The YouTube MCP Server is a standardized interface implementation that allows AI language models to interact with YouTube content through protocols, providing functions such as video information retrieval, subtitle management, channel and playlist management.

The YouTube MCP Server is a service that implements the Model Context Protocol (MCP). It provides a standardized interface for AI language models to interact with YouTube content, supporting functions such as video information retrieval, subtitle management, channel and playlist operations, etc.

Media Gen MCP is a server that strictly follows the TypeScript and MCP specifications, focusing on generating and editing images and videos using OpenAI and Google's AI models. It provides a series of tools, including image generation/editing, video creation/remixing, file acquisition and processing, and supports intelligent resource linking and inline output. It is suitable for various MCP-compatible clients.

Luma API MCP is a project that provides image and video generation services. Users can access it through an API key, supporting multiple aspect ratios, models, and resolution options, and can control the generation effect through reference images or video keyframes.

The Unsloth MCP Server is a server for efficiently fine-tuning large language models. Through optimized algorithms and 4-bit quantization technology, it achieves a 2-fold increase in training speed and an 80% reduction in video memory usage, supporting multiple mainstream models.

The YouTube MCP Server is a tool for obtaining and extracting subtitles from YouTube videos, enabling AI language models to access and process video content.

The implementation of the 360 AI Cloud Disk MCP Service allows AI models to interact with the cloud disk through a standard protocol, providing intelligent operation capabilities such as file management, upload and download, and video processing.

Tencent Cloud COS MCP Server is a service that allows large models to quickly access Tencent Cloud Storage (COS) and CI capabilities without coding, providing cloud storage and processing functions such as file upload and download, image processing, and video processing.

Tencent Cloud COS MCP Server is a service based on the MCP protocol that allows large models to quickly access Tencent Cloud Storage (COS) and Cloud Infinite (CI) capabilities without coding, providing cloud storage and processing functions such as file upload and download, image processing, and video frame extraction.

A Pixabay API service based on the MCP protocol, providing image and video search functions for AI models.

The OpenCV MCP Server is a Python - based computer vision service that provides OpenCV's image and video processing capabilities through the Model Context Protocol (MCP). It offers a full set of computer vision tools from basic image processing to advanced object detection for AI assistants and language models, including image processing, edge detection, face recognition, video analysis, and real - time object tracking.

The MCP server for YouTube video transcript extraction supports multi - language transcript acquisition, making it convenient for AI models like Claude to directly use video transcript content.

An MCP server based on the fal.ai API for generating images and videos, supporting the extension of different models and API endpoints.

An MCP server implementation based on the YouTube Data API, providing a standardized interface for AI language models to access YouTube content, including functions such as video information, subtitle management, channel analysis, and trend analysis.

The CloudGlue MCP service is a bridge connecting AI assistants and video data. It processes video content in a structured way so that it can be understood and used by large language models.

ComfyUI_StoryDiffusion is a plugin based on ComfyUI that supports the StoryDiffusion and MS - Diffusion models for generating coherent long - sequence images and videos. The project supports multiple functions, including dual - character co - appearance, ControlNet control, Lora acceleration, text - to - image and image - to - image conversion, etc.