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List Cloned Voices

lab_list_voices
Read-only

List a project's cloned voices (lab_voice_clone results) — id, label, voice house, sample and preview URLs. This is where a voice_id for lab_voice_tts or lab_dub comes from, and where you look up which house a voice belongs to before pricing it. Both houses are listed unless you narrow it. Reads only — no state changes, no credits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
providerNoNarrow the list to one voice house. Omit it and both come back, which is the normal case — a project can hold voices from either. An unknown value is refused rather than answered with an empty list: 'none found' and 'no such house' are different answers. What the houses can do: lab_list_models.
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

A4.4/5.0
Behavior4/5

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

Annotations cover readOnlyHint=true, and the description corroborates and extends it with 'Reads only — no state changes, no credits,' which adds the credit-cost signal annotations cannot express. It does not mention pagination or result caps, which for a list tool is the main remaining unknown.

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?

Front-loaded with the verb and resource, and the return fields, routing hint, and safety note each carry distinct information in roughly three sentences. The one redundant clause is 'Both houses are listed unless you narrow it,' which largely restates the provider schema description.

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?

With no output schema, the description carries the return shape (id, label, voice house, sample and preview URLs), the default filtering behavior, and the read-only/credit posture — everything an agent needs to call and consume this tool correctly.

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% and the enum is fully documented, so the baseline is 3. The description still adds value by framing provider as 'voice house' with a business purpose ('before pricing it') and by restating the default both-houses behavior, which ties the parameter to the workflow rather than just its type.

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 and resource ('List a project's cloned voices') and immediately scopes it by naming the producing tool (lab_voice_clone results), which cleanly separates it from the sibling lab_dub_voices. It also enumerates the returned fields, so the agent knows exactly what this list yields without opening a schema.

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?

Explicitly states when to reach for it ('This is where a voice_id for lab_voice_tts or lab_dub comes from') and the pre-pricing house lookup use case, which is real routing guidance. It does not distinguish itself from the closely-named lab_dub_voices sibling, so the alternative selection is not fully closed out.

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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