Enables AI agents to control macOS by capturing screenshots and performing mouse clicks, movement, dragging, text typing, hotkeys, and scrolling, with support for Gemini's normalized coordinate protocol and Retina display scaling.
Enables AI agents to access and manage project guidelines, documentation, and context through a structured content system with template support and workflow management.
Open-source, local macOS MCP server for AI agents. Automate background Safari/Chrome tabs, native apps, files and shell with scoped permissions, optional approvals and delegated coding agents.
A minimal macOS GUI-automation MCP server providing tools to see app state, perform actions, manage apps, send notifications, and run AppleScripts with security boundaries.
Enables AI clients to observe WeChat conversations on macOS via local screen capture and Apple Vision OCR, and to preview draft replies without sending messages.
Live Excel MCP server for macOS that interacts with running Excel instances in real time via AppleScript, enabling instant read/write, formula support, and VBA macro execution.
An MCP server that transforms books into agent-callable skill packages, enabling AI agents to use books as guides for evidence, procedures, and tutoring.
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.
A template MCP server that provides a basic structure and configuration for building Model Context Protocol servers. Includes Docker support, environment configuration, and examples for both STDIO and SSE transport modes.
Enables remote MCP clients to securely access a trusted macOS machine's local file system and command execution tools (mcp-combiner) via OAuth-authenticated HTTPS through Cloudflare Tunnel, without opening router ports.
Provides semantic search capabilities over the Plesk Extensions Guide documentation using Retrieval-Augmented Generation (RAG) and vector embeddings. It enables AI assistants to retrieve relevant technical information and answer natural language queries regarding Plesk extension development.