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generate_tts

Generates a Text to Speech audio voiceover using the provided voice object and written text string. This tool converts written text into a spoken audio file. It returns the direct download link, the duration of the audio, and the file size of the generated voiceover.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe written text string that you want to convert into a spoken audio voiceover.
voiceYesThe complete voice object retrieved from the get_voices tool. Do not modify or create this object manually; you must pass the exact object returned by the get_voices tool.

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the output (download link, duration, file size) but does not mention any side effects, authentication requirements, rate limits, or costs. For a generation tool, these aspects could be relevant, but the core behavior is adequately described.

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 two sentences, front-loaded with the action, and contains no filler. Every sentence adds value: the first explains the core function, the second lists the return values. It is concise and well-structured.

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?

The description covers the essential information: what the tool does, the inputs needed, and what it returns. It does not mention errors or edge cases, but for a straightforward TTS generation tool with a thorough schema, this is largely sufficient. The lack of an output schema is compensated by the explicit list of return values.

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?

The input schema already provides full descriptions for both parameters (100% coverage), including the voice object's required fields. The tool description itself adds no parameter-specific semantics beyond what the schema states, so the baseline of 3 is appropriate.

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 uses a specific verb ('Generates') and identifies the resource ('Text to Speech audio voiceover'), clearly stating it converts text to a spoken audio file. This distinguishes it from siblings like get_voices (retrieves voices) and usage_statistic (tracks usage) by focusing on audio generation.

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

Usage Guidelines3/5

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

The description implies the tool is used when you have a voice object and text, but it does not explicitly state when to use it versus alternatives or mention prerequisites. The schema's note about using get_voices is not part of the tool description itself, so usage context is implied rather than explicit.

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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TDQS

A4.1/5.0
Disambiguation5/5

Each tool has a distinct, non-overlapping purpose: generate_tts creates audio, get_voices fetches voice options, and usage_statistic monitors quota. No ambiguity exists between them.

Naming Consistency3/5

Tool names are readable and mostly follow a verb_noun pattern (generate_tts, get_voices), but 'usage_statistic' deviates by using a noun phrase without a verb. The style is consistent in snake_case, but the structural pattern is mixed.

Tool Count5/5

Three tools is a well-scoped count for a TTS server, covering the essential operations: listing voices, generating speech, and checking API usage. No unnecessary tools or glaring omissions.

Completeness4/5

The core workflow (get voices -> generate TTS) is fully covered, with usage monitoring as a useful addition. Minor gaps might include advanced audio format specifications, but nothing that blocks typical usage.

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