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by io.github.CalmNguyen
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Design a custom voice

design_custom_voice

Create a new personal AI voice from a natural-language description (Gemini Voice Design). Uses one slot of the user's custom-voice quota - confirm with the user first. Good for a unique narrator or a character voice in a video/podcast. Returns the new voice token.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "display_name": "<display_name>",
      "gender": "<gender>",
      "language_code": "<language_code>",
      "prompt": "<prompt>"
    },
    "name": "design_custom_voice"
  }
}

Input Schema

NameRequiredDescriptionDefault
genderYes
promptYesDescribe age, accent, timbre, personality, energy and pace. Example: 'Calm male documentary narrator in his 40s, deep warm timbre, slow confident pace, neutral American accent.'
display_nameYes
language_codeYesBCP-47 locale, e.g. en-US, vi-VN.

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 declare readOnly=false, destructive=false, idempotent=false, so the safety profile is covered. The description adds value beyond them by disclosing that the call consumes one slot of a custom-voice quota and that it returns a `voice` token. It doesn't say whether the voice can later be removed or what happens when the quota is exhausted.

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 short sentences, front-loaded with the action, followed by the cost warning and the return value. No filler and every sentence carries information the agent needs.

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 correctly compensates by naming the returned `voice` token, and it flags the quota side effect that annotations don't cover. Minor gaps remain around quota recovery/error behavior and the undocumented `display_name` parameter.

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 50%: `prompt` and `language_code` are documented in the schema, `gender` is a self-evident enum, but `display_name` has no description anywhere. The description only loosely maps 'natural-language description' to `prompt` and adds no constraints (length caps, locale format, gender effect) beyond the schema, 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 and resource ('Create a new personal AI voice') plus the mechanism ('from a natural-language description'), which is enough to distinguish it from siblings like list_my_custom_voices, get_voice_details, and list_voices. An agent can identify the right tool 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?

Gives clear situational guidance ('Good for a unique narrator or a character voice in a video/podcast') and an important precondition ('confirm with the user first'). It does not name an alternative (e.g., picking an existing voice via list_voices), so it stops short of explicit when-not guidance.

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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