"Executing code in a sandbox environment" matching MCP servers:

MCP server for E2B code sandbox, enabling creation, management, and execution of code in secure sandboxes using Python and shell commands.

Creates short-lived, offline JavaScript sandbox containers for safe code execution via MCP, with no network access and strict resource limits.

An extensible Message Communication Protocol server that provides secure code execution capabilities in isolated sandbox environments, compatible with Claude for Desktop and other MCP clients.

A secure Model Context Protocol server that allows AI assistants and LLM applications to safely execute Python and JavaScript code snippets in containerized environments.

Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.

Enables code execution in isolated Docker containers with persistent IPython, Node.js, or R kernels, supporting file import/export and cross-session transfers via MCP tools.

Enables AI coding agents to safely run commands, install dependencies, test, and modify code inside disposable, policy-enforced Docker sandboxes isolated from the host, returning results and diffs.

Enables AI coding agents to safely execute code, run tests, and build projects inside disposable Docker sandboxes, protecting the host machine through enforced isolation, filesystem snapshots, and network controls.

Connects AI assistants to Swiss environmental data from BAFU, including air quality, hydrology, natural hazards, and open datasets, with no API keys required.

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.

Production-ready MCP server for secure Python code execution with artifact capture, virtual environment support, and LM Studio integration.

Execute scientific scripts (R, Python, GMT, LaTeX, Octave, Julia, etc.) in isolated, disposable containers with no host filesystem access.

Policy-driven MCP servers that split web fetching and code execution into isolated containers, preventing prompt injection and data exfiltration by ensuring neither can both execute code and reach the network.

A lightweight MCP server that enables any MCP client to execute Python code safely in a sandboxed environment, with automatic matplotlib inline image return and temporary file isolation.

Enables AI agents to create, manage, and execute code in isolated Firecracker microVM sandboxes via the MCP protocol, with support for sandbox lifecycle and file operations.

An interactive Python code execution environment that allows users and LLMs to safely execute Python code and install packages in isolated Docker containers.

A sandbox MCP server for testing and debugging MCP tools and interactions via the MCP Inspector.

Provides a secure, containerized Python sandbox for executing LLM-generated code with multi-layer isolation, along with JSON/CSV validation and workspace state snapshots.

Call OpenAI Codex from Claude Code for independent second opinions, structured code review, and delegated coding tasks through a FastMCP plugin that drives the codex CLI safely.

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.