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SunoMCP

SunoMCP

by AceDataCloud
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

suno_remaster_music

Remaster an existing song to enhance clarity, dynamics, and overall sound quality. Use it to polish older AI-generated tracks into production-ready audio.

Instructions

Remaster an existing song to improve audio quality.

Takes a previously generated song and applies audio remastering to enhance
clarity, dynamics, and overall sound quality.

Use this when:
- You want to improve the audio quality of a generated song
- You want a song generated with an older model to sound better
- You need a polished, production-ready version

Returns:
    Task ID and the remastered audio information.

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": {
      "audio_id": "<audio_id>",
      "variation_category": "<variation_category>"
    },
    "name": "suno_remaster_music"
  }
}

Input Schema

NameRequiredDescriptionDefault
modelNoModel version to use for remastering. Supported choices are chirp-v4-5-plus, chirp-v5, and chirp-v5-5.chirp-v5-5
audio_idYesID of the audio to remaster. This is the 'id' field from a previous generation.
callback_urlNoWebhook callback URL for asynchronous notifications.
variation_categoryYesRequired remaster variation intensity. Use 'high' for maximum variation, 'normal' for balanced, or 'subtle' for minimal changes.

Output 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. Changed3 schema fields changedv0.1.11
    • changedInput schema / properties / model / description
      Previous value: -"Model version to use for remastering. Newer models produce better results."New value: +"Model version to use for remastering. Supported choices are chirp-v4-5-plus, chirp-v5, and chirp-v5-5."
    • addedInput schema / properties / variation_category
      Added value: +{
+  "description": "Required remaster variation intensity. Use 'high' for maximum variation, 'normal' for balanced, or 'subtle' for minimal changes.",
+  "enum": [
+    "high",
+    "normal",
+    "subtle"
+  ],
+  "title": "Variation Category",
+  "type": "string"
+}
    • changedInput schema / required
      Previous value: -[
-  "audio_id"
-]New value: +[
+  "audio_id",
+  "variation_category"
+]
  3. Addedv0.1.5
  4. Removedv0.1.3
  5. First observedv0.1.0

TDQS

A4/5.0
Behavior3/5

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

With no annotations, the description is the sole source of behavioral context. It discloses that existing audio is remastered and the result is a Task ID and remastered audio info, hinting at async behavior. However, it omits whether the original audio is preserved, permission/rate-limiting implications, or mechanism for retrieving the result, so transparency is partial.

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 well-structured with an opening statement, explicit use-case bullets, and a return-format note. However, the first two sentences are slightly redundant ('Remaster... to improve audio quality' vs 'Takes... applies... to enhance clarity, dynamics, and overall sound quality'), which prevents a perfect score.

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 full schema coverage, an output schema, and a moderately simple operation, this description is complete for an agent to decide when to use it and what to expect. It covers the purpose, variants/by-products, and indicates an asynchronous task flow via the returned Task ID; no critical context is missing.

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?

All 4 parameters are fully described in the schema (100% coverage), including the enum values and the meaning of audio_id. The description adds no new parameter-level meaning; it only reinforces the existing schema, so the baseline of 3 is appropriate.

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 uses the specific verb 'Remaster' with resource 'an existing song' and clearly states the purpose: improving audio quality. It distinguishes the tool from siblings like suno_generate_music or suno_cover_music by scoping it to a previously generated song, leaving no ambiguity about what action it performs.

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?

Provides explicit 'Use this when' context: improving quality, modernizing older models, and getting a polished version. This is clear context for choosing this tool. It does not explicitly state when not to use it or name alternatives, but the guidance is sufficient.

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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