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mohemohe

voiceroid_daemon-mcp

by mohemohe

speak_text

Convert text to speech audio with customizable voice parameters like speed, pitch, and emphasis, then play it back using VOICEROID2 technology.

Instructions

Generate speech audio from text and play it

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesText to speak
kanaNoOptional phonetic reading in kana
volumeNoVoice volume (0-2, default: 1)
speedNoSpeech speed (0.5-4, default: 1)
pitchNoVoice pitch (0.5-2, default: 1)
emphasisNoEmphasis level (0-2, default: 1)
Behavior2/5

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 mentions 'play it' which implies an audio output action, but fails to describe critical behaviors like whether this is a read-only or mutative operation, potential side effects (e.g., audio playback on the system), error handling, or performance characteristics. This is inadequate for a tool with audio generation and playback.

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 extremely concise with just one sentence that efficiently conveys the core functionality. It's front-loaded with the essential action ('Generate speech audio from text') and includes the additional behavior ('and play it') without any wasted words.

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?

For a tool that generates and plays audio with 6 parameters and no output schema, the description is insufficient. It lacks information about the audio format, playback mechanism, error conditions, or what happens on success. With no annotations to provide behavioral context, this leaves significant gaps for the agent to understand the tool's full behavior.

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 has 100% description coverage, providing clear documentation for all 6 parameters including ranges and defaults. The description adds no additional parameter semantics beyond what's in the schema, so it meets the baseline of 3 where the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with a specific verb ('Generate speech audio from text') and resource ('speech audio'), making it immediately understandable. However, it doesn't distinguish itself from sibling tools like 'convert_text' which might have overlapping functionality, preventing a perfect score.

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?

The description provides no guidance on when to use this tool versus alternatives like 'convert_text' or 'test_connection'. It lacks context about prerequisites, limitations, or scenarios where this tool is preferred, leaving the agent with minimal usage direction.

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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