aihub-mcp
Related Servers
Alternatives to aihub-mcp
No user-submitted related servers found.
Related Servers
- FlicenseAqualityCmaintenanceEnables AI assistants to generate images and videos via the Agnes AI API, supporting text-to-image, image-to-image, and video generation.31-
- AlicenseBqualityDmaintenanceEnables AI assistants to generate images and videos through natural language using ImaginePro's API. Supports text-to-image generation, video creation, image upscaling, variants, inpainting, and multi-modal generation with real-time progress tracking.826 npmMIT
- AlicenseBqualityDmaintenanceEnables AI assistants to generate images, text, and audio content through the Pollinations APIs. Provides direct access to multimodal generation capabilities including image creation from text prompts, text-to-speech, and text generation.1266 npmMIT
- AlicenseAqualityCmaintenanceEnables AI agents to generate images and videos via the Corent API with automatic model routing, provider fallback, and tools for planning, creation, and balance management.7278 npmMIT

vicsee-mcp-serverofficial
AlicenseAqualityBmaintenanceEnables AI agents to generate, edit, and upscale videos and images using VicSee's API, with support for multiple models and asynchronous task polling.7213 npmMIT- FlicenseBqualityDmaintenanceEnables AI-powered image and video generation through the TensorsLab API using models like SeeDream and SeeDance. It supports tasks such as creating high-resolution media from text prompts, image-to-video conversion, and monitoring generation status.9-
TDQS
Scored across 14 tools
Every tool has a clearly distinct purpose. Generation tools are separated by media type (audio, document, image, video), analysis is separate, task management tools differentiate polling (get_task) from blocking (wait_for_task), and other utilities (embeddings, credits, file upload, model listing) are unique.
All tools follow a consistent verb_noun snake_case pattern: analyze_media, ask_model, create_embeddings, etc. No mixed conventions or abbreviations.
14 tools is an appropriate count for a multimodal AI hub. It covers key functionalities without being bloated or sparse.
The tool set is comprehensive: media analysis, model querying, embeddings, generation (audio, document, image, video), file upload/download, task management, credits, and model discovery. No obvious gaps for the intended domain.