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GenMagic

by io.github.yumaheymans
OverviewDiscussionsAdmin

Generate speech (text to speech)

generate_speech

Turn text into spoken audio with GenMagic. Returns the audio inline plus a hosted URL. The text is voiced verbatim.

Input Schema

NameRequiredDescriptionDefault
inputYesThe text to speak, read aloud verbatim.
modelNoOptional GenMagic model id for the speech (call list_models with category "audio" for the ids and prices). Omit it to let GenMagic choose its default.
voiceNoOptional voice name (e.g. alloy); list_models shows each speech model's voices. Defaults to a neutral voice.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedInput schema / properties / model
      Added value: +{
+  "description": "Optional GenMagic model id for the speech (call list_models with category \"audio\" for the ids and prices). Omit it to let GenMagic choose its default.",
+  "type": "string"
+}
    • changedInput schema / properties / voice / description
      Previous value: -"Optional voice name (e.g. alloy). Defaults to a neutral voice."New value: +"Optional voice name (e.g. alloy); list_models shows each speech model's voices. Defaults to a neutral voice."
  2. First observed

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already declare non-read-only, non-destructive, non-idempotent, and open-world generation, so the safety profile is covered. The description adds useful context that the audio is returned inline with a hosted URL and that text is spoken verbatim, but it does not mention that calls are billed or that repeated calls yield different audio despite idempotentHint=false.

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?

Three short sentences, front-loaded with purpose, then output, then the verbatim constraint. No filler and nothing redundant with the title.

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?

With no output schema, the description correctly compensates by describing the return (inline audio plus hosted URL). Fully documented parameters and annotations leave little else needed, though cost/billing and the non-idempotent nature of repeated generation are unaddressed.

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% and all three parameters document their own defaults and the list_models lookup path. The description adds nothing beyond the schema on parameters; 'voiced verbatim' merely restates the input field's own description, so the baseline 3 applies.

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 ('Turn text into spoken audio'), which unambiguously separates this tool from generate_text, generate_music, generate_image, and create_video without needing to name them. It also states the output shape (inline audio plus hosted URL) in the same breath.

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?

No explicit when-to-use or when-not-to-use guidance is given, and no prerequisites (credits, auth, model selection) are stated. The use case is strongly implied by the purpose, but the agent must infer that selecting a voice/model belongs to list_models rather than here.

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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