Magic Hour MCP Server
OfficialRelated Servers
Alternatives to Magic Hour MCP Server
No user-submitted related servers found.
Related Servers
- FlicenseAqualityDmaintenanceEnables AI-powered media manipulation including face swapping for videos and photos, lip syncing, and face detection via the Magic Hour AI platform. It provides tools for uploading files, managing video projects, and processing content from both local files and YouTube URLs.12-
- AlicenseNot gradedqualityCmaintenanceEnables MCP-compatible clients to generate and upscale AI images, videos, music, and sound effects, manage generation jobs, access model catalogs, and track credits.3MIT
- AlicenseAqualityCmaintenanceEnables text-to-image and text-to-video generation via the Agnes AI API, with support for multiple models, async video generation, and automatic file downloads.61MIT
- AlicenseBqualityCmaintenanceEnables any MCP-compatible AI agent to generate videos and images from text or reference frames, chain scenes into seamless multi-scene films with sound and speech, and download finished media with manifests and previews. It also exposes account status, plan limits, history and library management through confirm-gated, safe-by-default operations.2034MIT
- AlicenseNot gradedqualityAmaintenanceEnables MCP-compatible clients to generate images and videos, synthesize speech, clone and design voices, and dynamically configure API interactions.549 npmMIT

maginary-mcpofficial
AlicenseAqualityBmaintenanceEnables MCP-compatible clients to discover Maginary's image/video generation flags, submit generation requests, and poll for results, including support for API-key and OAuth authentication.1353 PyPI1MIT
TDQS
Scored across 45 tools
Each tool targets a distinct media generation task (e.g., text_to_video vs image_to_video) or lifecycle operation (create vs retrieve vs delete). Some related tools like face_swap_photo_create_image, head_swap_create_image, and body_swap_create_image are similar but descriptions clarify their specific inputs. The main risk is the large number of video/image creation tools, but the descriptions are distinct enough.
Naming follows multiple inconsistent patterns: some use 'ai_' prefixes (ai_image_generator_create_image), some use domain prefixes (video_projects_retrieve_details), some use feature-based prefixes (text_to_video_create_video), and a few are standalone (ping, saved_items_list). The verb_noun structure varies (e.g., 'create_video' vs 'retrieve_details' vs 'delete'). This lack of a uniform convention makes it harder for an agent to predict tool names.
With 45 tools, this is on the heavy side. The server covers a wide range of media generation features (video, image, audio) plus lifecycle utilities (upload, create, poll, wait, delete, fetch), which justifies many tools, but the count is high and near the threshold where agents may struggle to choose. It's borderline appropriate but not excessively bloated.
The server provides a complete lifecycle for each media type: presigned upload URLs, creation tools, retrieval of project details, wait helpers, download fetching, and deletion. There are also utilities for saved items, account info, and face detection. Minor gaps include no explicit list-projects tool (only saved items) and no cancel/update operations, but these are not essential for the core generation workflow.