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suno_get_mp4

Convert a generated song into an MP4 video with visualizations. Provide the audio ID to get a video suitable for social media and video platforms.

Instructions

Get an MP4 video version of a generated song.

Converts a generated audio into an MP4 video file with visualizations.
Useful for sharing on social media or video platforms.

Use this when:
- You want a video version of a generated song
- You need to share the song on video platforms
- You want a visual representation of the audio

Returns:
    Task ID and MP4 video information.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
audio_idYesThe song ID to get the MP4 video for. This is the 'id' field from a previous audio generation result.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The MCP tool handler function for 'suno_get_mp4'. Defined as async function decorated with @mcp.tool(). It takes an 'audio_id' parameter (the song ID), calls client.get_mp4() to fetch the MP4 video, and formats the result via format_audio_result().
    @mcp.tool()
    async def suno_get_mp4(
        audio_id: Annotated[
            str,
            Field(
                description="The song ID to get the MP4 video for. This is the 'id' field from a previous audio generation result."
            ),
        ],
    ) -> str:
        """Get an MP4 video version of a generated song.
    
        Converts a generated audio into an MP4 video file with visualizations.
        Useful for sharing on social media or video platforms.
    
        Use this when:
        - You want a video version of a generated song
        - You need to share the song on video platforms
        - You want a visual representation of the audio
    
        Returns:
            Task ID and MP4 video information.
        """
        result = await client.get_mp4(audio_id=audio_id)
        return format_audio_result(result)
  • The client helper method 'get_mp4' on the SunoClient class. It sends a POST request to the '/suno/mp4' endpoint with the audio_id, using an async callback pattern. This is the actual API call that the handler delegates to.
    async def get_mp4(self, **kwargs: Any) -> dict[str, Any]:
        """Get MP4 video for a song."""
        logger.info(f"🎥 Getting MP4 for audio: {kwargs.get('audio_id', '')}")
        return await self.request("/suno/mp4", self._with_async_callback(kwargs))
  • The 'format_audio_result' helper used by the handler to format the API response into a JSON string with submission/async polling guidance.
    def format_audio_result(data: dict[str, Any]) -> str:
        """Format audio generation result as JSON.
    
        Args:
            data: API response dictionary
    
        Returns:
            JSON string representation of the result
        """
        return json.dumps(
            _with_submission_guidance(data, "suno_get_task", "suno_get_tasks_batch"),
            ensure_ascii=False,
            indent=2,
        )
  • main.py:258-261 (registration)
    Registration of 'suno_get_mp4' in the HTTP server card tool list (line 259). Also registered via @mcp.tool() decorator in media_tools.py.
    {
        "name": "suno_get_mp4",
        "description": "Get MP4 video of a generated song",
    },
Behavior3/5

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

No annotations are provided, so the description must disclose behavioral traits. It mentions the conversion to MP4 with visualizations and that it returns a Task ID and video info. However, it does not discuss potential limitations, prerequisites, or performance characteristics, leaving gaps for an AI agent.

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 concise (4 sentences plus a bullet list) and front-loaded with the main action. The bullet list is somewhat redundant with the first sentence, but overall it is efficient and easy to parse.

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 the tool has a single parameter and an output schema (implied from context), the description is adequate. It could mention that the audio must first be generated, but this is implicit. For a simple tool, it covers the essential context.

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 coverage is 100% for the single parameter audio_id, with a clear description in the schema. The tool description adds no additional parameter details, so a baseline of 3 is appropriate given the high schema coverage.

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 'Get an MP4 video version of a generated song' and explains it converts audio to MP4 with visualizations. This distinguishes it from sibling tools like suno_get_wav (audio) and suno_get_midi, which handle different output formats.

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 includes a 'Use this when' list, but it only restates the purpose (e.g., 'You want a video version of a generated song'). It does not provide explicit when-not-to-use guidance or compare to alternatives, which would be helpful given the many sibling tools.

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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