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neosapience

Typecast API MCP Server

by neosapience

Get a list of available voices using V2 API with filtering support

get_voices

Get available text-to-speech voices from Typecast V2 API, with optional filters for model, gender, age, and use cases.

Instructions

Get a list of available voices for text-to-speech using V2 API

Args:
    model: Optional filter for specific TTS models (ssfm-v21 or ssfm-v30).
    gender: Optional filter for voice gender (male or female).
    age: Optional filter for voice age group (child, teen, young_adult, middle_aged, senior).
    use_cases: Optional filter for voice use case (e.g. 'audiobook', 'narration', 'documentary').
        Pass a single use case string supported by the V2 voices endpoint.

Returns:
    List of available voices with enhanced metadata including gender, age, and use cases.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ageNo
modelNo
genderNo
use_casesNo
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It mentions the V2 API and that it returns a list with metadata, but does not explicitly state it is a read-only operation, any rate limits, or caching behavior. Adequate but not rich.

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?

The description is structured in a clear docstring format with Args and Returns, no redundant sentences, and appropriately sized for the tool's complexity.

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?

Given 4 optional parameters, no output schema, and no annotations, the description covers the tool's purpose, parameters, and return value. It does not mention pagination or result limits, but for a filtered listing tool it is largely complete.

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 0%, so the description compensates well: it explains each parameter with examples (model, gender, age, use_cases) and clarifies use_cases expects a single string. This adds meaning beyond the schema's bare types.

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?

The description clearly states the tool retrieves a list of available voices for text-to-speech, specifies the V2 API, and distinguishes itself from siblings like get_voice (singular) and recommend_voices (suggestive) through its filtering focus.

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

Usage Guidelines2/5

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

No explicit guidance on when to use this tool versus alternatives like get_voice or recommend_voices; lacks when-not-to-use or context for selecting filters.

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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