Skip to main content
Glama

lip_sync_create_video

Create lip-synced videos programmatically by matching audio to a video or image, enabling automated batch processing and integration into apps or workflows.

Instructions

What this API does

Create the same Lip Sync 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 lip sync into apps, pipelines, or tools

How it works (3 steps)

  1. Upload your inputs (video, image, or audio) with Generate Upload URLs and copy the file_path.

  2. Send a request to create a lip sync job with the basic fields.

  3. 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
NameRequiredDescriptionDefault
nameNoGive your video a custom name for easy identification.Lip Sync - dateTime
styleNoAttributes used to dictate the style of the output
assetsYesProvide the assets for lip-sync. For video, The `video_source` field determines whether `video_file_path` or `youtube_url` field is used
end_secondsYesEnd time of your clip (seconds). Must be greater than start_seconds.
max_fps_limitNoDefines the maximum FPS (frames per second) for the output video. If the input video's FPS is lower than this limit, the output video will retain the input FPS. This is useful for reducing unnecessary frame usage in scenarios where high FPS is not required.
start_secondsYesStart time of your clip (seconds). Must be ≥ 0.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesUnique 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_chargedYesThe 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.

  1. First observedv0.1.0

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 discloses rich behavior: it starts an async job returning id and credits_charged immediately, explains polling until complete/error/canceled, output downloads, per-frame credit charging, resolution limits, and file_path reliability caveats. No contradictions.

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 well-structured with clear headings, bullets, numbered steps, and a dedicated MCP guidance section. Purpose and use cases are front-loaded, and each section contributes operational guidance.

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?

It covers the full workflow: upload inputs, create the job, wait for completion, and download outputs, plus cost behavior and file path rules. Even with the output schema present, the end-to-end lifecycle is described thoroughly.

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 practical value by advising agents to prefer Magic Hour file paths over direct public URLs, explaining when hotlinks fail, and summarizing key options like resolution limits and extra fields.

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 'Create the same Lip Sync you can make in the browser, but programmatically,' naming a specific verb, resource, and value proposition. It clearly distinguishes this as the lip-sync creation API among many image/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 lists automation, batch processing, and adding lip sync into apps/pipelines/tools, giving clear contexts for using it. It doesn't name sibling alternatives or state when not to use it, but the use-case framing is explicit enough.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.