"Linux Foundation" matching MCP servers:

Exposes a Linux host with shell, Python, and filesystem tools as MCP tools over HTTPS, enabling AI agents to execute commands and manage files remotely.

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.

Provides agents with real Linux VMs to run commands, manage files, expose ports, and track costs, all through an ephemeral sandbox service.

MCP server to give every agent an ephemeral Linux sandboxes for executing shell commands.

Enables an AI agent to make music with SuperCollider by rendering code to WAV files offline with peak and RMS level stats, plus realtime playback and stopping. Runs on macOS and Linux via a subprocess, with per-call process isolation so crashes or hangs can't take the server down.

An MCP server that exposes Runloop Devbox operations as tools, enabling AI agents to create and manage Linux sandboxes, execute commands, edit files, snapshot disks, and drive interactive PTY sessions.

Enables MCP clients to manage disposable cloud Linux machines by creating and controlling them, executing commands, reading and writing files, creating snapshots, publishing ports, and issuing SSH tokens.

A local Model Context Protocol server that allows LLMs to securely execute shell commands on remote Linux and Windows systems via SSH connections.

Provides a local, isolated Linux VM sandbox for AI agents using Apple's Virtualization.framework, enabling fast command execution (~60ms) and package management without cloud costs.

Provides a ready-to-run stdio server scaffold that agents can launch via npx without installing anything, bundled into a single file with an example tool, strict tool-schema checks, tests, and bilingual documentation. It gives developers a consistent foundation for building, validating, and shipping new MCP servers that agent-kit can execute directly.

Enables AI assistants to perform controlled Linux system administration tasks like reading logs, managing services, cron jobs, WordPress, and executing sandboxed Python code, with strict security constraints.

Enables executing Bash commands, pipelines, and scripts across Windows, macOS, and Linux via MCP, with support for multiple runtimes, timeouts, working directories, and stream capture.

Enables controlling OriginLab Origin through MCP to read and write worksheets, build and style multi-panel graphs, and export figures, reporting what Origin actually did rather than what it was asked to do. Works from macOS or Linux against Origin running in a Windows VM.

A Model Context Protocol server that integrates Claude with Blender, enabling users to analyze and interact with IFC (Industry Foundation Classes) building models through natural language commands.

Enables your ChatGPT or Claude web subscription to drive real remote software engineering on a Linux server, providing isolated Git worktrees, file editing, asynchronous process execution, approvals, commits, and audit. It adds no second server-side AI API token meter.

Exposes vLLM capabilities to AI assistants, enabling chat completions, model management, and platform-aware container control with automatic detection of Docker/Podman and GPU availability across Linux, macOS, and Windows.

A basic Model Context Protocol (MCP) server implementation that provides a foundation for MCP server development. The README doesn't specify particular functionality, suggesting it may be a template or starting point for building custom MCP servers.

A starter template for building MCP (Model Context Protocol) servers that integrate with Claude, Cursor, or other MCP-compatible AI assistants. Provides a clean foundation with TypeScript support, example tool implementation, and installation scripts for quick customization.

A starter template for building custom MCP servers that can integrate with Claude, Cursor, or other MCP-compatible AI assistants. Provides a clean foundation with TypeScript support, example implementations, and easy installation scripts for quickly creating tools, resources, and prompt templates.

A foundation for building custom local Model Context Protocol (MCP) servers that provide tools accessible to AI assistants like Cursor or Claude Desktop.

Linux sandboxes for AI agents, each a Firecracker microVM. Run code, keep files, pause and wake with memory kept, snapshot and fork.

Gives an AI agent its own fleet of disposable Linux microVMs on hardware you control, with tools to spawn, execute commands in, fork from identical disk states, list, and destroy them. Enables safe parallel exploration and cleanup all within an isolated VM boundary.

Persistent Linux environments for AI agents, with interactive terminals, input-wait event feedback, background tasks, file transfers, and HTTPS previews. Connect through a local stdio MCP server. Requires an agend account; bring your own agent and model.

Turns your own VPS into an agent's remote computer, exposing shell and code execution, persistent workspace files, Bash PTYs, a Chromium browser, notes, scheduled tasks, and a streamed Linux desktop over MCP. Clients such as Claude Code or Codex can also use an authenticated REST API, CLI, and web console to drive the same shared workspace.

A remote MCP bridge that lets MCP clients use Modal sandboxes for Linux execution, dependency installation, Git operations, GPU jobs, and invoking deployed Modal Functions.

Enables AI agents to operate disposable Linux computers via MCP, providing tools for shell, file read/write, directory listing, and port exposure. It also supports browser automation and integration with any MCP-compatible client.

A TypeScript template for building Model Context Protocol servers that provides a structured foundation with automated tools, testing, and synchronization capabilities.

Disposable Firecracker microVMs as sandboxes for AI agents: run commands and move files in any OCI image, snapshot and fork running copies in ~150 ms, egress allowlists. Self-hosted; needs Linux with KVM.

Enables strict resource governance for AI agent processes using Linux cgroup v2, enforcing CPU bandwidth, memory limits, and process limits to prevent resource overconsumption, runaway loops, and fork bombs.

Enables chat clients to execute real shell commands (apt install, curl, ffmpeg, Python) inside a disposable, per-conversation Debian container and hand resulting files back to the user via signed download links. Installed packages and processes are discarded when the container recycles, while files under /workspace persist.