Magic Hour

ai_video_editor_create_video

What this API does

Create the same Video Editor 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 editor 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 editor 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.	Video Editor - dateTime
`model`	No	Editing model. Defaults to `ltx-2.3` for free tier and `gemini-omni` for paid. Use `ltx-2.3` for LTX video edit.
`style`	Yes
`assets`	Yes	Provide the assets for video editing.
`resolution`	No	Output resolution. Defaults to `480p` for free tier and `720p` for paid. Google Omni supports 720p only; LTX-2.3 supports 480p, 720p, and 1080p.
`end_seconds`	Yes	End time of your clip in seconds. Must be greater than `start_seconds`. Minimum duration depends on model: `gemini-omni`: 3s, `ltx-2.3`: 0.5s. Maximum duration depends on model: `gemini-omni`: 10s, `ltx-2.3`: 45s.
`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

A4.4/5.0

Behavior5/5

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

With no annotations provided, the description carries the full burden — and it delivers. It discloses async behavior, immediate return of id and credits_charged, the three terminal statuses, download URLs, and the cost model. File-path guidance about hotlinked URLs failing also adds useful behavioral nuance.

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 organized with clear headers, bullets, and a distinct MCP guidance section. It front-loads what the tool does and how the job flow works, then layers in cost, file-handling nuances, and polling instructions. Every section earns its place and no substantial redundancy appears.

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?

Given the tool's complexity — async job lifecycle, nested assets, seven parameters, and an output schema — the description covers the necessary workflow, polling behavior, upload strategy, resolution defaults, cost, and download result. The output schema covers return-value details, so the description does not need to restate them.

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 86%, so the schema already documents most parameters; the description adds meaningful extra context by recommending upload-generated file_path values over direct URLs and by explaining resolution tier defaults. This supplements rather than repeats the input schema.

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 clearly states this creates the browser Video Editor experience programmatically, and the MCP guidance confirms it starts an async video generation job. It names the resource and action, and the example prompt illustrates editing. However, it does not explicitly differentiate this from sibling video creation tools like video_to_video or text_to_video, so it stops short of full clarity.

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?

Usage context is explicit: automation, batch processing, app integration, and a three-step workflow. The MCP guidance provides concrete routing: use wait_for_video_project for a finished result or poll the GET endpoint until complete. It lacks an explicit 'when not to use this' versus other video tools, but the context is clear and actionable.

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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