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createSpeech

Convert text to speech by cloning the voice from an audio sample you provide (voice-cloning text-to-speech). Both text and sample are required; the text is limited to 1000 characters and the sample is supplied as a URL or base64 audio that must be at most 15MB, with violations returning HTTP 400. The job result is a single audio result containing a URL. Credits are held when the job is accepted and refunded if it fails or is cancelled. Use this when you have a reference voice sample to clone; use createSpeechPreset to speak with a built-in named preset voice instead, and createVoice to design a brand-new voice from a text description rather than cloning one. Pass an optional request_id to tag the result so you can locate it later via listGenerations (type audio). Async generation job: returns {id, status} - poll getApiJob (job and credit contract: see the server instructions).

Credits: This endpoint consumes 1 credits per call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
requestBodyYesPayload for text-to-speech generation using voice cloning

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / properties / requestBody / properties / request_id / description
      Previous value: -"Optional client-provided identifier, unique per request. Re-sending the same request_id returns the existing job instead of generating again. Also usable with the results endpoint."New value: +"Optional client-provided identifier, unique per request. Re-sending the same request_id returns the existing job instead of generating again. Also usable as the request_id filter of listGenerations."
    • changedInput schema / properties / requestBody / properties / text / description
      Previous value: -"Text to convert to speech (max 100 words)."New value: +"Text to convert to speech (max 1000 characters)."
  2. Changed1 schema field changed
    • changedInput schema / properties / requestBody / properties / request_id / description
      Previous value: -"Optional client-provided identifier for this request. Use this to retrieve results later via the results endpoint."New value: +"Optional client-provided identifier, unique per request. Re-sending the same request_id returns the existing job instead of generating again. Also usable with the results endpoint."
  3. Changed1 schema field changed
    • changedInput schema / properties / requestBody / properties / sample / example
      Previous value: -"<url> OR data:image/png;base64,..."New value: +"<url> OR data:audio/mp3;base64,..."
  4. Changed1 schema field changed
    • addedInput schema / properties / requestBody / properties / request_id
      Added value: +{
+  "description": "Optional client-provided identifier for this request. Use this to retrieve results later via the results endpoint.",
+  "type": "string"
+}
  5. Added

TDQS

A4.8/5.0
Behavior5/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It reveals the async job lifecycle ({id, status}, poll getApiJob), credit hold/refund behavior, 1000-character and 15MB constraints, HTTP 400 on violations, and the single-audio-URL result format.

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 long but information-dense, front-loading the core operation before constraints, credits, alternatives, and async behavior. A small redundancy exists around credit consumption, but every other sentence contributes necessary guidance.

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?

Given no annotations, no output schema, and an async generation flow, the description is remarkably complete: it explains inputs, constraints, error behavior, credits, result shape, polling, and sibling routing. An agent has enough context to invoke and monitor the job correctly.

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?

The schema already documents all parameters, so the baseline is 3; the description adds meaningful extra semantics such as the 15MB sample limit, requiredness of both text and sample, HTTP 400 behavior, and request_id idempotency/lookup usage.

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 opens with a specific verb and resource: 'Convert text to speech by cloning the voice from an audio sample you provide.' It also distinguishes this tool from createSpeechPreset and createVoice, making its scope unambiguous.

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?

It explicitly says 'Use this when you have a reference voice sample to clone' and names the alternatives: createSpeechPreset for preset voices and createVoice for designing a voice from text. It also includes when to use request_id for lookup via listGenerations.

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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