Skip to main content
Glama
Sign In
@aiwerk/mcp-server-elevenlabs

@aiwerk/mcp-server-elevenlabs

by AIWerk
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

create_finetune

Train a custom music model by uploading audio files and specifying a name, genre, and model version; spends ElevenLabs credits.

Instructions

Create Music Finetune Spends ElevenLabs credits.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "name": "<name>",
      "primary_genre": "<primary_genre>"
    },
    "name": "create_finetune"
  }
}

Input Schema

NameRequiredDescriptionDefault
nameYesName for the finetune (5-200 characters).
tagsNoTags to associate with the finetune.
model_idNo
visibilityNoFinetune visibility. Only 'private' and 'workspace' can be set.
files_pathsNoAudio files to train on. Local paths.
primary_genreYesPrimary musical genre of the finetune.
files_filenamesNoFilenames to send for "files". Some endpoints infer the audio format from them.
files_base64_listNoBase64 contents for "files", one entry per file. Use this when the server cannot read your local disk.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=false, idempotentHint=false, and openWorldHint=true, so the mutation profile is covered. The description adds one genuinely useful behavioral fact — that the call consumes ElevenLabs credits — but says nothing about training time, data requirements, or irreversibility.

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?

Very short and front-loaded with the action, but it is a single run-on fragment ('Create Music Finetune Spends ElevenLabs credits.') with awkward capitalization and a missing clause boundary, which slightly hurts readability.

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

Completeness3/5

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

For an 8-parameter training operation with no output schema and no annotations beyond the standard hints, the description is thin — it omits the mutual-exclusivity of the three file-input parameters and any notion of training/cost implications. The rich schema partly compensates.

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 88%, so the schema already documents nearly all eight parameters, including file-input variants. The description adds no parameter-level meaning (e.g. that exactly one of files_paths/files_base64_list/files_filenames must be supplied). Baseline 3 applies.

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 clear verb ('Create') and resource ('Music Finetune'), which separates it from get_finetune, update_finetune, and delete_finetune in the sibling list. It does not explicitly name those alternatives, but the action+resource pairing is unambiguous.

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

Usage Guidelines2/5

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

No guidance on when to create a finetune versus using an existing model, no prerequisites (training data required), and no mention of the sibling read/update/delete operations. The only usage-adjacent fact is the credit cost.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Other Tools