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neosapience

Typecast API MCP Server

by neosapience

Convert text to speech using the specified voice and parameters

text_to_speech

Convert text to speech with customizable voice, emotion, volume, pitch, and tempo. Adjust audio output using TTS models and optional loudness normalization.

Instructions

Convert text to speech using the specified voice and parameters

Args:
    voice_id: ID of the voice to use
    text: Text to convert to speech
    model: TTS model to use (ssfm-v21 or ssfm-v30, default: ssfm-v30)
    emotion_type: For ssfm-v30: 'preset' for explicit emotion or 'smart' for context-aware inference (default: preset)
    emotion_preset: Emotion preset type. v21: normal/happy/sad/angry. v30: adds whisper/toneup/tonedown (default: normal)
    emotion_intensity: Intensity of the emotion, between 0.0 and 2.0 (default: 1.0)
    previous_text: For smart mode - previous context text for emotion inference
    next_text: For smart mode - next context text for emotion inference
    volume: Audio volume level, between 0 and 200 (default: 100)
    audio_pitch: Audio pitch adjustment, between -12 and 12 (default: 0)
    audio_tempo: Audio playback speed, between 0.5 and 2.0 (default: 1.0)
    audio_format: Audio format, either 'wav' or 'mp3' (default: wav)
    target_lufs: Optional absolute loudness normalization target in LUFS (-70.0 ~ 0.0).
        Mutually exclusive with a custom volume value on this non-streaming endpoint.

Returns:
    Local mode: path to the saved audio file.
    Remote mode: dict with audio_url and expires_in_seconds.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes
modelNossfm-v30
volumeNo
voice_idYes
next_textNo
audio_pitchNo
audio_tempoNo
target_lufsNo
audio_formatNowav
emotion_typeNopreset
previous_textNo
emotion_presetNonormal
emotion_intensityNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations provided, the description carries the full burden. It discloses important behaviors such as the mutual exclusivity between target_lufs and custom volume, default values, and the distinction between local and remote modes. However, it does not mention potential side effects like API costs or rate limits.

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 front-loaded with the purpose but then presents a lengthy bulleted parameter list. Given the complexity (13 parameters), this verbosity is justified. The structure is clear with 'Args:' and 'Returns:' sections, though it could be slightly more concise.

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?

The description covers all parameters and return values (local path or remote dict). It explains local vs remote mode and the mutual exclusivity constraint. Despite no annotations, the description is complete enough for an agent to use the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It explains every parameter in detail, including valid ranges, defaults, model-specific options (e.g., emotion_preset for v21 vs v30), and context-specific parameters (previous_text, next_text). This adds significant value beyond the raw schema.

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 begins with a clear statement: 'Convert text to speech using the specified voice and parameters.' This uses a specific verb ('convert') and resource ('text to speech'), and the detailed parameter list helps differentiate it from sibling tools like 'text_to_speech_stream' and 'text_to_speech_with_timestamps'.

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?

The description provides no guidance on when to use this tool versus its siblings (e.g., streaming or timestamp variants). It only describes parameters and does not mention trade-offs or preferred use cases.

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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