Magic Hour MCP Server

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Python

Hybrid

face_swap_create_video

Swap faces in a video using an uploaded face image. Automate video face swap for apps, pipelines, or batch processing.

Instructions

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: 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.	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.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 23, 2026

TDQS

A4.6/5.0

Behavior5/5

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

With no annotations provided, the description carries full disclosure burden and does so thoroughly. It reveals asynchronous behavior (returns id plus credits_charged immediately), job lifecycle statuses, where to find downloads, reliability caveats about hotlinked URLs, the free-tier resolution limit, and the credits-only-for-rendered-frames cost model. This is far beyond what the schema alone provides.

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 carefully structured with clear headings: What this API does, Good for, How it works, Key options, Cost, and MCP guidance. It is front-loaded with purpose, and each section carries actionable information. It is denser than strictly necessary due to repeated upload URL references, but the structure keeps it navigable.

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 tool with a nested assets object, required fields, an output schema, and async behavior, the description covers the full call lifecycle: uploading inputs, creating the face swap job, waiting for completion, and downloading results. It also covers error/canceled statuses, file-path pitfalls, and cost behavior. 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. The description adds meaningful parameter-level guidance beyond the schema by explaining that *_file_path values should be existing Magic Hour paths or file_path values from the upload-URL endpoint, and that direct public URLs may work but can fail. It also highlights key parameter families like face_swap_mode and start/end seconds.

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 by stating exactly what the API does: 'Create the same Face Swap you can make in the browser, but programmatically.' It is unambiguous that this is a face-swap video creation tool and clearly conveys that it launches an async video generation job, distinguishing it from photo-only face swap and other video creation siblings.

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 names automation, batch processing, and app/pipeline integration as the intended use cases. The MCP guidance is concrete: it tells agents to call wait_for_video_project or poll GET /v1/video-projects/{id} until complete/error/canceled, and to use Generate Upload URLs for inputs. It does not explicitly name alternative face-swap tools as exclusions, but the context is otherwise clear.

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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