Magic Hour

audio_to_video_create_video

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: see the request schema for endpoint-specific assets
Resolution: free users default to 480p; higher plans unlock HD and larger sizes
Extra fields: see the request schema for endpoint-specific options

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.

TDQS

A4.7/5.0

Behavior5/5

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

No annotations are provided, so the description carries the full behavioral burden — and it delivers. It discloses the async job semantics ('starts an async video generation job and returns id plus credits_charged immediately'), the terminal job states ('complete, error, or canceled'), the cost model (credits charged only for rendered frames, with an estimate at queue time), and tier-based resolution limits. It even warns that hotlinked URLs can fail and directs users to the presigned upload flow as the safe alternative.

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

Conciseness5/5

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

The description is long but densely structured with clear headers — What this API does / Good for / How it works (3 steps) / Key options / Cost / MCP guidance — making it scannable for an agent. The core purpose is front-loaded in the first sentence, the step-by-step workflow is compact, and every section adds non-redundant operational value rather than restating the schema.

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?

This is a complex tool — 6 parameters, 2 required, nested assets/style objects, plus an async job lifecycle — and the description covers all operational aspects: the upload-URL preparation step, the create-job step, polling for completion, the downloads field, cost estimation, and the wait_for_video_project helper for synchronous use. With an output schema present to document the return shape, nothing an agent needs to invoke this correctly 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 description coverage is 100%, so the schema already documents every parameter and the baseline is 3. The description adds real value beyond the schema by explaining resolution tier behavior (free users default to 480p, higher plans unlock HD) and by giving practical guidance on *_file_path values — preferring Magic Hour file paths or upload-URL returns over direct media URLs, including the failure-mode caveat. This exceeds the baseline meaningfully.

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

Purpose5/5

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

The opening sentence states the exact operation — 'Create the same Audio To Video you can make in the browser, but programmatically' — a specific verb and resource that an agent can immediately distinguish from sibling tools like text_to_video_create_video, image_to_video_create_video, or video_to_video_create_video. The 'Good for' section reinforces the scope with concrete use cases and the tool name itself matches the described behavior exactly.

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 explicitly frames when this tool is appropriate (automation, batch processing, adding audio to video into apps and pipelines), and the MCP guidance gives precise routing: use the wait_for_video_project helper or poll GET /v1/video-projects/{id} when the finished result is wanted. It doesn't explicitly state 'when not to use this' in favor of a sibling creation tool, but the audio-input framing makes the selection criterion clear without needing an explicit exclusion.

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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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

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