Vidu MCP
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Alternatives to Vidu MCP
No user-submitted related servers found.
Related Servers
- AlicenseBqualityBmaintenanceEnables AI agents to create Vidu image-to-video and reference-to-video generation tasks, poll asynchronous results, and check pricing across two model variants. Works from Claude Code, Codex, Cursor, VS Code, and other MCP-compatible hosts.569 npmApache 2.0

PixVerse MCPofficial
AlicenseNot gradedqualityDmaintenanceEnables video generation from text, images, and more through MCP-compatible apps like Claude and Cursor.55MIT- AlicenseAqualityBmaintenanceEnables MCP clients to generate videos from text prompts or source images through Google's Gemini API, poll render jobs, and save MP4 files to disk. It also lists available video models.3471 npm1MIT
- AlicenseAqualityAmaintenanceEnables video and image generation from any MCP client, with support for models like Veo 3.1, Grok Imagine, and Nano Banana, including text-to-video, image-to-video, image generation and editing.465 npmMIT

@vidofy/mcpofficial
AlicenseAqualityBmaintenanceEnables generating images, video, audio, and speech from MCP clients using your own Vidofy account, with access to hundreds of models for text-to-video, image-to-video, image editing, lipsync, text-to-speech, and voice cloning.9376 npm2MIT- AlicenseNot gradedqualityBmaintenanceEnables AI agents to browse video production packages, obtain free quotes, start and track productions with budget safeguards, and download finished videos, thumbnails and subtitles from vidia.kr. It also supports account balance checks and asset library lookups through MCP clients such as Claude, Cursor and Codex.65 npmMIT
TDQS
Scored across 5 tools
The tools are clearly differentiated by their input types and purposes: single image, multiple reference images, start-end images, template-based, and text-only generation. However, the first three tools have identical descriptions ('Generate a video from a pic and prompt'), which could cause initial confusion until the specific parameters are examined.
All tools follow a consistent 'generate_[input_type]_to_video' naming pattern (e.g., generate_img_to_video, generate_text_to_video). The pattern is logical and predictable, making it easy to understand what each tool does based on its name alone.
With 5 tools, this is well-scoped for a video generation server. Each tool covers a distinct input modality (image, reference images, start-end images, templates, text), providing comprehensive coverage without being overwhelming or sparse.
The tool set provides complete coverage of video generation modalities for this domain: from single images, multiple reference images, start-end sequences, templates, and pure text prompts. There are no obvious gaps in the core functionality, and all tools return consistent outputs (task_id and video_url).