"A system or tool for enabling computer vision capabilities in an MCP (Microcontroller Platform)" matching MCP servers:

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.

An MCP server that provides AI agents with a full Ubuntu desktop environment inside Docker, enabling them to perform complex computer tasks like browsing, coding, testing, and GUI automation.

Enables monitoring of Ubuntu system metrics, Docker containers, and public IP drift, with Feishu alerting and HTTP/MCP tools for querying status, viewing history, and restarting containers.

Gives a coding agent its own Docker-hosted Linux desktop with XFCE, Chromium, Blender, LibreOffice, GIMP and a sudo shell, exposing tools for screenshots, mouse and keyboard input, window focus, shell commands, file read/write, opening URLs or GUI apps, and headless Blender renders. The agent can watch and drive that desktop live from a browser while working on the shared project folder.

Enables deploying multiple isolated Odoo MCP connector containers from a single shared codebase, each with its own configuration and credentials for different clients.

MCP server for the Agent Platform API, enabling management of sandbox environments through MCP protocol. Exposes tools to interact with sandbox lifecycle operations such as start, stop, and status checks.

Enables MCP agents to automate real GUI applications on headless desktops, providing background mouse/keyboard control, window/process management, screenshots, and safe human handoff without disturbing the user's desktop.

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

Cloud Linux desktops for AI agents. Create a computer, see its screen through screenshots, and drive it with click, type, drag and keys, or hand off a whole task with run_agent. Also hosted with OAuth at https://app.mandala.computer/mcp.

System Initiative provides AI Native Infrastructure automation that gives you clarity over your running infrastructure, control to change, automate, and integrate it, with conifdence

Enables AI coding agents to perform automated Blender UI testing, user-story validation, and computer-use workflows inside an isolated GPU-partitioned Windows 11 VM without disrupting the host desktop.

A Model Context Protocol server that enables AI assistants to interact with Kubernetes clusters through natural language, supporting core Kubernetes operations, monitoring, security, and diagnostics.

A VMware ESXi/vCenter management server based on MCP (Machine Control Protocol), providing simple REST API interfaces for virtual machine management.

Provides 180+ tools for full system control including terminal, Git, Docker, Kubernetes, VMs, BIOS, kernel, cybersecurity, AI, and cloud, designed to work with MCP clients like Cursor and Claude Desktop.

Enables AI assistants to create isolated Kubernetes Pod sessions for real-time shell command execution, file management, and Node.js code execution, with automatic TTL-based cleanup.

Enables AI assistants to manage Docker/Podman deployments through Komodo, supporting container management, server orchestration, and infrastructure control via natural language.

A Node.js application that provides a JSON-RPC interface to interact with CO2 sensor data, working in both simulation mode and with real Raspberry Pi Pico hardware connections.

More than just an MCP server that covers the entire range of Docker management tasks, it helps you create workflows to easily manage your Docker environments at scale. It allows you manage multiple local or remote (TCP, TLS, SSH) docker instances and exposes things like logs and stats as resources so that you can monitor and triage all of them.

A virtual Linux desktop as an MCP server, shipped in Docker. Agents drive screen, mouse, keyboard across any GUI — browsers, IDEs, office suites, Wine/Windows apps, legacy software — many in parallel.

A read-only server that enables AI agents to triage Kubernetes CronJobs in a single tool call: whether runs happened and stayed on schedule, whether durations are trending slower or overrunning, which jobs have silently stopped succeeding, and why a given run failed, with evidence and stated basis for each verdict. It also lists active runs and recent failures while enforcing namespace allowlists and no write access.

Read-only MCP adapter for the Dockhand REST API, enabling secure querying of container environments, networks, stacks, and runtime status via STDIO or authenticated HTTP.

Let your AI build and run a whole app on your computer: an online shop, website, booking app or AI helper. It creates the parts, starts them, checks they work and fixes problems.

Enables AI agents to control virtual computers through Orgo, supporting computer management, screen actions, shell commands, file operations, streaming, and AI completions.

An MCP server for TrueNAS that lets users manage ZFS pools, datasets, shares, snapshots, VMs, alerts, and network configuration through natural language, using a single hierarchical tool exposing 278 actions.

Enables querying and managing a Calagopus game server panel via natural language, covering users, servers, nodes, eggs, databases, backups, and system health. Write operations are opt-in and require explicit confirmation.

MCP server for Beaker lab automation, enabling system provisioning, job management, distro discovery, and failure diagnosis through 25 tools.

Enables an assistant to control a Tandy Color Computer 3 emulated in MAME, allowing typing, disk operations, screen viewing, and state management.

Enables AI agents to connect to and control a Virtual Private Server via SSH. It provides a comprehensive suite of tools for remote file system operations, session management, and shell command execution.

An MCP server implementation that integrates with the Civo cloud platform API, providing capabilities to manage cloud instances, networks, and Kubernetes clusters.

Exposes vLLM capabilities to AI assistants, enabling chat completions, model management, and platform-aware container control with automatic detection of Docker/Podman and GPU availability across Linux, macOS, and Windows.