AI Video Generator MCP Server
Related Servers
Alternatives to AI Video Generator MCP Server
No user-submitted related servers found.
Related Servers
- FlicenseNot gradedqualityDmaintenanceA server that provides Luma AI's video generation API as the Model Context Protocol (MCP)3-
- AlicenseNot gradedqualityFmaintenanceA Model Context Protocol server that enables AI assistants to perform comprehensive video and audio editing operations including trimming, effects, overlays, audio processing, and YouTube downloads.25MIT
- AlicenseAqualityCmaintenanceA lightweight Model Context Protocol server for planning image-to-video shots, building production-ready motion prompts, and preparing reliable generation workflows.38 npmMIT
- AlicenseBqualityAmaintenanceA comprehensive Model Context Protocol server for the kie.ai generation API, providing access to 47+ image models, 86+ video models, and 20+ audio tools with deep model intelligence.4472 npm3MIT
- AlicenseNot gradedqualityCmaintenanceA Model Context Protocol server for AI image and video generation using Midjourney through the AceDataCloud API.MIT
- AlicenseAqualityAmaintenanceFastMCP server for Google's gemini-omni-flash-preview video model, enabling text-to-video, image-to-video, and video editing with stateful interactions and batch generation.280 PyPIMIT
TDQS
Scored across 2 tools
The two tools have completely distinct purposes with no overlap: one checks status of existing requests, the other creates new videos. An agent would never confuse these functions as they operate on different stages of the video generation workflow.
Both tools follow a consistent verb-object naming pattern with hyphen separation: 'check-video-status' and 'generate-video'. The naming is predictable and follows the same convention throughout the set.
With only 2 tools for a video generation server, the surface feels severely limited. While the basic create+status pair covers minimal functionality, a video generation domain typically requires more operations like listing videos, canceling generations, or managing templates. The count is too low for the apparent scope.
The toolset provides only generation initiation and status checking, creating significant gaps. Missing are operations like listing existing videos, canceling pending generations, retrieving generated content, managing templates/presets, or configuring generation parameters. Agents will hit dead ends when trying to manage the video lifecycle beyond initial creation.