Magic Hour

face_swap_create_video

What this API does

Create the same Face Swap 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 face swap 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 face swap 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.	Face Swap - dateTime
`style`	No	Style of the face swap video.
`assets`	Yes	Provide the assets for face swap. For video, The `video_source` field determines whether `video_file_path` or `youtube_url` field is used
`end_seconds`	Yes	End time of your clip (seconds). Must be greater than start_seconds.
`start_seconds`	Yes	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

A4.6/5.0

Behavior5/5

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

There are no annotations provided, so the description carries the full burden of behavioral disclosure—and it does so thoroughly. It states the async nature upfront, what is returned immediately (`id` plus `credits_charged`), the lifecycle statuses to poll for, that completed projects include `downloads`, and that credits are charged per rendered frame with estimates and final totals. It also warns that direct public media URLs can fail and recommends the presigned upload flow, which is exactly the kind of non-obvious behavior an agent needs to know.

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 longer than average, but the length is justified by the tool's complexity: it covers async behavior, file upload prerequisites, polling, output retrieval, costs, and file-path edge cases. It is well-structured with clear headers and bullet lists, and the most important behavioral facts (async job, immediate return values) are front-loaded in the MCP guidance. Minor redundancy such as repeating 'see the request schema' is the only blemish.

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, the absence of annotations, and the existence of an output schema, this description is comprehensive. It explains the full lifecycle from upload to download, names the `wait_for_video_project` helper explicitly, handles the ambiguous `*_file_path` value inputs, and warns about the most common failure mode (hotlinked URLs). An agent has enough information to invoke the tool correctly and to guide the user through the result.

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?

Input schema coverage is 100%, so the schema already documents every parameter in detail; the baseline is therefore 3. The description adds meaningful value by explaining the recommended file-path strategy, the distinction between acceptable `file_path` values and fallible direct URLs, and the resolution/credits implications. This goes beyond simply restating the schema and helps the agent make correct parameter choices.

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 opens with a specific verb and resource: 'Create the same Face Swap you can make in the browser, but programmatically.' It names the exact operation (face-swap video creation), distinguishes it from a mere general swap tool, and the 'Good for' section clarifies its intended niche of automation and app integration, separating it from one-off face swap photo tools.

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 gives explicit workflow guidance: upload inputs first, create the job, then poll or use `wait_for_video_project` until `complete`, `error`, or `canceled`. It also provides clear file-path guidance, preferring presigned upload `file_path` values over unreliable hotlinked URLs. It stops short of explicitly contrasting this tool with sibling tools like `face_swap_photo_create_image` or `body_swap_create_image`, so it does not fully articulate when not to use it.

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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