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List narration voices

list_voices
Read-onlyIdempotent

The AI voices narration can use: ElevenLabs default voices (they speak every language) and a few from its voice library per language, with id, name, gender, accent, age, language and description. Also the workspace’s AI narration status: whether AI voices are available (Pro and Team: an active subscription, or a trial with a smaller allowance; recording and uploading happen in the editor), the voice model, and this month’s character allowance. Pass a voice id to generate_voiceovers.

Requires: demos:read.

Input Schema

NameRequiredDescriptionDefault
languageNoOnly default voices plus library voices of this language, e.g. "es".

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already cover readOnly/idempotent/non-destructive, and the description adds substantial extra context: it discloses the two voice sources, the workspace narration status payload, subscription/trial allowance semantics, the monthly character allowance, and the demos:read auth requirement. It stops short of 5 only because it doesn't state anything about result size or filtering behavior beyond the language parameter.

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-loads what is returned and keeps the permission note last, which is good ordering. The parenthetical on Pro/Team subscriptions and 'recording and uploading happen in the editor' is slightly digressive but still useful orientation for the caller.

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 compensates fully by enumerating the voice fields and the workspace narration-status fields, plus the auth scope. For a single-optional-parameter read tool, an agent has everything needed to call and interpret it.

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 coverage is 100% and the lone 'language' parameter is already documented in the schema ('Only default voices plus library voices of this language'). The description adds no additional meaning about that parameter's format or edge cases, so baseline 3 applies.

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+resource ('The AI voices narration can use... with id, name, gender, accent, age, language and description') and even enumerates the returned fields. It distinguishes itself from action siblings like generate_voiceovers by positioning this as the lookup that feeds them.

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?

The final line 'Pass a voice id to generate_voiceovers' gives an explicit downstream-use path, and 'Requires: demos:read' states a prerequisite. It does not enumerate when-not-to-use or name a direct alternative list tool, so it stops short of a 5.

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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