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ahamed-kashif

text-to-speech

text_to_speech

Generate MP3 audio from text with customizable language, accent, and speaking rate.

Instructions

Convert text into spoken audio (MP3) using Google TTS (gTTS).

Args:
    text: The text to speak. Required, must be non-empty.
    language: Language code such as "en", "bn", "es". Defaults to "en".
    slow: Speak more slowly when True.
    accent: Google domain TLD controlling accent for some languages,
        e.g. "com" (US), "co.uk" (UK), "com.au" (AU), "co.in" (India).
    output_path: Where to write the MP3. If omitted, a timestamped file
        is created under the configured output directory.

Returns:
    A human-readable status string including the saved file path, or an
    "Error: ..." message describing what went wrong.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slowNo
textYes
accentNocom
languageNoen
output_pathNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are provided, so the description must carry the burden. It explains the conversion process, parameter effects, and return format. However, it omits details like network dependencies, file overwriting behavior, or authentication needs, which are relevant for a tool using an external service.

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 concise with a clear lead sentence, followed by well-organized Args and Returns sections. Every sentence adds value with no redundancy.

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 the tool's complexity (5 parameters, no schema descriptions, no annotations), the description covers all parameters and the return value comprehensively. The presence of an output schema is noted, and the description aligns with it.

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%, but the description compensates fully by explaining each parameter (required, defaults, examples, behavior). It adds significant meaning beyond the bare schema types and names.

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 converts text to spoken audio (MP3) using gTTS, with a specific verb ('Convert') and resource ('text into spoken audio'). It distinguishes from the sibling tool 'list_languages' by its action and output format.

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 context of siblings (list_languages) implies this tool is for speech generation, not language listing. However, no explicit when-to-use or when-not-to-use guidance is provided, leaving some ambiguity.

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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