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eroq — AI video, images, voice and music

Generate speech

generate_speech

Text-to-speech, returns MP3 audio. Costs credits per 100 characters — models: eroq-voice-one (? cr), eroq-voice-turbo (? cr). Voices: roster ids (see list_voices), a cloned voice id, or char: to speak as a character.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesWhat to say. Punctuation drives the intonation.
modelNoSpeech model id (default eroq-voice-one).
speedNoPlayback speed, 1 = natural.
voiceNoVoice id (default aria).
expressivenessNo0 = steady read, 1 = theatrical.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations, the description must carry the behavioral burden, and it does disclose that output is MP3 and that the call costs credits per 100 characters — genuinely useful context. But the credit amounts are left as literal '? cr' placeholders, and there is no mention of auth requirements, rate limits, or maximum text length, so the disclosure is partial.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Tight, front-loaded fragment that leads with purpose and return type, then cost, then options, with zero filler. The unresolved '? cr' placeholders are the only blemish, making it slightly less actionable than it looks.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 5-parameter generation tool with no output schema, the description correctly covers the return artifact (MP3) and cost model, so an agent knows what comes back. It still omits practical limits — maximum text length, exact credit price, and failure modes — and the unknown credit figures undercut the cost warning it does provide.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3, but the description adds real meaning beyond the schema for the `voice` parameter by enumerating accepted forms (roster id, cloned voice id, or char:<id> for character speech) which the schema only calls 'Voice id (default aria)'. It also ties the two models to their credit cost framing, though the values are unknown.

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+resource (text-to-speech generation) and the output artifact (MP3 audio), which cleanly separates it from generate_image, generate_music and generate_video in the sibling list. An agent can identify the tool's job without opening the schema.

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 names available models and voice sources and routes the agent to list_voices for roster ids, which is a useful pointer. However, it never states when this tool is preferred over alternatives or any preconditions (e.g., credit balance, text length limits), leaving usage largely implied.

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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