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Generate AI audio

generate_audio
Destructive

Starts an audio generation on the signed-in aicut account (speech, music, a song with vocals or a sound effect, chosen by model) and returns its job id at once; it does NOT wait for the audio. WHEN: the user asks for one of those. Call list_models with type: "audio" first: each model takes DIFFERENT arguments (its parameters; get_recipe starter-audio maps them), and a field it does not publish is rejected, not ignored. A speech voice id comes from list_voices. HOW IT SOUNDS: the knobs marked 'Result only' come at no extra price and need no re-quote; only those a model publishes in parameters are accepted. THE CARD: this call already opened the aicut card: do not call show_generation for this job. WAIT: wait_for_generation (this id, media), again at once while terminal: false, then one short line; no get_* polling, timers, background shells or narrating the wait. No card? Give the user the url. TIMING: audio usually finishes in under a minute (often 10 seconds for a short effect) and can take up to about 4 minutes for a long script. Do not spawn timers or background shells. COST: speech, music and sound effects are priced on a RATE (per character, minute, second: pricing_rates), a song per call. Never multiply it yourself: pass estimate_only: true with the exact settings whenever the user has not agreed to the cost. SPENDS TOKENS: price with estimate_only: true, ask in the SAME message as the price, and wait for a go that names THIS purchase (SPENDING RULES, this server's instructions). IDEMPOTENCY: a reused idempotency_key REPLAYS the first job whatever the settings: never reuse one for a new generation (this server's instructions).

Input Schema

NameRequiredDescriptionDefault
loopNoSound effects: loop seamlessly. Result only, not price.
textNoSpeech: the exact words to speak (priced PER CHARACTER: never padded or shortened to change the price). Sound effect: a description of the sound. Only if the user asked; never to save tokens, never carried over.
modelYesModel id exactly as `list_models` returns it (`elevenlabs-tts-v2` speech, `elevenlabs-music` music, `suno-v6` song, `elevenlabs-sfx` sound effect); it decides which other arguments apply. Use the one the user asked for.
speedNoSpeech, `elevenlabs-tts-v2` only: speaking rate, 1 is normal. Result only.
styleNoSpeech, `elevenlabs-tts-v2` only: how far the speaker's own style is exaggerated. Result only.
titleNoSong: the title (required in Lyrics mode).
voiceNoRequired by speech models. Get it from `list_voices` with `provider: 'elevenlabs', usable_for: 'audio', limit: 100` (21 stock voices; without `limit` you see 20 and cannot tell). The user picks it from `preview_url` links BEFORE this call. Never invent one: it fails AFTER the job exists and is charged. Cloned and custom voices work on `generate_fake_text_video` and `generate_image_story`, not here.
lyricsNoSong, Lyrics mode: words to sing; omit to have them written.
promptNoMusic: the track (mood, instruments, tempo). Song: the Quick-mode idea.
durationNoSound-effect length in seconds within the model's `min`/`max`; billed per second. An omitted length is quoted at a fallback price. Only if the user asked; never to save tokens, never carried over.
length_msNoMusic length in whole milliseconds within the model's `min`/`max`; billed per started minute. An omitted length is quoted at a fallback price. Only if the user asked; never to save tokens, never carried over.
stabilityNoSpeech: how steady the delivery is (lower more expressive, higher flatter). Result only, not price.
custom_modeNoSong: true = Lyrics mode (needs `title`; takes `lyrics`, `vocal_gender`, `duration_seconds`); omit = Quick (needs `prompt`).
music_styleNoSong, required: genre, mood, instruments.
vocal_genderNoSong, Lyrics mode: singer voice (a preference).
estimate_onlyNoPrice only; creates nothing. The only accurate audio quote.
idempotency_keyNoRetry-safety key (IDEMPOTENCY).
duration_secondsNoSong, Lyrics mode: whole seconds. Result only.
prompt_influenceNoSound effects: how literally to follow the text (the model's `min`/`max`). Result only, not price.
similarity_boostNoSpeech, `elevenlabs-tts-v2` and `-v4` only: how closely the delivery sticks to the voice. Result only.
force_instrumentalNoMusic: no vocals. Result only, not price.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

The description discloses behavior beyond the annotations: async job-id return, up-to-four-minute timing, pricing model, estimate-only spending rules, idempotency-key replay semantics, and the fact that the call creates a card. These details are critical for a costly, destructive, open-world generation tool. The annotations do not contradict the description; `destructiveHint: true` is consistent with spending tokens and creating an audio asset.

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?

The description is long but well organized with front-loaded labels such as WHEN, HOW IT SOUNDS, THE CARD, WAIT, TIMING, COST, and IDEMPOTENCY. There is some repetition around not spawning timers or background shells and around 'only if the user asked', but overall the length is justified by the 21-parameter complexity.

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?

Given a 21-parameter generation tool with no output schema, the description covers the full operational picture: async behavior, model-dependent inputs, voice sourcing, waiting behavior, pricing and estimates, spending rules, and idempotency. Nothing essential for correct invocation is missing.

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 description coverage is already 100%, so the schema carries the field-level semantics. The description still adds cross-parameter meaning that the schema cannot express: model-specific arguments, the rejection of fields a model does not publish, voice-id sourcing, and the Lyrics-vs-Quick mode distinction. It does not add much syntax beyond what the schema says, which keeps it below a 5.

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?

The description states a specific verb and resource: starts an audio generation on the signed-in aicut account and returns a job id immediately. It also distinguishes the four supported audio subtypes (speech, music, song, sound effect) by the `model` parameter, which is enough to tell it apart from siblings like `generate_video` or `wait_for_generation`.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives explicit WHEN criteria and named prerequisites (`list_models`, `get_recipe`, `list_voices`) before calling. It also routes the agent to `wait_for_generation` after the call and explains not to use `show_generation` for this job, which is exactly the alternative-selection guidance needed.

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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Directories

Listed in ChatGPT · aicut

Last observed