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synthesize

Render text to speech as base64-encoded WAV audio. Choose from six voices and 23 languages, with accurate pronunciation of numbers, dates, and order IDs.

Instructions

Render text to speech. Returns the WAV audio base64-encoded.

voice: one of gandr-ava, gandr-dane, gandr-jenny, gandr-leo, gandr-lewis, gandr-mia. language: a two-letter code, call list_languages for the 23 supported. Any voice can speak any of them. temperature: 0.1-1.2 pitch range (omit for the tuned default). cfg_weight: 0.2-1.0 pacing. sample_rate: 8000-48000.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes
voiceNogandr-ava
languageNoen
cfg_weightNo
sample_rateNo
temperatureNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations are provided, so the description carries full burden. It discloses the output format (WAV base64), parameter ranges and meanings (e.g., 'temperature: 0.1-1.2 pitch range', 'cfg_weight: 0.2-1.0 pacing'), and a key capability ('Any voice can speak any of them'). It lacks details on potential errors, text length limits, or rate limits, but for a generation tool this is solid coverage.

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?

The description is front-loaded with the core purpose, then uses a compact line-per-parameter format. Every sentence adds value: output format, parameter ranges, cross-reference, and defaults. It is information-dense without fluff, achieving high conciseness.

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 output schema present, return value structure is covered by schema, so description does not need to explain it. It covers all six parameters with meanings and ranges, and gives a cross-reference for languages. It could mention text length limits or error behavior, but for a moderately complex TTS tool, it is largely complete.

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 description must compensate. It does: voice options are enumerated, language is explained with a pointer to list_languages, temperature and cfg_weight get non-obvious meanings (pitch range and pacing), and sample_rate range is given. This adds substantial meaning beyond the 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 clearly states the tool's function with a specific verb and resource: 'Render text to speech.' It also specifies the output format ('Returns the WAV audio base64-encoded'), and the sibling tools (list_voices, list_languages, get_usage) are distinct supplementary tools, so there is no ambiguity.

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?

The description implies usage for generating speech and even references a sibling tool: 'call list_languages for the 23 supported,' guiding the user on a related dependency. However, it does not explicitly state when NOT to use this tool, but there are no alternative TTS tools among siblings, and the purpose is clear enough.

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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