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generate_audio

Convert text to speech with selectable voice and language. Get an mp3 URL for standalone use or as voiceover in video composition.

Instructions

Generate speech from text (text-to-speech): pick a voice and language, get an mp3 back. Returns request_id and cost in credits immediately — narration is quick, poll with wait_generation until COMPLETED, then output.audio.url is the mp3. Pass the resulting request_id as voiceover_request_id in compose_video to narrate a multi-scene video, or use the mp3 on its own.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe text to speak. Russian or English. Keep each call within the model limit (typically ≤500 characters) — split a long narration into separate calls and stitch them if needed.
inputNoAdvanced model-specific parameters (see get_model input_schema). Merged with the fields above.
modelNoModel slug from list_models (type audio). Omit to use the default text-to-speech model.
voiceNoVoice preset from the model input_schema (see get_model) — different male/female narrators. Omit for the model default.
languageNoLanguage of the text, e.g. 'ru' or 'en'. Omit for the model default.
stabilityNoDelivery stability where supported: lower is more expressive, higher is more even.
wait_secondsNoHow long to wait inline for completion before returning a request_id.
idempotency_keyNoOptional stable key to safely retry without creating (and paying for) a duplicate generation. Reuse the same key when retrying the same request.
Behavior5/5

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

Discloses asynchronous behavior (immediate request_id, polling needed), cost return, and safe retry via idempotency_key. No contradictions with annotations (readOnlyHint=false, idempotentHint=false).

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?

Single paragraph, efficiently structured: main action first, then return/polling details, then integration use case. No wasted words.

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?

Covers workflow end-to-end: generation, polling, output retrieval, and integration with compose_video. Handles the async nature and optional parameters adequately without needing an output schema.

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?

Adds meaningful context beyond schema, e.g., character limits for text, language support (Russian/English), and usage of stability parameter. Schema coverage is 100%, so description complements well.

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 'Generate speech from text (text-to-speech)' and specifies the resource (audio), action (generate), and output format (mp3). It distinguishes from sibling tools like generate_music by mentioning integration with compose_video.

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?

Provides explicit guidance on polling with wait_generation and using the result for compose_video. Includes advice on splitting long texts. Does not explicitly mention when not to use, but is clear enough given sibling context.

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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