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Generate speech or sound

generate_audio

Generate speech (text-to-speech) for the title. Billed per character. The voice comes from the tier's model unless you pass one in params. Runs as a job: the call waits up to about two minutes and returns the result; if it is not ready by then it returns the generation with status queued/running — poll get_generation with its id.

Input Schema

NameRequiredDescriptionDefault
textYesThe words to say out loud. They are read verbatim, so do not put instructions here.
tierNoQuality/price tier, e.g. image-cheap or video-premium. Call list_ai_tiers for the current list. Omit it to use this title's default tier.
modelNoModel name, if you insist on a specific one (optional). ⚠ Setting it cancels the tier COMPLETELY — together with its provider and its settings — so params may stop applying. Prefer `tier`.
paramsNoModel settings, as declared by the vendor for the chosen tier — for example {"aspect_ratio":"16:9","resolution":"720p","duration":"4"}. Call list_ai_tiers to see which keys this tier accepts, their allowed values and defaults. Values are strings. Unknown keys are ignored rather than rejected.
providerNoProvider key, if you insist on one (optional). Cancels the tier the same way.
title_idNoWhich game this is for — a label for the report, optional. Credits are always the publisher's, so leaving it out is normal for assets that belong to no game.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior5/5

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

Adds rich behavior beyond the annotations: 'Billed per character' discloses cost, and the async job contract (waits ~2 minutes, otherwise returns status queued/running, then poll get_generation) is exactly what an agent needs to invoke it correctly. Annotations only mark it as a non-destructive open-world write, so the description carries and delivers the real behavioral load.

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 tightly packed sentences, front-loaded with purpose, then billing, then the async contract. Every clause earns its place with no filler.

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?

For a 6-param mutation tool with no output schema, the description covers cost, async lifecycle, and the polling path well. It does not say what the successful result actually contains (e.g., an audio URL/asset), leaving the agent to infer the artifact from get_generation.

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 meaning by explaining the tier/model/params interaction — the voice comes from the tier's model unless passed in params — which clarifies how params and tier relate in practice. It stops short of explaining params keys or provider precedence beyond what the schema already documents.

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?

States a specific verb and resource — 'Generate speech (text-to-speech) for the title' — which distinguishes it reasonably from generate_music/generate_image/generate_video siblings. The title mentions 'or sound' but the description narrows to speech only, leaving a small mismatch with the advertised scope.

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 clear operational context: when the call returns synchronously versus when to 'poll get_generation with its id', and how the voice is chosen (tier's model unless overridden in params). It does not state when to prefer this over generate_music for non-speech audio, so sibling routing is only 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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