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text_to_speech

Convert text into spoken audio in 31 languages. Automatically chunks long text and supports configurable output modes.

Instructions

Generate natural-sounding speech audio from text. Use this when the user wants to: hear text read aloud, create narration or voiceover, generate voice audio, preview how text sounds when spoken, or convert any writing into spoken audio. Supports 31 languages including Korean, English, and Japanese. There is no text-length limit: long text is automatically split (auto-chunked) by the service, and credit usage and latency scale with the length of the text. Set output_mode ('files', 'resources', or 'both') to control how audio is returned, and autoplay=true to play it back on macOS. These per-call parameters REPLACE the removed SUPERTONE_MCP_OUTPUT_MODE and SUPERTONE_MCP_AUTOPLAY environment variables; autoplay now defaults to false. A default voice is already configured -- just call this tool directly. Only call search_voice if the user explicitly asks to change or browse voices.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes
modelNo
speedNo
styleNo
autoplayNo
languageNo
voice_idNo
streamingNoWhen true, stream the audio via the chunked synthesize path instead of a single one-shot request. Streaming is ONLY supported by model=sona_speech_1; using streaming=true with any other model returns a validation error. Defaults to false (one-shot synthesize).
output_modeNo
pitch_shiftNo
output_formatNo
normalized_textNoOptional pre-normalized text to use for synthesis (SDK 0.2.3). Only applies to the sona_speech_2 and sona_speech_2_flash models; other models ignore it. When omitted, the SDK default (None) is used.
include_phonemesNoWhen true, request phoneme timing data alongside the audio (SDK 0.2.3). Defaults to false. Note: the phoneme data is not yet surfaced in the tool response — this is a pass-through flag for now.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations provided, so description carries full burden. Discloses auto-chunking for long text, credit and latency scaling, output mode options, autoplay default false, and that parameters replace environment variables. Lacks details on error handling or model-specific behaviors, but covers key behavioral traits.

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?

Well-structured and front-loaded. Begins with purpose, lists use cases, then key behaviors, parameter guidance, and sibling differentiation. Every sentence adds value without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Tool has 13 parameters and no annotations; an output schema exists. Description covers essential use case and key behaviors but lacks details on many parameters' semantics and error conditions. Adequate for simple usage but incomplete for complex parameter combinations.

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 description coverage is only 23%, so description must compensate. It adds meaning for output_mode and autoplay (options, defaults) and mentions text length behavior, but does not elaborate on model, speed, style, voice_id, pitch_shift, output_format, normalized_text, or include_phonemes beyond what the schema defines. Adequate for basic use but incomplete.

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 generates speech audio from text, lists specific use cases (hear text read aloud, narration, voiceover, etc.), and differentiates from sibling search_voice by explicitly stating to only use search_voice for changing voices.

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

Usage Guidelines5/5

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

Provides explicit when-to-use conditions (user wants to hear text, create narration, etc.) and when-not-to-use (only call search_voice for changing voices). Also notes that a default voice is configured, so no need to set voice_id unless the user requests a change.

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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