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suno_underpainting

Add AI-generated instrumental accompaniment to uploaded vocal tracks, turning acapella recordings into full musical arrangements.

Instructions

Add AI-generated accompaniment/instrumental background to uploaded audio.

Takes your uploaded vocal track and adds an AI-generated instrumental
accompaniment beneath it (underpainting = adding music under vocals).

Use this when:
- You have a vocal recording and want to add music behind it
- You want to give an acapella track a full musical arrangement
- You need to add instrumental backing to existing vocals

Returns:
    Task ID and the audio with accompaniment added.

Input Schema

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

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The main handler function for suno_underpainting. It is an async MCP tool decorated with @mcp.tool() that accepts an audio_id (uploaded via suno_upload_audio), underpainting_start, underpainting_end, model, and callback_url. It builds a payload with action='underpainting' and calls client.generate_audio(), then formats the result.
    @mcp.tool()
    async def suno_underpainting(
        audio_id: Annotated[
            str,
            Field(
                description="ID of the uploaded audio to add accompaniment to. Must be uploaded via suno_upload_audio."
            ),
        ],
        underpainting_start: Annotated[
            float,
            Field(description="Start time in seconds for adding accompaniment. Default is 0."),
        ] = 0.0,
        underpainting_end: Annotated[
            float | None,
            Field(
                description="End time in seconds for adding accompaniment. Must be less than total song duration."
            ),
        ] = None,
        model: Annotated[
            SunoModel,
            Field(description="Model version to use."),
        ] = DEFAULT_MODEL,
        callback_url: Annotated[
            str | None,
            Field(description="Webhook callback URL for asynchronous notifications."),
        ] = None,
    ) -> str:
        """Add AI-generated accompaniment/instrumental background to uploaded audio.
    
        Takes your uploaded vocal track and adds an AI-generated instrumental
        accompaniment beneath it (underpainting = adding music under vocals).
    
        Use this when:
        - You have a vocal recording and want to add music behind it
        - You want to give an acapella track a full musical arrangement
        - You need to add instrumental backing to existing vocals
    
        Returns:
            Task ID and the audio with accompaniment added.
        """
        payload: dict = {
            "action": "underpainting",
            "audio_id": audio_id,
            "underpainting_start": underpainting_start,
            "model": model,
            "callback_url": callback_url,
        }
    
        if underpainting_end is not None:
            payload["underpainting_end"] = underpainting_end
    
        result = await client.generate_audio(**payload)
        return format_audio_result(result)
  • The parameter schema (Pydantic Field annotations) for suno_underpainting defining audio_id (str), underpainting_start (float, default 0.0), underpainting_end (float|None), model (SunoModel enum, default DEFAULT_MODEL), and callback_url (str|None).
    async def suno_underpainting(
        audio_id: Annotated[
            str,
            Field(
                description="ID of the uploaded audio to add accompaniment to. Must be uploaded via suno_upload_audio."
            ),
        ],
        underpainting_start: Annotated[
            float,
            Field(description="Start time in seconds for adding accompaniment. Default is 0."),
        ] = 0.0,
        underpainting_end: Annotated[
            float | None,
            Field(
                description="End time in seconds for adding accompaniment. Must be less than total song duration."
            ),
        ] = None,
        model: Annotated[
            SunoModel,
            Field(description="Model version to use."),
        ] = DEFAULT_MODEL,
        callback_url: Annotated[
            str | None,
            Field(description="Webhook callback URL for asynchronous notifications."),
        ] = None,
  • The @mcp.tool() decorator registers suno_underpainting as an MCP tool. The mcp server instance is imported from core.server.
    @mcp.tool()
    async def suno_underpainting(
  • main.py:231-233 (registration)
    The tool is also listed in the HTTP server card (server_card endpoint) for tool discovery, with name 'suno_underpainting' and description 'Add AI accompaniment to uploaded vocal audio'.
        "name": "suno_underpainting",
        "description": "Add AI accompaniment to uploaded vocal audio",
    },
Behavior3/5

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 states the tool adds accompaniment and returns a Task ID and audio, but does not disclose whether the operation is destructive, auth requirements, or rate limits. Basic transparency, but could be more detailed.

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?

Description is concise with a title, explanation, use-case list, and returns section. Every sentence adds value, though the returns section is somewhat redundant given the output schema. 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?

Given 5 parameters, 1 required, and an output schema, the description covers the essential: purpose, usage, and high-level behavior. It doesn't elaborate on model selection or prerequisites beyond the schema, but overall complete enough.

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 baseline is 3. The description adds minimal extra meaning beyond the schema (e.g., 'vocal track' hints at audio_id purpose). The param descriptions in the schema are adequate.

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 the tool adds AI-generated accompaniment to uploaded audio, with specific examples like 'adding music under vocals'. It distinguishes itself from siblings like suno_overpainting by explaining the underpainting concept.

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 cases: when you have a vocal recording, want to give an acapella track a full arrangement, or need instrumental backing. Lacks explicit when-not-to-use or alternatives, but context is clear.

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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