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generate_create-generation

Generate a lipsync video by providing visual and audio inputs (URL or assetId) and receive a generation ID to track and retrieve the result.

Instructions

Create a lipsync video. Provide a video input (or an image for sync-3) and an audio input, each by url or assetId. Returns a generation id — call generate_get-generation once with wait: true and timeout: 55, then read outputUrl. To choose which face to sync in a multi-person video, pass options.active_speaker_detection (auto_detect, or coordinates + frame_number).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputYesArray of input objects. Standard lipsync requests without segments require exactly one visual input (video or image) and one audio or text input. Multi-segment lipsync requests require exactly one video plus one or more audio/text inputs; give each audio/text input a unique refId and reference it from segments[].audioInput.refId. Dubbed lipsync requests using dubParams require exactly one video and no audio/text input because the dubbed audio is extracted from that video.
modelYesname of the model to use for generation.
optionsNoadditional options available for generation.
segmentsNoone or more segments definition to apply edits to
dubParamsNoDubbing parameters. When present, audio is extracted from the single video input, dubbed via ElevenLabs into the target language, and then lipsync is run with the dubbed audio. Do not include audio or text inputs with dubParams; requests that send both are rejected.
projectIdNoAttach this generation to a project (from POST /v2/projects) so it appears in Studio under that project.
webhookUrlNoWebhook URL for generation status updates. When the generation reaches a terminal state, Sync sends a POST request with the generation payload and a Sync-Signature header (`t=<unix>,v1=<hmac_sha256>`). Verify the signature with the organization webhook secret from GET /v2/organizations/webhook/secret. HTTPS is strongly recommended.
outputFileNameNofilename for the generated output (up to 255 characters). Characters outside letters, numbers, dashes and underscores are stripped and spaces become underscores, so "My Clip!.mp4" is stored as "My_Clipmp4".

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idNoSync generation id.
errorNo
modelNoModel used for the generation.
statusNoCurrent generation status.
errorCodeNo
outputUrlNoSigned result URL when the generation is complete. Copy this exact string verbatim; do not reconstruct, shorten, or edit it.
outputDurationNoOutput duration in seconds.
Behavior4/5

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

Beyond the annotations (readOnlyHint false, openWorldHint true, destructiveHint false), the description reveals important behavioral traits: it returns a generation ID and requires polling with a specific timeout, and it exposes options.active_speaker_detection for multi-person videos. This adds meaningful operational context.

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?

Three well-structured sentences. The first establishes the core action, the second gives input and polling instructions, and the third covers an edge case. No redundant information; each sentence earns its place.

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 8 parameters and a nested input schema, the description covers the essential workflow (input format, polling, output retrieval) and specific scenarios (sync-3 image, multi-person selection). The rich schema and output schema handle remaining details.

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?

While the schema covers all 8 parameters, the description adds value by specifying that inputs use 'url' or 'assetId' and by documenting options.active_speaker_detection with allowed values (auto_detect or coordinates+frame_number), which is absent from the schema's generic 'options' object.

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 a lipsync video,' a specific verb+resource statement, and clarifies input types (video/image, audio) and the asynchronous polling workflow. It distinguishes itself from sibling tools by explaining the generation lifecycle and options like active_speaker_detection.

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?

Provides clear usage context: how to supply inputs, call generate_get-generation with wait:true and timeout:55, and handle multi-person face selection. However, it does not explicitly mention alternatives such as create-lipsync or state when not to use this tool, so it stops short of full guidance.

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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