"Methods or tools to control a computer system" matching MCP servers:

Enables structured extraction of methods and reproducibility heuristics from academic papers, allowing AI agents to obtain metadata, full text, structured methods, code repository discovery, and a no-clone reproducibility verdict from a paper URL.

Enables AI agents to observe and operate a real Windows or Linux desktop like a human — snapshotting screen geometry, active window, OCR text and normalized UI elements, then clicking, typing, scrolling and managing windows with built-in verification and recovery. Backends are chosen automatically per action (UIA/AT-SPI → OCR → vision → PyAutoGUI), so browsers, IDEs, office suites and canvas apps are all controllable without any browser-automation MCP.

This server enables AI agents to operate Windows and Linux computers by observing the screen, understanding the UI through accessibility, OCR, and vision, and performing mouse, keyboard, window, clipboard, and system actions with verification and recovery.

Recommends study designs for a research question, states the identifying assumptions to verify, and returns runnable R, Python, or Julia code with a formatted citation for every source, drawn from methodological textbooks, an R package, and research articles on causal inference, econometrics, statistics, and machine learning.

Enables system automation and control including command execution, process management, network tools, environment variables, disk usage, and service status.

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.

Exposes Anthropic's computer-use action surface (screenshot, click, move, keyboard, clipboard, batch) against a persistent desktop display via MCP stdio protocol. Enables AI agents to control a virtual desktop environment through natural language instructions.

Enables MCP-speaking clients to control a real desktop via the computer_use tool, including clicking, typing, scrolling, dragging, key combos, app focus, and screen/accessibility capture. It also provides a verdict system that verifies whether input actions had their intended effect, with configurable approval modes.

Enables AI to execute registered system commands across platforms using a stable program + args interface, without requiring shell-specific syntax or tool discovery.

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.

Deterministic, LLM-first control of Word documents (.docx). An agent reads the outline, finds text by phrase, and applies edits as tracked changes and comments in one atomic batch, with a safety layer that refuses edits based on a stale read and rolls back failed batches. It works on .docx files directly, and can also drive an open document in Microsoft Word on Windows or in LibreOffice Writer.

An open-source MCP server that lets any coding agent operate your computer like a person does—reading the UI through accessibility trees, clicking and typing in the background, showing an agent pointer, zooming into regions, and driving your signed-in Chrome on macOS, Windows, and Linux.

A secure, read-only MCP server for AI-powered system monitoring. It provides real-time OS metrics, config discovery, and safe log tailing to enable autonomous infrastructure audits without shell access risks.

Enables Claude to monitor CPU, memory, disk, processes, network connections, Docker containers, and system logs for system diagnostics and troubleshooting.

Provides system hardware information (CPU, memory, storage, etc.) and system status (OS, network, resource usage) via MCP tools.

A Model Context Protocol server that exposes an airline design system as a queryable knowledge base, enabling AI to discover components, find components for use cases, and scaffold prototypes.

Turns Canvas LMS into a scriptable pipeline and exposes it to AI assistants via MCP for querying courses, grades, assignments, and syncing files using natural language.

Enables an agent to see and operate a GNOME Wayland desktop like a human, including screenshots, mouse/keyboard control, accessibility tree access, OCR, clipboard, and audio listening.

MCP server enabling Claude Code to capture screenshots of Windows applications through WSL2, allowing AI to diagnose UI errors and verify layouts.

Lets any number of concurrent Claude Code sessions drive a Firefox browser through the Browser Control extension by funneling every session's commands over a single shared port via a background daemon and private unix socket. Sessions no longer have to compete for a limited pool of extension ports, and requests stay isolated through per-request correlation ids.