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speak

Convert text to speech using Microsoft Edge TTS with customizable voice, rate, volume, and pitch settings for audio output.

Instructions

Speak text aloud using Microsoft Edge TTS.

Args:
    text: Text to speak
    voice: Voice name (e.g., en-US-AriaNeural, uk-UA-OstapNeural)
    rate: Speech rate (-50% to +100%, e.g., +20%)
    volume: Volume (-50% to +100%, e.g., +10%)
    pitch: Pitch (-50Hz to +50Hz, e.g., +5Hz)

Returns:
    Confirmation message

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes
voiceNo
rateNo
volumeNo
pitchNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
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. It clearly indicates this is an output/action tool (speaking aloud) rather than a data retrieval tool, and mentions the specific TTS engine. However, it doesn't disclose important behavioral aspects like whether this blocks execution, requires specific permissions, has rate limits, or what happens if TTS fails.

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 perfectly structured and concise. It starts with the core purpose, then provides a well-organized parameter section with clear examples, and ends with the return value. Every sentence earns its place with no wasted words.

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?

Given the tool's moderate complexity (5 parameters, 1 required), no annotations, and the presence of an output schema (which handles return values), the description is mostly complete. It covers all parameters thoroughly and states the core purpose. The main gap is lack of behavioral context about execution characteristics.

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?

The description provides excellent parameter semantics beyond the schema's 0% coverage. For all 5 parameters, it adds crucial context: text is 'Text to speak', voice includes example names, rate explains the range and format, volume explains the range and format, and pitch explains the range and format. This fully compensates for the schema's lack of descriptions.

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 specific action ('Speak text aloud') and the technology used ('using Microsoft Edge TTS'), which distinguishes it from potential siblings like get_config or list_available_voices. The verb+resource combination is precise and unambiguous.

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 guidance is provided on when to use this tool versus alternatives. While the description mentions Microsoft Edge TTS, it doesn't explain when text-to-speech is appropriate or how this tool relates to sibling tools like list_available_voices (which might help select voices).

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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