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SunoMCP

by AceDataCloud
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

suno_samples_music

Add AI-generated samples to uploaded audio within a specified time range to enhance tracks with loops or motifs in targeted sections.

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

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_endNoRequired sample interval end in seconds, greater than start and no later than source duration.
callback_urlNoWebhook callback URL for asynchronous notifications.
samples_startNoSample interval start in seconds, at least 0. Default is 0.

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv0.1.25
    • changedInput schema / properties / samples_end / description
      Previous value: -"End time in seconds for adding samples. Must be less than total song duration."New value: +"Required sample interval end in seconds, greater than start and no later than source duration."
    • changedInput schema / properties / samples_start / description
      Previous value: -"Start time in seconds for adding samples. Default is 0."New value: +"Sample interval start in seconds, at least 0. Default is 0."
    • addedInput schema / properties / samples_start / minimum
      Added value: +0
  2. 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"
+]
  3. Addedv0.1.5
  4. Removedv0.1.3
  5. Addedv0.1.1

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It does disclose async behavior ('Returns: Task ID'), implying the agent must poll via suno_get_task, and it reiterates the upload prerequisite. It does not state whether the source audio is preserved or overwritten, whether credits are consumed, or any limits on the sample interval beyond what the schema says.

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 core action is front-loaded in the first sentence and the returns note is short. The three 'Use this when' bullets overlap heavily ('add sample loops', 'enhance a track', 'add samples to a specific section'), so a bit of the length does not earn its place.

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?

An output schema exists, so the returns section is largely redundant, and the schema handles parameter semantics. What is missing for a 5-param async generation tool with no annotations is the lifecycle and cost story: whether the original is retained, how the task is retrieved, and any model/cost tradeoffs.

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 parameters are already fully documented, including interval constraints and the model enum. The description only adds the loose phrase 'within the specified time range' and gives no guidance on model selection or interval sizing, so it earns the baseline 3 rather than more.

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 gives a specific verb and resource ('Add AI-generated samples to uploaded audio') and narrows scope to a time range, which separates it from stem tools like suno_add_stem or section tools like suno_replace_section. It stops short of naming the closest siblings explicitly, so an agent must infer the boundary from vocabulary alone.

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 'Use this when' block gives three concrete selection conditions (sample loops/motifs, enhancing a track, targeting a specific section). There are no when-not conditions and no named alternatives, so the agent gets positive guidance but no exclusion logic.

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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