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MCPGeneration-for-ElevenLabs

MCPGeneration-for-ElevenLabs

create_music_finetune

Creates a music finetune from audio samples after rights confirmation and paid-operation approval, allowing tailored music generation from your samples.

Instructions

Create an optional music finetune from audio samples. Requires local enablement, rights confirmation, and both paid-operation gates.

Input Schema

NameRequiredDescriptionDefault
nameYes
tagsNo
model_idNomusic_v1
allow_paidNo
visibilityNo
sample_pathsYes
primary_genreYes
rights_confirmedNo
allow_unknown_costNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.3/5.0
Behavior3/5

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

With no annotations, the description carries the full burden, and it does disclose real behavioral constraints: local enablement and dual paid-operation gates imply this can fail for non-technical reasons and may incur cost. It omits whether training is long-running/asynchronous, what happens to samples, and what a successful call returns.

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?

Two short sentences with the action front-loaded and the prerequisites second — efficient and easy to scan. The only waste is the vague 'optional' qualifier, which earns no place without explanation.

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

Completeness2/5

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

For a 9-parameter, annotation-free mutation tool with no output schema, the description is thin: five parameters are undocumented and nothing explains the model_id enum choice, visibility semantics, or the return value (presumably a finetune id to poll or fetch). The gating information is genuinely valuable but does not cover the whole surface an agent needs.

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 0%, so the description must compensate for all 9 parameters. It maps loosely to sample_paths ('audio samples'), rights_confirmed ('rights confirmation'), and allow_paid/allow_unknown_cost ('both paid-operation gates'), but name, primary_genre, tags, model_id, and visibility get no mention at all.

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 ('Create ... music finetune from audio samples'), which cleanly separates it from the sibling read/update/delete finetune tools. The word 'optional' is unexplained and adds ambiguity rather than precision.

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?

It lists prerequisites for invocation (local enablement, rights confirmation, paid-operation gates), which is useful gating context. However, it never says when to choose a finetune over the sibling generate_music family, nor what conditions make this the wrong tool.

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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