"Setting up services on a Linux virtual machine" matching MCP servers:

An MCP server that provides AI agents with a persistent, PostgreSQL-backed virtual filesystem, supporting session-isolated file operations, cross-session shared stores, and glob/grep search.

Enables an AI agent to run local tools on the user's own machine via stdio, including command execution, workspace file read/write, and system status checks.

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.

A powerful Python-based Model Context Protocol (MCP) server that enables AI assistants to manage remote Linux machines over SSH directly from your chat interface.

Enables LLMs to execute shell commands and perform file operations on a Linux system, exposing tools like execute_command, read_file, write_file, and more.

Provides AI assistants with the ability to control Linux desktop environments through tools for file management, application launching, and system operations like clipboard access. It includes a multi-level security model to manage permissions for safe, elevated, and restricted actions.

Enables remote MCP clients to access local filesystem and shell commands by deploying a Cloudflare Worker relay and a local daemon, providing tools like read/write files, exec commands, git status, etc.

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.

Enables AI clients to securely operate a local machine via MCP or REST, providing file read/write, Playwright browser automation, and shell/service administration.

Enables MCP-compatible AI clients to access local Linux files, run sandboxed commands and Git operations, capture screenshots, and manage authorized applications, with optional Unity and Blender integration.

Turns your local machine into an MCP server with a GUI, allowing agents to execute file operations, run commands (including admin), manage background processes, and query databases, with optional ngrok exposure.

Provides a persistent Docker computer with terminal, file editing, and optional desktop GUI control through a Three.js interface.

Connects ChatGPT to local files, terminals, and the desktop through MCP with Chrome integration, letting users read and edit project files, run commands, and follow tool results as they arrive. It also adds durable multi-agent workers, Goal/Loop task tracking, mid-task corrections, and Compact & Resume for carrying session and worker history into a fresh chat.

Enables ChatGPT to access approved local Windows folders, run commands, control the desktop, and manage session history through a permission-bounded MCP server.

Remote Linux operations via SSH, exposed as an MCP server, with tools for filesystem, systemd, Docker, network, and more, using per-user AD authentication and optional sudo elevation.

Enables ChatGPT Web to securely control a trusted local computer through an OpenAI Secure MCP Tunnel, letting it perform file, process, Git, and other system operations on Windows, macOS, and Linux.

A read-only remote MCP server that searches and reads files from a personal Microsoft OneDrive folder via Cloudflare Workers, enabling live access without a local workstation or tunnel.

Enables ChatGPT's web interface to operate a real Mac by executing shell commands and reading or writing files over a reverse SSH tunnel, without installing a client or consuming API quota.

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

Exposes full Linux system control to AI clients via MCP tools for bash execution, file operations, and search, with configurable permissions and audit logging.

Gives ChatGPT hands-on control of a local machine: reading, patching and executing code in approved project folders, running real interactive processes, managing plans and worker sub-chats, and driving the browser or desktop through user-granted permissions. Every capability is a switch inside an approved-folder sandbox, so nothing runs that the user did not explicitly enable.

Enables ChatGPT to work on a local project by reading and editing files, running shell commands, keeping terminals and desktop sessions open, and splitting work across durable workers whose context persists between tasks. Long-running work can be steered mid-flight, tracked with Goal, and carried into fresh chats via Compact & Resume.

Lets ChatGPT work directly on a local project by reading and editing files, running shell commands and tests, keeping terminals open, and using the user's desktop, all paired with an approved workspace folder. It also splits independent jobs across persistent workers that retain context, while riding the user's existing ChatGPT plan rather than Codex quota.

Provides a secure workspace for AI-driven file management and Python script execution designed to run on Vercel. It enables tools for full file operations and code execution within a sandboxed environment.

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 agents to compress JPEG, PNG, and WebP images to a target byte size, split PDFs by page or ranges, and remove image metadata while processing local files without sending file bytes over the network.

An HTTP MCP server that indexes large documents into exact-line-numbered sections, enabling AI models to locate, read, summarize, and edit parts of a document without ingesting the whole file.

A local MCP server that gives Claude in the browser tools to interact with a Fedora machine, including file operations, system diagnostics, build tasks, and root actions via Polkit.

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 interaction with the WhkerDB shared PDF editor to manage collaborative rooms, file structures, and real-time annotations. It allows users to upload PDFs and images, manage note trees, and sync data across platforms through natural language.