Veo 3.1 MCP Server
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- AlicenseNot gradedqualityDmaintenanceEnables video generation from text prompts or images using Google's Veo 3 API. Supports multiple models, audio generation, and various aspect ratios for creating high-quality videos.29 PyPI3MIT
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- AlicenseAqualityAmaintenanceGenerate video from a prompt, from an image, or from up to thirty reference images, routed across the field of AI video models (Sora, Veo, Kling, Seedance, Hailuo, Wan and more). Quotes the credit cost before spending it and refunds a technical failure automatically.5MIT
- AlicenseAqualityDmaintenanceEnables image generation, editing, and analysis using Google's Gemini 2.5 Flash and Gemini 3 Pro models, with support for batch processing, style templates, and high-resolution output.8546 npm1MIT
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TDQS
Scored across 6 tools
Each tool has a clearly distinct purpose with no overlap: cost estimation, video extension, job status checking, batch generation, single generation, and image upload. The descriptions reinforce distinct workflows (e.g., 'extend_video' specifically requires Veo-generated input, while 'upload_image' handles image preparation).
All tools follow a consistent verb_noun pattern with snake_case (e.g., 'estimate_veo_cost', 'extend_video', 'get_video_job'). The naming is predictable and readable, using clear verbs like 'estimate', 'extend', 'get', 'start', and 'upload' paired with relevant nouns.
Six tools are well-scoped for a video generation server, covering core operations from initiation to status checking and cost management. This count avoids bloat while ensuring each tool earns its place, such as separate tools for single and batch generation to handle different use cases efficiently.
The toolset provides complete coverage for the video generation domain: starting jobs (single and batch), checking status, extending videos, uploading images for reference, and estimating costs. There are no obvious gaps; agents can handle the full lifecycle from planning to retrieval without dead ends.