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hedging8563

TokenLab MCP Server

by hedging8563

Create speech

create_speech

Convert text into spoken audio using text-to-speech. Select voice, model, speed, and format to produce speech from written input.

Instructions

Create speech Generates audio from the input text (Text-to-Speech).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputYesText to synthesize.
modelNoTTS model. Query GET /v1/models?recommended_for=tts for the current shortlist.tts-1
speedNoSpeech speed for model families that support it.
voiceNoVoice selector for OpenAI-compatible, Gemini, xAI, and MiniMax-compatible routes. Some MiniMax routes also accept voice_id.
promptNoOptional speaking style prompt for Gemini TTS models.
voice_idNoProvider-native voice selector for MiniMax-compatible speech models.
temperatureNoSampling temperature for Gemini-compatible TTS routes.
instructionsNoOptional style or delivery instructions for OpenAI-compatible TTS models that support them.
language_codeNoOptional language code for Gemini, xAI, and compatible TTS routes.
stream_formatNoTokenLab delivery format. stream_format=sse is not supported for tts-1 or tts-1-hd.audio
response_formatNoAudio format. Common values include mp3, opus, aac, flac, wav, and pcm. Supported values vary by model family.
Behavior3/5

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

Annotations indicate this is a non-read-only, non-destructive operation. The description accurately describes generation, but does not add behavioral context beyond the annotations, such as cost, latency, or side effects like file creation.

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 a single concise sentence that immediately conveys the tool's primary function. While very brief, it is appropriately front-loaded and contains no superfluous information.

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

Completeness2/5

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

Given 11 parameters and no output schema, the description lacks crucial context such as return type (e.g., audio file), format details, or expected behavior for different model families. More detail is needed for practical use.

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 100%, so the schema fully documents all 11 parameters. The tool description adds no additional meaning to parameters, maintaining the baseline score for high coverage.

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 title 'Create speech' and description 'Generates audio from the input text (Text-to-Speech)' clearly state the verb-resource relationship and the tool's specific function, distinguishing it from sibling tools like transcribe_audio or create_music.

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?

No usage guidance is provided. The description does not explain when to use this tool vs. alternatives (e.g., transcribe_audio) or mention any prerequisites, such as required voice model availability.

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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