"Information about sandbox environments or games" matching MCP servers:

Enables AI agents to validate and publish game bundles to the Hypercho Games marketplace, including uploading browser builds, banners, screenshots, and metadata.

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.

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.

Salesforce sandbox seeding, built for AI agents. SOQL-driven org-to-org record copy with automatic dependency-graph walking, cross-org FK remapping, and a hard AI-never-sees-your-data boundary.

A local, keyless MCP server simulating Forge's industrial telemetry kernel, enabling agents to normalize vendor-specific machine data, forecast breaches, and test integrations against realistic equipment data without credentials or persistence.

MCP server allowing two agents to play chess or Connect Four against each other, with a live rendered board and emotion signaling.

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.

Enables AI agents to run untrusted, model-generated Python code in a locked-down throwaway Docker container with enforced isolation and resource limits. Supports starting, checking, and stopping asynchronous runs while returning structured status reports without exposing the host, network, or Docker controls.

Enables AI coding agents to run Kubernetes inspection and Terraform plan/apply operations inside ephemeral gVisor-sandboxed jobs with short-lived, narrowly-scoped credentials, while routing destructive changes through a human approval gate.

Validates a built HTML5 game against CrazyGames portal rules — build checks, an emulated SDK v3 session, and frame/layout audits — returning dense JSON lists of failures, warnings and rules it could not exercise instead of screenshots or ad-hoc Playwright scripts. It emulates the portal entirely in-process (no server or port) using the game's own Playwright/Chromium, with a small per-game scenario file driving gameplay steps.

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

Exposes tools over stdio that let an AI agent create disposable Docker sandboxes to run commands and stream output, read and write files, and take snapshots, roll them back, fork them, list, and destroy them. It tells the model to snapshot before risky changes so failed attempts can be undone or explored in parallel on self-hosted infrastructure.

Enables MCP-compatible AI clients to run curated Kali Linux security tools such as nmap, gobuster, nikto, sqlmap and netcat inside an isolated microVM sandbox. Commands execute as asynchronous jobs with readable output, cancellation and persistent per-workspace storage, with configurable network, CPU and memory limits.

Enables MCP-capable agents to execute generated Python, Node.js, or shell code inside disposable, network-isolated Docker containers with strict resource, filesystem, privilege, and time limits, returning structured results and errors.

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

A Model Context Protocol (MCP) server that enables LLMs to run ANY code safely in isolated Docker containers.

自托管的MCP服务器，在Docker沙箱中提供命令执行、后台长任务、大文件传输和多沙箱自动管理，专为AI助手设计。

Spins up isolated opencode instances in Docker containers as MCP servers, providing 80 tools for file and shell operations within a scoped workspace without exposing the host filesystem.

Enables running arbitrary JavaScript code in isolated Docker containers with on-the-fly npm dependency installation, supporting both ephemeral one-shot executions and persistent sandbox environments.

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

Enables Claude to create local, persistent mock REST APIs on demand from plain-language descriptions, with data written to disk so it survives restarts.

Sandboxed Python execution with automatic dependency management. Executes Python scripts in isolated environments (bubblewrap or Docker) with PEP 723 inline dependencies, preventing host pollution.

Enables assistants to analyze files and URLs for malware by integrating with security services like VirusTotal and ANY.RUN, returning threat reports.

Enables coding agents and CI/CD pipelines to audit untrusted code by detecting Python sandbox escapes, SQL injection taint, ReDoS regex vulnerabilities, and CORS misconfigurations, while generating Seccomp BPF filters for safer execution.

A Docker-based MCP server that enables AI to securely run Foundry test commands in isolated containers, with automatic dependency management and cleanup.

Gives an LLM a secure local workspace where it can write files, execute Python in a locked-down Docker container with no network access, and run read-only SQLite queries. Lets the model run, debug, and fix its own code without risking the host machine.

Enables running arbitrary JavaScript code in isolated Docker containers with on-demand npm dependency installation, allowing for ephemeral script execution and long-running services with controlled resource limits.

Enables AI coding agents to audit and sweep away transient scratch directories, bloated test caches, and forgotten probe scripts in a workspace, with dry-run previews to guarantee nothing tracked or production-related is touched. It also detects zombie background dev servers still holding common ports like 3000, 5173, and 8000.

Enables AI coding agents to work inside a disposable Windows guest desktop, where they can run shell commands, launch applications, inspect and operate native GUI controls, perform browser DOM actions, and transfer files. Fresh observations, PNG screenshots, and hashed artifacts let agents verify results without touching the host desktop.