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VoiceHue

Text to speech

text_to_speech

Convert text to spoken audio with a VoiceHue voice. Returns the audio (MP3 by default, or WAV) and a one-line summary with the voice, number of characters and duration. Up to 1,200 characters per call. Use list_voices first to get a valid voice id.

Input Schema

NameRequiredDescriptionDefault
textYesThe text to speak.
speedNoSpeaking speed, 0.5 to 2.
voiceYesA voice id from list_voices.
formatNoAudio format. MP3 is the default.mp3

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior3/5

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

Annotations cover the safety profile (readOnlyHint=false, destructiveHint=false, openWorldHint=true, idempotentHint=false), so the description's job is to add context. It does useful work by describing the return payload (audio file plus a one-line summary with voice, characters, duration) and the character ceiling, but it says nothing about the non-idempotent nature, latency, or whether repeated calls with the same input are billed/regenerated — a real gap for an openWorld, non-idempotent generation tool.

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?

Four short sentences, front-loaded with the action, then the return value, then the limit, then the prerequisite — the ordering matches how an agent needs the information. Every sentence carries non-redundant content; nothing to cut.

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?

Absent an output schema, the description takes responsibility for describing the return value and does so concretely. It also covers the required-voice prerequisite and size limit; only error behavior and the implications of the non-idempotent annotation are left unaddressed, which is a minor gap for a four-parameter tool.

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 every parameter is already documented, and the description adds no semantics beyond it — the MP3 default and 1,200-character limit are both restated from the schema, and the speed parameter is not mentioned at all. Baseline 3 is appropriate when the schema does the heavy lifting.

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?

States a specific verb and resource ("Convert text to spoken audio with a VoiceHue voice") and immediately distinguishes it from the only sibling, list_voices, both in purpose and in prerequisite ordering. An agent can tell exactly which of the two tools produces audio.

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?

Gives an explicit prerequisite: "Use list_voices first to get a valid voice id," which is the main failure mode for this tool, plus a hard constraint of 1,200 characters per call. It stops short of stating when not to use it (e.g. no batching / long text handling), so it is clear context rather than a full usage rubric.

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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