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lip_sync_create_video

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: 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
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.

TDQS

A4.9/5.0
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 of disclosing behavior. It does so thoroughly: async job creation, immediate id and credits_charged response, status values (complete/error/canceled), download URLs, cost behavior, and resolution limits. This is exceptionally transparent for an unannotated tool.

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 highly structured and front-loaded. It uses clear headings, numbered steps, and focused bullet lists. Every section serves a purpose: what it does, who it's for, how to use it, key options, cost, and MCP-specific guidance. No filler or redundant repetition.

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 asynchronous video-generation tool with no annotations, the description is remarkably complete. It covers the full lifecycle: upload, job creation, polling, completion, downloads, cost estimation, resolution limits, and failure modes. An output schema exists, so return values need not be repeated.

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 value beyond the schema by explaining the preferred upload flow for *_file_path values, the async return behavior, and resolution/cost context. It doesn't deeply re-explain each parameter, but the schema already does that well.

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: programmatically creating Lip Sync videos. It clearly distinguishes this tool from sibling video/image tools by naming the exact capability and includes 'Good for' examples that clarify its intended niche.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit step-by-step workflow guidance: upload inputs first, create the job, then poll or use wait_for_video_project. It also gives concrete file_path handling recommendations and explains when direct URLs may fail, which is actionable guidance beyond the schema.

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