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Design Voice

design_voice

Invent a brand-new voice from a written description instead of cloning a real person: no consent needed and nobody owns it. Describe who they are, how old they sound, their accent, their pace and their feeling, in 20 to 1000 characters, for example "a woman in her thirties, warm low London accent, unhurried, close and conversational like a late night radio host". Optionally add sample_text (100 to 1000 characters) for the line you want to hear it read, or leave it out and one is written for you. The voice is saved to your voice list and the sample line is saved as audio. Costs tokens (a charge applies). Returns voice_id and an audio_url of the sample.

Input Schema

NameRequiredDescriptionDefault
ageNoOptional label, e.g. "middle aged".
nameYesWhat to call this voice in the library, e.g. "Gravel, late night radio".
tagsNoOptional labels for grouping voices.
accentNoOptional label, e.g. "british".
genderNoOptional label, e.g. "female".
descriptionYes20 to 1000 characters describing the voice: age, accent, pace, tone, the feeling it gives.
sample_textNoOptional. 100 to 1000 characters for the voice to read in the sample. Leave out and one is written for you.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.6/5.0
Behavior5/5

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

Goes well beyond the annotations (readOnlyHint=false, destructiveHint=false, openWorldHint=false) by disclosing persistence ('saved to your voice list', 'sample line is saved as audio'), cost ('Costs tokens, a charge applies'), and the no-consent/no-ownership property. Return values are also surfaced (voice_id and audio_url).

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 core value proposition and constraints before the example; every sentence carries information. The long embedded example quote is illustrative but slightly inflates the length, keeping it short of a 5.

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 7-parameter generative tool with no output schema, the description covers the essential concerns: input constraints and an example, persistence side effects, cost signal, and return values. Nothing an agent needs to invoke it correctly is missing.

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 description coverage is 100%, so the baseline is 3; the description adds a concrete worked example for description and clarifies sample_text's fallback behavior ('leave it out and one is written for you'), which is meaningful beyond the schema's field docs.

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 (invent a brand-new voice) and explicitly contrasts with the sibling clone_voice ('instead of cloning a real person'). An agent can distinguish this from clone_voice, change_voice, and list_voices without opening the 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?

Names the alternative (cloning a real person) and the condition that selects this tool (no consent needed, nobody owns it). It does not enumerate every sibling that touches voices (isolate_voice, change_voice), but the primary routing decision is explicit.

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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