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SunoMCP

by AceDataCloud
OverviewSchemaSourceRelated ServersScoreDiscussionsAdmin
Python
Hybrid

suno_list_models

List all Suno models with limits, features, and use cases to select the right one for your music generation.

Instructions

List all available Suno models and their capabilities.

Shows all available model versions with their limits, features, and
recommended use cases. Use this to understand which model to choose
for your music generation.

Model comparison:
- chirp-v6: Current v6 model
- chirp-v6-wild: v6 Wild model
- chirp-v6-mini: v6 Mini model
- chirp-v5-5: Previous model name, retained for compatibility
- chirp-v5: High quality, 8-minute max duration
- chirp-v4-5-plus: High quality with 8-minute duration
- chirp-v4-5: Recommended balance of quality and speed, 4-minute duration
- chirp-v4: Good quality, 150 seconds max
- chirp-v3-5/v3: Legacy models, 120 seconds max

Returns:
    Table of all models with their version, limits, and features.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.5
  2. Removedv0.1.3
  3. First observedv0.1.0

TDQS

A4.2/5.0
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. It discloses the return format (a table) and the scope (all available models). It also lists model comparisons with limits and durations, which is useful context for agent behavior. Does not mention any side effects (it's a read operation), but since nothing suggests otherwiseton, it's adequately transparent.

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 structured with a clear intro, a list of model comparisons, and a return-value note. It is front-loaded with the purpose and includes a helpful bulleted list. It is a bit longer than necessary, but every part contributes to decision-making. No filler or redundancy.

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 tool's simplicity (zero params), the description covers everything an agent needs: what it does, what it returns, and model-specific details to aid selection. The output schema exists, which further covers returns. No missing critical information.

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?

The tool has zero parameters, so the description doesn't need to explain params. The baseline for zero params is 4, and the description exceeds the baseline by adding useful model details that an agent might need. Schema coverage is 100% trivially, but the description adds value by providing model-specific context.

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 clearly states it lists all available Suno models and their capabilities, which distinguishes it from generation and other tools. It provides a specific verb and resource, making the purpose unmistakable.

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?

The description says 'Use this to understand which model to choose for your music generation,' giving clear context but not explicit when-not-to-use or alternatives. It implies usage before generation tasks but doesn't mention alternative info sources like suno_list_custom_models.

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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