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suno_create_custom_model

Train a reusable custom music model from authorized audio examples. Feed in 6 to 24 audio URLs to capture your unique sound for future music generation.

Instructions

Create a reusable custom music model from authorized audio examples.

This is a paid, long-running operation. Call it only after the user confirms the source files and the 5.6-Credit list price.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesName for the custom music model.
audio_urlsYes6 to 24 authorized audio URLs used to train the custom music model.
callback_urlNoWebhook callback URL for custom model creation status.
idempotency_keyNoOptional Idempotency-Key header value for safe retries.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Addedv0.1.15

TDQS

A4.3/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden of disclosure. It reveals that this is a 'paid, long-running operation' and that it requires user confirmation before invoking, which is critical behavioral context. It could add more detail about asynchronous status or cost implications, but the provided warning is substantial.

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?

The description is two short paragraphs with no filler. The core purpose is front-loaded, and the important usage warning about payment and user confirmation is placed immediately after in a compact, readable format.

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 the presence of a full output schema and complete parameter descriptions, the description provides the key missing context: it is paid, long-running, and must only be invoked after explicit user confirmation. This is sufficient for an agent to decide whether and when to call this 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 the schema fully documents all four parameters. The description's mention of 'source files' and 'authorized audio examples' maps to the audio_urls parameter but adds little new information beyond what the schema already states.

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 begins with a specific verb and resource: 'Create a reusable custom music model from authorized audio examples.' This clearly distinguishes the tool from generation tools like suno_generate_music and suno_generate_with_custom_model, since it creates a reusable artifact rather than generating music.

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?

The description gives an explicit precondition: 'Call it only after the user confirms the source files and the 5.6-Credit list price.' This is strong guidance on when to invoke the tool. It does not explicitly name alternatives such as suno_generate_with_custom_model or suno_get_custom_model, but the precondition is clear enough for safe usage.

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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