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Generate Dub TTS Track

lab_dub_tts

Generate the EN-dub audio track for a sequence via ElevenLabs TTS — SYNCHRONOUS, returns a ready audio_url immediately, no job_id and nothing to poll. Optionally pads the track with silence up to the sequence's own length so a shorter reading doesn't shorten the final cut. Feed the resulting audio_url into lab_dub's en_audio_url to lipsync the sequence to this track.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe line(s) to speak. Kept short — very long text is rejected by the backend before anything is spent.
seq_idYesSequence id (from lab_create_sequence/lab_list_sequences) — positive integer as string
get_costNo`true` reports the price and your balance WITHOUT generating anything and without spending quota. This one is billed flat, one sound rate per run — the length of your text does not change it. Not drawn from the lab_list_models catalog (that covers image and video models only): the TTS voice is picked with `voice_id`, and the price does not follow the voice.
voice_idNoAn ElevenLabs voice id, from lab_dub_voices. Omit to use the backend's configured default voice — NOT a lab_cloned_voices id.
pad_to_videoNoPad the generated track with silence up to the sequence's own duration if the reading comes out shorter. Omit to use the backend default (on).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / additionalProperties
      Added value: +false
  2. First observed

TDQS

A4.5/5.0
Behavior5/5

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

Annotations only mark it non-read-only and non-destructive. The description adds the critical trait that it is SYNCHRONOUS, returns a ready audio_url with no job_id and nothing to poll, and explains the padding behavior — genuinely useful context the structured fields do not carry.

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 tightly written sentences with the synchronous/no-poll fact front-loaded, followed by the optional padding and the downstream wiring. Every 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 mutation tool with no output schema, the description discloses the return shape (immediate audio_url), the cost model context, and the downstream handoff, so an agent has everything needed to call and chain it correctly.

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 five parameters are already documented in the schema. The description restates the padding concept but adds no syntax or format detail beyond what the schema provides; baseline 3 is appropriate.

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?

States a specific verb (Generate) and resource (the EN-dub audio track for a sequence via ElevenLabs TTS), and its synchronous nature immediately distinguishes it from polling-based generation siblings. An agent can identify it apart from lab_dub and lab_voice_tts.

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?

Clearly positions the tool within the dub pipeline by naming the downstream consumer (lab_dub's en_audio_url for lipsync). However, it does not explicitly contrast with the sibling lab_voice_tts, so an agent still has to infer which TTS entry point to pick.

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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