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SunoMCP

by AceDataCloud
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Python
Hybrid

suno_samples_music

Add AI-generated musical samples to a selected time range of uploaded audio to insert loops or motifs into existing tracks.

Instructions

Add AI-generated samples to uploaded audio.

Takes your uploaded audio and adds AI-generated musical samples
within the specified time range.

Use this when:
- You want to add sample loops or motifs to existing music
- You need to enhance a track with additional musical elements
- You want to add AI-generated samples to a specific section

Returns:
    Task ID and the audio with samples added.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoModel version to use.chirp-v5-5
audio_idYesID of the uploaded audio to add samples to. Must be uploaded via suno_upload_audio.
samples_endNoEnd time in seconds for adding samples. Must be less than total song duration.
callback_urlNoWebhook callback URL for asynchronous notifications.
samples_startNoStart time in seconds for adding samples. Default is 0.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.1.18
    • changedInput schema / properties / model / enum
      Previous value: -[
-  "chirp-v3-0",
-  "chirp-v3-5",
-  "chirp-v4",
-  "chirp-v4-5",
-  "chirp-v4-5-plus",
-  "chirp-v5",
-  "chirp-v5-5"
-]New value: +[
+  "chirp-v6",
+  "chirp-v6-wild",
+  "chirp-v6-mini",
+  "chirp-v3-0",
+  "chirp-v3-5",
+  "chirp-v4",
+  "chirp-v4-5",
+  "chirp-v4-5-plus",
+  "chirp-v5",
+  "chirp-v5-5"
+]
  2. Addedv0.1.5
  3. Removedv0.1.3
  4. Addedv0.1.1

TDQS

A3.8/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden. It describes the core behavior (adds samples), the input requirement (uploaded audio), and the output (Task ID and audio). It implies an asynchronous task model, which is consistent with the callback_url parameter. It could mention that it modifies the audio in place or that processing time might be significant, but the provided details are adequate.

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 well-structured with clear use cases and a returns section. It's concise, front-loads the main action, and uses bullet points for readability. No fluff.

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?

The description is complete for an asynchronous tool with a rich output schema (Task ID and audio). It covers prerequisites (uploaded audio), parameter hints (time range), and return type. Given the output schema exists and schema covers parameters, there are no critical gaps. It could mention that samples are generated using a specific model, but that's in the schema.

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 already documents all parameters. The description mentions 'within the specified time range' which aligns with samples_start and samples_end, but doesn't add details like how to specify the samples themselves (not a parameter) or what happens if samples_end is null. It adds marginal value beyond the schema, maintaining the baseline.

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?

The description clearly states the tool adds AI-generated samples to uploaded audio within a specified time range. It distinguishes itself from siblings like suno_extend_music or suno_stems_music by focusing on sample addition, though it doesn't explicitly name alternatives.

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 'Use this when' bullets provide some context for when to use it (adding sample loops or motifs, enhancing a track, adding to a specific section). However, it doesn't mention when not to use it or how it differs from similar tools like suno_replace_section or suno_overpainting.

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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