FilmLab

Clone Voice

lab_voice_clone

Clone a voice from real audio or video material into a project (fal.ai minimax voice-clone) — the source for lab_voice_tts and for lab_dub's voice_id+dub_text path. SYNCHRONOUS: the clone runs inside the request and can take a while to return; there is no job_id to poll. The source needs a clean speech track of workable length — too short or silent material is rejected before anything is spent. Voice cloning from real material is consent-sensitive: only clone voices you have the right to use, and the Fish Audio path requires you to say so explicitly.

Input Schema

TableJSON Schema

Name	Required	Description
`name`	Yes	A label for this cloned voice, shown in lab_list_voices — needed to tell several clones of one project apart later.
`get_cost`	No	`true` reports the price and your balance WITHOUT cloning anything and without spending quota. A clone is billed per clone (a MiniMax clone costs far more than a Fish clone — ask with get_cost) — the length of the source material does not change it, and the clone is then reusable for as many readings as you like. Not drawn from the lab_list_models catalog (that covers image and video models only); there is no model to pick here.
`provider`	No	Which voice house clones it. Omit to stay on the default path — an existing workflow does not change vendor just because another one exists. The choice sticks to the voice: whichever house made it is the one that later speaks with it, so lab_voice_tts has no say in this. What each house can do and what it charges is not stated here — lab_list_models carries that.
`project_id`	Yes	Project id (from lab_list_projects) — positive integer as string
`einwilligung`	No	Consent of the person whose voice is being uploaded. Required on the fish_audio path, which refuses without it before anything is charged. This is not the moderation gate: that one reads the label for abuse, this one asserts you are allowed to use this person's voice at all. Do not set it to be rid of the error — set it because the consent exists. Provider capabilities and prices: lab_list_models.
`likeness_ref`	No	Consent id from the Likeness Register (LK-XXXXX-XXXXX-XXXXX-XXXXX) for the person whose voice this is. When set, the register is checked live (active, purpose voice_synthesis, the project's target markets) and the clone is refused if the consent does not hold. Needed for a real person's voice in a project for children or young people.
`sample_audio_url`	Yes	URL of the source audio or video the voice is cloned from. Needs a real, sufficiently long speech track — material without one is rejected before anything is spent.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 11, 2026
- addedInput schema / additionalProperties
```
Added value: +false
```

Changed1 schema field changedOct 8, 2026

addedInput schema / properties / likeness_ref

Added value: +{
+  "description": "Consent id from the Likeness Register (LK-XXXXX-XXXXX-XXXXX-XXXXX) for the person whose voice this is. When set, the register is checked live (active, purpose voice_synthesis, the project's target markets) and the clone is refused if the consent does not hold. Needed for a real person's voice in a project for children or young people.",
+  "type": "string"
+}

First observedOct 3, 2026

TDQS

A4.5/5.0

Behavior5/5

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

Annotations only say readOnlyHint=false/destructiveHint=false, but the description adds the traits that actually matter: it is SYNCHRONOUS with no job_id to poll, bad source material is rejected before anything is charged, billing is per clone (not per source length) and the clone is reusable, and cloning is consent-sensitive with a required assertion on the fish_audio path.

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?

Purpose and the synchronous/no-poll constraint are front-loaded, and each sentence carries a distinct point (routing, sync behavior, source quality, consent). Slightly dense with some overlap against the schema's billing notes, but nothing is padding.

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 still covers the non-obvious return behavior (synchronous, no pollable job) and downstream usage, but it only indirectly implies that the result is a reusable voice_id-style artifact. Minor gap for a 7-parameter mutation tool.

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%, so provider enum, einwilligung, likeness_ref, get_cost and sample_audio_url are already fully documented in the schema. The description restates the consent/billing intent but adds little parameter-level detail beyond that structured text, 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+resource (clone a voice from real audio/video material into a project) and explicitly positions it relative to siblings: it is the source for lab_voice_tts and lab_dub's voice_id+dub_text path. An agent can distinguish it from text-to-speech and dubbing tools without opening any schema.

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?

Explicit routing: use get_cost to price before spending, omit provider to stay on the default path, use einwilligung/likeness_ref for consent-sensitive real voices. It also names lab_list_models as the place to compare provider capability and price rather than duplicating that here, which is actionable guidance rather than a dead end.

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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