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

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.

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 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.

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

Enables agents and MCP clients to safely execute and verify multi-language code in a deterministic sandbox, using AST security analysis to block dangerous system calls and capture isolated execution telemetry.

Enables agents and MCP-compliant clients to run multi-language code in a deterministic, zero-dependency sandbox where AST security analysis blocks dangerous syscalls and isolated execution telemetry is captured. It exposes this through JSON-RPC 2.0 stdio, letting frameworks like LangGraph, CrewAI, Cursor, and Claude Desktop validate and execute agent-generated code safely.

A deployable MCP server for Cloudflare Workers or Azure Functions that provides example tools (time, echo, math), prompt templates for code assistance, and configuration resources. Enables AI assistants to interact with edge-deployed services through the Model Context Protocol.

An MCP Server that enables users to interact with Google's On-Demand Scanning API (v1beta1) through natural language, likely providing capabilities for vulnerability scanning and security analysis of containers and artifacts.

Enables remote Python code execution via the Model Context Protocol for secure, real-time lab scenarios and sandbox testing. It allows AI clients like Claude and Cursor to execute and evaluate code within a virtual lab environment.

A boilerplate project for quickly developing Model Context Protocol (MCP) servers using TypeScript SDK, with examples of tools (calculator, greeting) and resources (server info).

Enables AI assistants to run, list, and analyze Lupa test suites, returning structured JSON results for debugging.

Enables GUI automation by executing Python code with instrumented pyautogui, providing MCP-friendly output and inline screenshot delivery.

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.

A server implementation for the Model Context Protocol (MCP) that runs on Cloudflare Workers and supports OAuth login, allowing Claude AI to use custom tools through local or remote connections.

Enables deployment of MCP servers on Cloudflare Workers with OAuth authentication and remote connectivity. Provides a template for creating cloud-hosted MCP servers that can be accessed by Claude Desktop and other MCP clients over HTTP/SSE.

A serverless deployment for Model Context Protocol (MCP) on Cloudflare Workers that enables AI agents to use custom tools without authentication requirements.

A tool that allows deployment of a remote MCP (Model Context Protocol) server on Cloudflare Workers without authentication requirements, enabling connection to AI models through Claude Desktop or Cloudflare AI Playground.

Enables agents to accomplish multi-step work by writing a single sandboxed JavaScript program that drives shell, filesystem, HTTP, and every tool of any configured MCP server, returning only a small summary instead of raw JSON. This collapses dozens of advertised tool schemas into one tool, cutting per-request context by roughly 26× while keeping disabled upstream servers reachable.

Gives AI coding agents typed, mode-gated access to a real WordPress site — content, media, users and settings via core REST, plus PHP execution, SQL queries, filesystem operations and Elementor page read/build/patch/snapshot via a companion bridge plugin. Destructive actions and code execution are guarded by feature flags, explicit confirm literals, dry runs, redaction and audit logging.

A simple MCP server implementation in TypeScript that communicates over stdio, allowing users to ask questions that end with 'yes or no' to trigger the MCP tool in Cursor.

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.

Facilitates interaction with AWS Bedrock-enabled tools by implementing Anthropic's Model Context Protocol for seamless tool integration and communication.

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.

Enables AI agents to interact with Jupyter notebooks via MCP tools for querying, modifying, executing, and setting up notebooks, with state preservation and real-time collaboration.

Enables AI agents to interact with Bentley STAAD.Pro models for tasks like load case definition, data extraction, and property setting through natural language.

Enables AI coding agents to safely execute commands, run tests, and modify project files inside disposable, policy-enforced Docker sandboxes that are isolated from the host machine and its credentials.

A Python code execution sandbox based on IPython and Docker. Stateful code execution, file transfer between host and container, configurable network access.

A secure, containerized workspace for LLMs to autonomously code, test, and debug projects without risking the host machine.

An MCP server that manages an IPython kernel, allowing LLMs to execute Python code in a project's virtual environment.

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