Magic Hour

animation_create_video

Create a Animation video. The estimated frame cost is calculated based on the fps and end_seconds input.

MCP guidance:

This starts an async video generation job and returns id plus credits_charged immediately. If the user wants the finished result, call the wait_for_video_project helper with the returned id, or poll the matching GET /v1/video-projects/{id} endpoint until status is complete, error, or canceled. Completed projects include downloads with direct URLs. The custom wait helper also returns exact_download_urls separately from expiration metadata.
For *_file_path values, prefer an existing Magic Hour file path or a file_path returned by the upload-URL endpoint after the file bytes are uploaded. Direct public media URLs may work when they are stable, fetchable, and return raw file bytes, but hotlinked URLs can fail; when in doubt, use the presigned upload flow first and pass the returned file_path.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`fps`	Yes	The desire output video frame rate
`name`	No	Give your video a custom name for easy identification.	Animation - dateTime
`style`	Yes	Defines the style of the output video
`width`	Yes	The width of the final output video. The maximum width depends on your subscription. Please refer to our [pricing page](https://magichour.ai/pricing) for more details
`assets`	Yes	Provide the assets for animation.
`height`	Yes	The height of the final output video. The maximum height depends on your subscription. Please refer to our [pricing page](https://magichour.ai/pricing) for more details
`end_seconds`	Yes	This value determines the duration of the output video.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`id`	Yes	Unique ID of the video. Use it with the [Get video Project API](https://docs.magichour.ai/api-reference/video-projects/get-video-details) to fetch status and downloads.
`credits_charged`	Yes	The amount of credits deducted from your account to generate the video. If the status is not 'complete', this value is an estimate and may be adjusted upon completion based on the actual FPS of the output video. If video generation fails, credits will be refunded, and this field will be updated to include the refund.

TDQS

A3.8/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the transparency burden and does it well: it discloses async behavior, immediate id and credits_charged return, final statuses, download URLs, exact_download_urls, and file URL risks. It does not mention rate limits or authentication, but covers the most consequential behavioral traits for correct use.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-organized, front-loading the purpose and then providing bulleted MCP guidance. Each sentence contributes useful operational detail, though the exact_download_urls note is slightly niche and the opening line could have added more differentiating detail.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complex nested schema, the async behavior, and the absence of annotations, the description is quite complete for invocation and follow-up: it covers job lifecycle, return fields, and file input pitfalls. The main gap is not explaining the 'Animation' domain or choosing this tool over sibling generation tools.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description adds genuine value by explaining that estimated frame cost depends on fps and end_seconds, and by giving practical guidance on *_file_path values that is not fully captured in the schema. This pushes it above baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

'Create a Animation video' states a verb and resource, but it essentially restates the tool name and does not explain what an 'Animation video' is or how it differs from sibling video creators like text_to_video_create_video, image_to_video_create_video, or video_to_video_create_video. The cost sentence adds related information but not purpose differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The MCP guidance gives clear operational context: this is an async job, the user should call wait_for_video_project with the returned id, and file_path values should come from Magic Hour or the upload flow. It does not explicitly name alternative tools or when-not-to-use conditions, so it stops short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Magic Hour tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.9/5.0

Disambiguation3/5

Most tools are differentiated by product-specific prefixes (e.g., lip_sync, text_to_video, image_upscaler), but the set contains many overlapping create_image/create_video tools, and generic editors like ai_image_editor_create_image and ai_video_editor_create_video blur boundaries with their more specific counterparts. Face/body swapping tools also occupy a similar conceptual space, requiring careful description reading to avoid misselection.

Naming Consistency4/5

Names generally follow a descriptive snake_case pattern of feature plus action (e.g., text_to_video_create_video, image_projects_delete, wait_for_image_project). Minor inconsistencies like ai_face_editor_edit_image versus the dominant create_image suffix, and the mixed ai_ prefix usage across tools, prevent a perfect score.

Tool Count2/5

44 tools is a large surface for an MCP server, even for a broad media-generation API. The count exceeds the 25+ threshold and creates a heavy selection burden, especially with over a dozen create tools for images and videos.

Completeness4/5

The surface covers the full create-to-download workflow for image, video, and audio: creation, status polling, wait helpers, fetch helpers, delete, and asset upload support. Minor gaps include no list/cancel endpoints and no general project search, but agents can complete core tasks without dead ends.

Resources

Need Help?