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audio_to_video_create_video

Generate a video from an audio track and optional reference image programmatically, enabling automated audio-to-video creation in apps and workflows.

Instructions

What this API does

Create the same Audio To Video 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 audio to video 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 audio to video 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.	Audio To Video - dateTime
`style`	No	Attributes used to dictate the style of the output
`assets`	Yes	Provide the audio file and an optional reference image.
`resolution`	No	Output video resolution. Defaults to `720p` on paid tiers and `480p` on free tiers.
`end_seconds`	Yes	End time of your clip (seconds). Must be greater than start_seconds.
`start_seconds`	No	Start time of your clip (seconds). Must be ≥ 0.

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.

First observedv0.1.0Sep 23, 2026

TDQS

A4.3/5.0

Behavior5/5

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

With no annotations present, the description carries the full behavioral burden and does so thoroughly: it discloses that this starts an async job returning id and credits_charged immediately, that callers must poll or wait via wait_for_video_project, that statuses include complete/error/canceled, and that completed projects expose downloads URLs. It also discloses billing behavior and file-path caveats for hotlinked URLs.

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 long but well-structured with clear headings: 'What this API does,' 'Good for,' 'How it works,' 'Key options,' 'Cost,' and 'MCP guidance.' The MCP guidance is essential and directly actionable; minor redundancy exists between the opening and the 'Good for' section, but the length is justified.

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 a complex async creation tool with nested objects and no annotations, this description is unusually complete: it covers prerequisite upload steps, job lifecycle, response behavior, cost, resolution constraints, and how to obtain the final result via wait/poll helpers. Nothing essential for correct invocation 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 100%, so the baseline is 3, but the description adds meaningful semantics beyond the schema: the resolution limitation, the behavior of *_file_path values, and the async response fields. It does not walk through every parameter, but the extra context is valuable.

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 opens with a clear verb and resource: 'Create the same Audio To Video you can make in the browser, but programmatically.' It precisely names the tool's function, though it does not explicitly differentiate it from sibling tools like text_to_video_create_video or image_to_video_create_video.

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 'Good for' section provides clear usage context, such as 'Automation and batch processing' and 'Adding audio to video into apps, pipelines, or tools.' The 3-step workflow also signals when the upload step is necessary, but it does not explicitly state when not to use this tool or name alternative tools.

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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