"How to run a Windows command" matching MCP servers:

Enables ChatGPT to control multiple registered Windows devices through a self-hosted gateway, routing Desktop Commander tool calls to specific workers by device ID.

Enables AI agents to control a Playwright-driven browser through natural-language instructions, including page interaction and live browser task execution.

A lightweight server that enables AI agents to interact with the Windows operating system, allowing for file navigation, application control, UI interaction, and QA testing through various tools.

Listens for Slack @mentions and publishes them as real-time events to the KĀDI event bus, enabling event-driven architectures with low latency.

Enables ESP32 XiaoZhi voice devices to command a Windows PC via MCP, including app control, file operations, screen vision, browser interaction, shell execution, and orchestration of local AI coding agents like Codex CLI and Claude Code.

Comprehensive quest orchestration system with KĀDI broker integration, multi-channel approval workflow, and Git-backed data storage.

基于MCP架构的微信公众号文章爬虫系统，支持AI智能体通过Selenium自动抓取微信公众号文章内容和图片，实现文章内容的智能分析。

Enables Claude Code and Claude Desktop to command the jev-ultrafast browser agent in your real Chrome, autonomously pursuing plain-language goals on a given start URL while enforcing a domain-bound safeguard.

Enables real-time user intervention for MCP agents through a web UI, allowing users to review context and provide feedback when AI agents drift from intent, keeping them on track.

Discover and connect your agents with other agents via A2A protocol. Share resources for free or fee.

A self-hostable, open-source, semantically-searchable Agent Skills registry delivered over MCP, with a three-tier progressive disclosure architecture.

Human-in-the-loop approval inbox for AI agents: an agent proposes an action (send email, post comment, run a command), a human approves, rejects, or edits it from a web, mobile, or Slack/Discord/Telegram inbox, and the agent only runs on approval. Full audit trail, self-hostable (MIT).

Enables agents to accomplish multi-step work by writing a single sandboxed JavaScript program that drives shell, filesystem, HTTP, and every tool of any configured MCP server, returning only a small summary instead of raw JSON. This collapses dozens of advertised tool schemas into one tool, cutting per-request context by roughly 26× while keeping disabled upstream servers reachable.

Agent-native hosting: your agent deploys any folder to a live URL in one command — no account, no dashboard, no CAPTCHA — and hands the human a link plus a plain-language summary. Full deploy lifecycle as MCP tools (deploy, verify, logs, inspect, rollback), production approval human-only; static sites and full Node.js apps, open source (Apache-2.0).

MCP server that connects AI assistants to Leapd, enabling them to launch and manage an AI-run business by creating tasks, deploying sites, and accessing business documents.

Enables terminal AI coding assistants to interact with web AI chat services through browser automation, supporting multiple platforms like Doubao and ChatGPT.

async/await for LLM agents: spawn and await sub-agents scheduled on your own GPUs or any OpenAI-compatible endpoint. Every tool call is a scheduled await: a resumed run goes ahead of batch work and back to the replica holding its prefix cache. Durable runs, run trees with budgets, caller fairness, a status page, OpenTelemetry.

Enables coding agents to perform computer use across browsers and native Windows apps by observing the screen, reducing it to a finite list of executable candidates, and letting a small decision model choose one action before input is sent and the result verified. It also returns per-goal outcomes, uncertainty handoffs, and run journals so the calling agent can recover from failures.

Enables collaboration across Claude, GPT, and Gemini through a unified MCP interface, with configurable execution policies for synthesizing or directly delivering worker outputs.

Enables AI assistants and other MCP clients to launch and steer Cursor Cloud Agents on repositories or as repo-less research tasks, including model selection, mid-run follow-ups, cancellation, result retrieval, and run listing. Also exposes authentication checks and per-agent or per-run token usage and cost reporting.