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ai_voice_generator_create_audio

Generate speech from text by selecting a voice. Starts an async audio generation job and returns a project ID to fetch the completed audio.

Instructions

Generate speech from text. Each character costs 0.1 credits. The cost is rounded up to the nearest whole number.

MCP guidance:

This starts an async audio generation job and returns id plus credits_charged immediately. If the user wants the finished result, call the wait_for_audio_project helper with the returned id, or poll the matching GET /v1/audio-projects/{id} endpoint until status is complete, error, or canceled. Completed projects include downloads with direct URLs. The custom wait helper also returns exact_download_urls separately from expiration metadata.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`name`	No	Give your audio a custom name for easy identification.	Voice Generator - dateTime
`style`	Yes	The content used to generate speech.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`id`	Yes	Unique ID of the audio. Use it with the [Get audio Project API](https://docs.magichour.ai/api-reference/audio-projects/get-audio-details) to fetch status and downloads.
`credits_charged`	Yes	The amount of credits deducted from your account to generate the audio. We charge credits right when the request is made. If an error occurred while generating the audio, credits will be refunded and this field will be updated to include the refund.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 23, 2026

TDQS

A4.4/5.0

Behavior5/5

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

With no annotations, the description carries the full burden and does so well: it discloses that this is an async job, that it returns id and credits_charged immediately, and how to reach completion via statuses and downloads. It even notes the helper's exact_download_urls behavior, which is beyond what structured fields would convey.

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 compact, front-loads the core action, and then uses a clearly labeled MCP guidance section for the async workflow. Every sentence adds operational value without repetition.

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

Completeness5/5

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

For a two-parameter tool with an output schema, the description covers the essential runtime behavior: immediate response fields, terminal statuses, how to retrieve completed audio, and download URLs. An agent has enough to invoke it and handle the follow-up correctly.

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 the baseline is 3, but the description adds meaningful parameter-related context by explaining that each character costs 0.1 credits and is rounded up. This gives the agent a cost implication for the prompt parameter that the schema does not state.

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 opens with 'Generate speech from text,' a specific verb and resource that clearly identifies this as a text-to-speech creation tool. It does not explicitly contrast itself with the sibling ai_voice_cloner_create_audio, so it misses the top score for sibling differentiation.

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?

The MCP guidance gives clear post-invocation steps: call wait_for_audio_project with the returned id or poll the GET endpoint until a terminal status. It does not state when to choose this tool over the voice cloner or other audio tools, so it lacks explicit when-not/alternatives guidance.

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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