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AIWerk

@aiwerk/mcp-server-elevenlabs

by AIWerk

dubbing_project_create

Create and queue a dubbing project for audio or video, with optional target language and webhooks; uses ElevenLabs credits.

Instructions

Create Dubbing Project Spends ElevenLabs credits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keytermsNoKey terms to bias transcription and translation toward (for example, product or brand names). At most 1,000 terms; each term at most 50 characters and 5 words; the characters `<>{}[]\` are not allowed. Terms are trimmed and deduplicated.
model_idNoDubbing model (`dubbing_v1` or `dubbing_v2`) every language target of this project is dubbed with. Defaults to `dubbing_v2`. Fixed at create time — the source is prepared for this model, so neither the project nor an individual target can change it later.
file_pathNoThe source media file to dub: an audio or video file of at most 3 GiB. Provide this or `source_url`, not both. Local path.
referenceNoOptional free-form string (at most 500 characters) to identify the project on your end. Stored and echoed back verbatim; it does not affect the dub.
source_urlNoPublic HTTP(S) URL the source media is fetched from server-side, subject to the same size and format limits as an upload. Provide this or `file`, not both.
file_base64NoBase64 contents for "file". Use this when the server cannot read your local disk.
webhook_idsNoIDs of workspace webhooks to notify as this project progresses — the alternative to polling, and what we recommend. Each receives a `dubbing_project_ready` or `dubbing_project_failed` event for the project, and a `dubbing_language_completed` or `dubbing_language_failed` event for every language unde
file_filenameNoFilename to send for "file". Some endpoints infer the audio format from it.
source_languageNoBCP-47 language tag of the source media; must be a language the transcription model supports. Any region or script subtag is ignored, since transcription is per-language. Omit to auto-detect.
target_languageNoOptional shortcut: also create a language target in this BCP-47 language, queued to start once the project is ready — equivalent to creating the project and then creating one language target. Must be one of the [languages the dubbing model supports](https://elevenlabs.io/docs/help-center/product/dub
transcript_pathNoEnterprise only. Optional JSON transcript to use instead of transcribing the source: a `{"segments": [...]}` document, at most 20,000 segments and 4 MiB. See [Bring your own transcript](https://elevenlabs.io/docs/eleven-api/guides/how-to/dubbing/bring-your-own-transcript) for the segment fields and
transcript_base64NoBase64 contents for "transcript". Use this when the server cannot read your local disk.
transcript_filenameNoFilename to send for "transcript". Some endpoints infer the audio format from it.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.1/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=false, destructiveHint=false, openWorldHint=true, and idempotentHint=false. The description adds the important side effect that it 'Spends ElevenLabs credits,' which is beyond annotations. However, it does not cover auth requirements, rate limits, or failure behavior, so it is only partially transparent.

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 very short and front-loads the core action 'Create Dubbing Project.' The second fragment 'Spends ElevenLabs credits.' is grammatically incomplete but still concise and informative. There is no wasted text.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex creation tool with 13 parameters and no output schema, the description is far too sparse. It does not explain what the tool returns (e.g., a project identifier), how it relates to language targets or webhooks, or when to choose it over similar creation tools. The rich schema covers parameters, but the description leaves significant contextual gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so all 13 parameters are fully documented in the input schema. The description adds no parameter-specific meaning beyond what the schema already provides, making the baseline score of 3 appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The phrase 'Create Dubbing Project' states a clear verb and resource. However, it does not distinguish this tool from siblings like create_dubbing or dub, and it omits any scope details such as what a 'dubbing project' encompasses.

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

Usage Guidelines2/5

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

The description offers no guidance on when to use this tool versus alternatives such as create_dubbing or dub, nor does it mention prerequisites or context. It only notes the credit cost, which is not usage 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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