Agnes Media MCP
Related Servers
Alternatives to Agnes Media MCP
No user-submitted related servers found.
Related Servers
- FlicenseAqualityAmaintenanceEnables AI assistants to generate images and videos via the Agnes AI API, supporting text-to-image, image-to-image, and video generation.31-
- AlicenseAqualityAmaintenanceEnables 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

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.743 npmMIT- AlicenseAqualityDmaintenanceEnables video generation from text prompts or images using Agnes AI's video models, with async task submission and status polling.210 npm1MIT
- AlicenseAqualityDmaintenanceEnables image generation and editing using Volcengine Ark Doubao-Seedream models, with tools for text-to-image, image-to-image, editing, and result polling.421 npmMIT
- AlicenseNot gradedqualityDmaintenanceEnables image generation and editing using laozhang.ai's OpenAI-compatible API, with tools for text-to-image and image-to-image transformations.MIT
TDQS
Scored across 7 tools
The image tools are ambiguous: agnes_image_generate and agnes_image_generate_v2 both generate images using the same model, with v2 merely adding tier-based sizes while also accepting legacy sizes, making it unclear which to use. Additionally, agnes_image_edit overlaps with generate's image-to-image mode, adding further confusion.
Tools mostly follow an agnes_<resource>_<action> pattern, but agnes_video_status is a noun phrase rather than an action (e.g., check_status), and agnes_image_generate_v2 introduces a version suffix that breaks the pattern. These minor deviations make the set readable but not fully consistent.
Seven tools is within the ideal range for a media generation server. While there is some redundancy (image v1 vs v2, video generate combining submit+wait), the overall count is well-scoped and not excessive.
The server covers core image generation, image editing, and video generation workflows (submit, status, wait, generate). Minor gaps exist such as lack of task listing or cancellation, but the primary generation and polling lifecycle is complete for typical usage.