"Learning to Create an Expert Python Program to Exit GitHub" matching MCP servers:

Enables AI assistants to manage OpenStack cloud resources including compute, images, identity, network, and block storage via the Model Context Protocol.

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

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.

An MCP server that enables LLMs to execute Python code on GPU-accelerated compute nodes within SLURM-managed HPC environments. It bridges local clients to remote clusters by launching JupyterLab sessions via SLURM jobs to facilitate high-performance notebook-based computation.

An MCP server that lets AI agents manage virtual machines across local and cloud targets, with tools to create machines, run commands, transfer files, and clean up ephemeral instances via natural language.

A Model Context Protocol server integrating DevOps tools for GitHub, Docker, and filesystem operations.

Provides programmatic access to Xcode functionality, enabling AI assistants to create, build, test, and manage iOS/macOS projects directly.

Create sandboxed local development environments directly from Github and execute tests, see coverage and more! Supports Python, Node, Bun and many test runners.

An MCP server that enables seamless management of Novita AI platform resources, currently supporting GPU instance operations (list, create, start, stop, etc.) through compatible clients like Claude Desktop and Cursor.

An MCP server that enables agents to attach debugpy to running Python processes inside Docker containers for enhanced debugging and inspection. It provides tools for container autodiscovery, process injection, and generating breakpoint plans based on logs and metadata.

Enables natural language management of Latitude.sh infrastructure, allowing users to list, create, and delete servers, projects, and SSH keys through MCP-compatible clients.

Enables agents to deploy, run, expose, inspect, and manage isolated cloud environments, including databases, caches, browsers, GitHub-hosted apps, and arbitrary OCI images, via MCP tools.

An MCP server to create secure code sandbox environment for executing code within Docker containers.

Enables managing RunPod compute for the Learning-to-Swim replication through natural language, including pod lifecycle, SSH/rsync file transfer, detached training jobs, and supervised runs with cost guardrails.

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 AI coding agents to create disposable, isolated Firecracker MicroVM sandboxes on AWS Lambda, execute commands, transfer files, and tear them down when done.

A Python-based MCP server for interacting with Proxmox hypervisors, enabling management of nodes, VMs, containers, and executing commands via QEMU Guest Agent.

A Python-based server that enables AI assistants to interact with an Unraid server through the official Unraid GraphQL API, providing read-only access to system information, Docker containers, VMs, storage, and more.

An enhanced Python-based MCP server that enables complete VM lifecycle management and monitoring of Proxmox virtualization platforms through natural language, with 11 REST API endpoints for seamless integration.

A server tool for running Deep Learning models that offers Shell execution, Ngrok connectivity, and Docker container hosting with support for multiple AI frameworks including Anthropic, Gemini, and OpenAI.