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Screenmonitormcp

ScreenMonitorMCP - Revolutionary AI Vision Server Give AI real-time sight and screen interaction capabilities ScreenMonitorMCP is a revolutionary MCP (Model Context Protocol) server that provides Claude and other AI assistants with real-time screen monitoring, visual analysis, and intelligent interaction capabilities. This project enables AI to see, understand, and interact with your screen in ways never before possible. Why ScreenMonitorMCP? Transform your AI assistant from text-only to a visual powerhouse that can: Monitor your screen in real-time and detect important changes Click UI elements using natural language commands Extract text from any part of your screen Analyze screenshots and videos with AI Provide intelligent insights about screen activity Core Features Smart Monitoring System start_smart_monitoring() - Enable intelligent monitoring with configurable triggers get_monitoring_insights() - AI-powered analysis of screen activity get_recent_events() - History of detected screen changes stop_smart_monitoring() - Stop monitoring with preserved insights Natural Language UI Interaction smart_click() - Click elements using descriptions like "Save button" extract_text_from_screen() - OCR text extraction from screen regions get_active_application() - Get current application context Visual Analysis Tools capture_and_analyze() - Screenshot capture with AI analysis record_and_analyze() - Video recording with AI analysis query_vision_about_current_view() - Ask AI questions about current screen System Performance get_system_metrics() - Comprehensive system health dashboard get_cache_stats() - Cache performance statistics optimize_image() - Advanced image optimization simulate_input() - Keyboard and mouse simulation

Video Jungle Video Editor Mcp Server

See a demo here: https://www.youtube.com/watch?v=KG6TMLD8GmA Upload, edit, search, and generate videos from everyone's favorite LLM and Video Jungle. You'll need to sign up for an account at Video Jungle in order to use this tool, and add your API key. The server implements an interface to upload, generate, and edit videos with: Custom vj:// URI scheme for accessing individual videos and projects Each project resource has a name, description Search results are returned with metadata about what is in the video, and when, allowing for edit generation directly Tools The server implements a few tools: add-video Add a Video File for analysis from a URL. Returns an vj:// URI to reference the Video file create-videojungle-project Creates a Video Jungle project to contain generative scripts, analyzed videos, and images for video edit generation edit-locally Creates an OpenTimelineIO project and downloads it to your machine to open in a Davinci Resolve Studio instance (Resolve Studio must already be running before calling this tool.) generate-edit-from-videos Generates a rendered video edit from a set of video files generate-edit-from-single-video Generate an edit from a single input video file get-project-assets Get assets within a project for video edit generation. search-videos Returns video matches based upon embeddings and keywords update-video-edit Live update a video edit's information. If Video Jungle is open, edit will be updated in real time.

Video Extraction Server

MCP Video & Audio Text Extraction Server A Model Context Protocol (MCP) server that enables text extraction from various video platforms and audio files, allowing compatible host applications (like Claude Desktop, Cursor) to access video content and perform text transcription. What is it? MCP Video & Audio Text Extraction Server is a Model Context Protocol (MCP) server that can download videos from various platforms, extract audio, and convert it to text. The server utilizes OpenAI's Whisper model for high-quality audio-to-text conversion. How to use it? Clone the repository and install dependencies Ensure FFmpeg is installed Run the server Configure your MCP host application (like Claude Desktop) to use the server Key Features Support video downloads from multiple platforms including YouTube, Bilibili, TikTok, etc. Extract audio content from videos High-quality speech recognition using Whisper model Multi-language text recognition support Asynchronous processing for large files Standardized MCP tools interface Use Cases Provide text transcription capabilities for applications that need to process video content Batch process video content and extract text information Create custom applications requiring audio/video text extraction functionality Enable AI assistants to understand video content FAQ What are the system requirements to run the server? > Requires Python 3.9+, FFmpeg, minimum 8GB RAM, GPU acceleration recommended What should I know about first run? > The system will automatically download the Whisper model file (approximately 1GB), which may take several minutes to tens of minutes What audio formats are supported? > Supports common audio formats including mp3, wav, m4a, etc. This description maintains the core information from the original README while adopting a similar structure and style to the reference page. Would you like me to adjust or add anything to this description?