"Testing Local AI Agents in LMStudio for Computer Use and Code Execution Tasks" matching MCP servers:

MCP server for Windows computer use, providing low-token, low-latency automation through UIA/Win32, OCR, and vision. It enables batch actions, shortcuts, isolated desktop execution, and browser control via MCP tools.

Enables a constrained subagent to perform foreground GUI actions on Hyprland—observing, focusing windows, clicking, typing, scrolling, dragging, and managing the clipboard—under per-action approval with lease, audit log, and kill-switch gating.

MCP server providing 200x faster macOS computer use via Accessibility API, enabling direct UI automation (click, type, scroll) with ~1s workflows.

Enables AI agents to manage n8n workflow automation instances through tools for workflow CRUD operations, execution monitoring, and webhook triggering. It facilitates programmatic interaction with n8n instances via the n8n API with AI-optimized descriptions and error handling.

Enables AI agents to observe and control the user's live desktop by listing apps, reading accessibility trees, and performing clicks, typing, scrolling, dragging, and other input actions with per-action approvals and local audit archives.

An MCP server for computer automation that provides tools for screenshots, mouse actions, keyboard input, and drag-and-drop functionality. It supports cross-platform desktop interaction for both Linux (X11) and Windows environments.

Enables any compatible MCP harness to control macOS apps through OpenAI's Computer Use backend, with explicit approval checks.

Lets AI agents see and control desktop applications through the accessibility layer, enabling clicking, typing, scrolling, dragging, and window/app management across macOS, Windows, and Linux entirely on the local machine.

Enables AI agents to control Linux/X11 desktops by providing tools for taking screenshots, clicking, typing, and managing windows via AT-SPI and xdotool.

Enables macOS GUI automation via screenshots, mouse/keyboard control, accessibility tree inspection, and app/window management.

Provides native macOS computer control tools including mouse and keyboard simulation, screenshot capture, and application management for MCP-compatible agents. It enables AI assistants to directly interact with the macOS operating system and installed apps through standard tool calls.

Enables low-cost agent models to control Windows applications through a compact, state-safe proxy over Open Computer Use, reducing model-visible context by up to 99.8% with support for record/replay and reusable UI component memory.

Enables users to drive a selected Windows application toward a natural-language goal by reading the UI with local OCR/UIA and deciding next actions.

MCP stdio server that lets DSH agents see and operate Windows desktop apps via UI Automation, screenshots, mouse/keyboard, OCR, and window management.

A standalone MCP server for Windows desktop control, enabling screenshots, mouse and keyboard input, app launch, window/display management, and clipboard access via natural language.

An MCP server that gives AI agents human-like control over Windows via visual perception and simulated mouse and keyboard input, enabling automation of any application without APIs.

Enables MCP clients to observe and control Windows desktop applications by reading UIA element trees and capturing screenshots, then acting through UIA patterns or synthesized mouse and keyboard input. It runs standalone on an interactive Windows session, without requiring the original host application.

Provides safe Linux desktop automation for Codex through focused MCP tools for inspecting, clicking, typing, dragging, and verifying native applications on Wayland and X11.

Provides desktop automation tools for Windows, enabling mouse and keyboard control, screenshots, clipboard management, and application interaction via the MCP protocol.

Provides AI assistants with a standardized interface to interact with the Todo for AI task management system. It enables users to retrieve project tasks, create new entries, and submit completion feedback through natural language.