JADX-AI-MCP is a fully automated server and plugin for integrating Model Context Protocol (MCP) with JADX for the purpose of analyzing Android APKs using large language models such as Claude. It streamlines vulnerability discovery, reverse engineering, and static analysis by leveraging LLMs in conjunction with established tools. The project facilitates real-time code review and efficient collaboration between AI and human analysts.

• ⭐ 637
• MCP
• zinja-coder/jadx-ai-mcp

omniparser-autogui-mcp is an MCP server that leverages OmniParser to analyze on-screen content and perform automated GUI operations. It integrates with clients such as Claude Desktop and can be configured via a detailed environment setup. The tool supports Windows and can delegate OmniParser processing to external devices, offering flexibility for complex contexts. Multiple environment variables allow customization of backend processing, target window selection, and communication methods, including SSE.

• ⭐ 58
• MCP
• NON906/omniparser-autogui-mcp

Jupyter MCP Server is an implementation of the Model Context Protocol (MCP) designed to enable AI-driven, real-time management and interaction with Jupyter Notebooks. It offers context-aware capabilities, smart execution features, and multimodal output handling, seamlessly integrating with JupyterLab and supporting multiple notebooks simultaneously. The server is compatible with any MCP client and can work with local or hosted Jupyter deployments.

• ⭐ 765
• MCP
• datalayer/jupyter-mcp-server

ScreenPilot provides an MCP server interface to enable large language models to interact with and control graphical user interfaces on a device. It offers a comprehensive toolkit for screen capture, mouse control, keyboard input, scrolling, element detection, and action sequencing. The toolkit is suitable for automation, education, and experimentation, allowing AI agents to perform complex operations on a user’s device.

• ⭐ 50
• MCP
• Mtehabsim/ScreenPilot

Utopia MCP Server simulates a wide range of smart home and lifestyle devices, enabling AI agents to interact with and control them through the Model Context Protocol. It provides endpoints for managing devices like lighting, climate control, audio, security, and household robots, allowing for complex, user-centered tasks to be automated without explicit workflow programming. The server is designed to facilitate the testing and experimentation of AI-driven orchestration among multiple simulated endpoints in household environments.

• ⭐ 9
• MCP
• altinoren/Utopia

MCP Status Observer enables real-time monitoring and querying of service status for major digital platforms and AI providers through the Model Context Protocol (MCP). It supports integration with tools like Claude Desktop for streamlined status checks, incident tracking, and impact analysis across a range of platforms including GitHub, Slack, OpenAI, and Cloudflare. The tool provides detailed component-level insights, incident histories, and resolution statuses, empowering rapid awareness of operational issues and outages.

• ⭐ 5
• MCP
• imprvhub/mcp-status-observer