.

Enables reading and writing macOS system defaults and settings through commands equivalent to the defaults command-line tool. Supports listing domains, searching for settings, and modifying system preferences programmatically.