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Speak in Cloned Voice

lab_voice_tts

Speak text in a project's cloned voice (via lab_voice_clone) — SYNCHRONOUS, returns a ready audio_url, no job_id. The voice house is whichever one cloned this voice; you do not pick it here. Consent-sensitive like the clone itself: only speak text you have the right to put in that voice's mouth.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe line(s) to speak in this voice.
get_costNoPrice this call instead of running it: returns the estimate and the balance, charges nothing, produces no audio. Worth doing before a long passage, because both houses bill by the size of the text, not per run — so the same tool can cost wildly different amounts depending on what you hand it. The rates behind the estimate are not repeated here; lab_list_models carries them.
providerNoCross-check, not a choice: the house that cloned this voice is the one that speaks with it, because a voice id from one house means nothing to the other. Pass it only to assert what you believe the voice is; a mismatch is refused before anything is charged. Omit it and the stored voice decides. Which house a voice belongs to: lab_list_voices. What each house can do: lab_list_models.
voice_idYesA cloned voice's id (lab_cloned_voices.id, from lab_voice_clone or lab_list_voices) — positive integer as string, NOT the raw provider voice id.
project_idYesProject id (from lab_list_projects) — positive integer as string

Schema Changelog

Changes observed during successful MCP inspections.

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

TDQS

A3.9/5.0
Behavior4/5

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

Annotations only declare readOnlyHint=false and destructiveHint=false, leaving the sync/async profile and billing undisclosed. The description adds real value: it is synchronous, returns a ready audio_url with no job_id to poll, and is consent-sensitive. The billing-by-text-size behavior is largely carried by the get_cost parameter description rather than the main text.

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?

Two compact sentences with the core purpose and the sync/no-job_id distinction front-loaded. The em-dash asides are slightly dense but every clause carries information; nothing is wasted.

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

Completeness4/5

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

With no output schema, the description usefully states the return shape (ready audio_url). Combined with annotations covering the safety profile and a fully documented schema, an agent has enough to call it correctly; only the explicit sibling alternatives remain unaddressed.

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 the schema already fully documents text, voice_id, project_id, get_cost, and provider, including the provider cross-check and the billing rationale. The description adds little beyond this, so the 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 (Speak) and resource (text in a project's cloned voice), and explicitly distinguishes itself by being SYNCHRONOUS with a ready audio_url and no job_id. It also ties itself to its sibling lab_voice_clone, so an agent can route between cloning and speaking without opening either schema.

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

Usage Guidelines3/5

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

Conveys when to use it implicitly (to voice text through a cloned voice) and adds a consent constraint ('only speak text you have the right to put in that voice's mouth'). It gives provider guidance ('you do not pick it'), but never explicitly contrasts with alternative TTS siblings like lab_dub_tts or lab_dub.

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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