Magic Hour

AI Video Translator

ai_video_translator_create_video

What this API does

Create the same Video Translator you can make in the browser, but programmatically, so you can automate it, run it at scale, or connect it to your own app or workflow.

Good for

Automation and batch processing
Adding video translator into apps, pipelines, or tools

How it works (3 steps)

Upload your inputs (video, image, or audio) with Generate Upload URLs and copy the file_path.
Send a request to create a video translator job with the basic fields.
Check the job status until it's complete, then download the result from downloads.

Key options

Inputs: usually a file, sometimes a YouTube link, depending on project type
Resolution: free users are limited to 576px; higher plans unlock HD and larger sizes
Extra fields: e.g. face_swap_mode, start_seconds/end_seconds, or a text prompt

Cost
Credits are only charged for the frames that actually render. You'll see an estimate when the job is queued, and the final total after it's done.

For detailed examples, see the product page.

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
`name`	No	Give your video a custom name for easy identification.	Video Translator - dateTime
`assets`	Yes	Source video for the translation job.
`resolution`	No	Output video resolution. Defaults to 480p. 720p and 1080p require a paid plan.
`end_seconds`	Yes	End time of your clip (seconds). Must be greater than start_seconds. The clip must be 1-30 seconds long.
`start_seconds`	No	Start time of your clip (seconds). Must be ≥ 0.
`target_language`	Yes	Language to translate the video's speech into.

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.

Schema Changelog

Changes observed during successful MCP inspections.

AddedSep 26, 2026

TDQS

A4.5/5.0

Behavior5/5

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

Annotations only cover readOnly/openWorld/destructive. The description goes well beyond: it discloses the async job model, that only rendered frames are charged, that an estimate appears at queue time, the exact return fields (id, credits_charged), and that the wait helper returns exact_download_urls separately from expiration metadata. It also warns that hotlinked public URLs can fail and to prefer the presigned upload flow.

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?

Content is front-loaded with the core purpose and a clean 3-step workflow, and the MCP guidance block carries the highest-value operational detail. Some framing ('Good for', product-page link, marketing tone) is padding an agent does not need, keeping it from a 5.

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

Completeness5/5

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

For an async, nested-schema, open-world generation tool with an output schema, this covers everything an agent needs: the upload prerequisite, the async lifecycle, the polling/wait alternative, cost behavior, and URL reliability caveats. Nothing material is missing.

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 already 100%, so the baseline is 3, but the description adds genuine meaning: the upload-URL -> file_path workflow for *_file_path values, the fallback behavior of direct public URLs, and resolution plan gating (free tier limited vs paid HD). The 576px vs 480p wording differs slightly from the schema but is not contradictory.

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

Purpose4/5

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

The description states a specific verb and resource ('Create ... Video Translator ... programmatically'), and the mention of translation targets and the target_language parameter make the purpose unambiguous. It does not explicitly distinguish itself from close siblings like auto_subtitle_generator or lip_sync_create_video, so it stops short of a 5.

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

Usage Guidelines5/5

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

It names concrete use cases ('automation and batch processing', 'pipelines or tools') and, in the MCP guidance, explicitly routes the agent: the call returns an id immediately and the agent should call wait_for_video_project or poll GET /v1/video-projects/{id} for a finished result. That is an explicit when/when-not with a named alternative.

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

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