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ai_video_editor_create_video

Create programmatic AI video editing jobs with a text prompt to modify video content, then poll for completion and download the result.

Instructions

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: 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 Editor - dateTime
`model`	No	Editing model. Defaults to `ltx-2.3` for free tier and `gemini-omni-1.1` for paid. `gemini-omni` is deprecated; use `gemini-omni-1.1` instead.
`style`	Yes
`assets`	Yes	Provide the assets for video editing.
`resolution`	No	Output resolution. Defaults to `480p` for free tier and `720p` for paid. `gemini-omni-1.1` and deprecated `gemini-omni` support 720p and 1080p; 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-1.1`: 3s, `ltx-2.3`: 0.5s. Maximum duration depends on model: `gemini-omni-1.1`: 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.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 23, 2026

TDQS

A4.7/5.0

Behavior5/5

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

With no annotations, the description carries the full burden and exceeds it: it discloses that this is an async job that returns an ID immediately, requires polling or a wait helper, includes credit-cost behavior, explains file path preferences and potential URL failures, and notes free-tier resolution limits. This is rich behavioral transparency beyond basic schema data.

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 structured with clear headings, bullet points, and a numbered 3-step workflow. Every section serves a distinct purpose: what it does, use cases, how it works, key options, cost, and MCP-specific guidance. It is comprehensive without being redundant, and key operational details are front-loaded.

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 (7 params, nested objects, async behavior, output schema), the description covers all necessary context: input preparation, job creation, status polling, result retrieval, cost model, and file path caveats. It also provides an output schema and clearly explains what is returned, so an agent has everything needed to invoke it correctly.

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 high (86%), so the baseline is 3. The description adds meaningful parameter-related context, especially for `assets.video_file_path` (prefer existing Magic Hour file paths, presigned upload flow, hotlink risks) and resolution limits (free vs paid tiers). It also mentions optional fields like `start_seconds`/`end_seconds` and text prompts, supplementing the schema.

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 description clearly states the tool creates the Video Editor programmatically, with explicit verbs ('Create the same Video Editor... but programmatically') and identifies the resource (video editor job). It distinguishes itself from sibling video-generation tools by focusing on the editor use case (automation, batch processing, integration into apps/pipelines).

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 description provides clear context for when to use this tool ('Good for' automation and batch processing, adding video editor to apps) and explains the full workflow (upload inputs, create job, poll status). It does not explicitly name alternatives or exclusion conditions, but the 'Good for' and workflow guidance make usage context strong.

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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