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suno-mcp

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add_samples

Create a Suno task to sample a specific audio segment, returning a task ID and output URLs.

Instructions

Create a Suno task on RunAPI (add samples). Returns a task id, status, and output URLs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
waitNoPoll until the task reaches a terminal status.
modelNoRunAPI model slug for this model line.
promptNoOptional description of the sample to add.
audio_urlYesURL of the audio file to sample.
timeout_msNo
end_secondsYesEnd of the sample range in seconds; must exceed start_seconds.
callback_urlNoWebhook URL for async notifications.
start_secondsYesStart of the sample range in seconds.
poll_interval_msNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.3.5
    • addedInput schema / properties / prompt
      Added value: +{
+  "description": "Optional description of the sample to add.",
+  "type": "string"
+}
  2. Addedv0.3.2

TDQS

C2.9/5.0
Behavior2/5

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

No annotations exist, so the description carries the full burden, and it only discloses that the call returns a task id, status, and output URLs. It says nothing about the async task lifecycle, how the wait/poll_interval_ms/timeout_ms parameters interact with that lifecycle, cost, or auth requirements, which is a significant gap for a job-creating tool.

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?

Two short sentences, front-loaded with the action and followed by the return shape; nothing is redundant. It is economically written, though the brevity comes partly from simply omitting necessary context.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 9 parameters, no annotations, and no output schema, the description is too thin: it covers the action and return shape but omits parameter guidance, async behavior, and any sibling routing. An agent would need to open the schema and guess at operational semantics.

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 78%, so most parameters (audio_url, start_seconds, end_seconds, wait, model, callback_url, prompt) are already documented in the schema, making 3 the baseline. The description adds no parameter detail and does not compensate for the two undocumented fields (timeout_ms, poll_interval_ms).

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?

States a concrete verb and resource ("Create a Suno task on RunAPI") and parenthetically clarifies the operation ("add samples"), which lets an agent distinguish it from siblings like text_to_music or extend_music. However, it never explicitly contrasts itself with a close sibling (e.g. cover_audio), so the differentiation is inferred rather than stated.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives no when-to-use guidance, no prerequisites, and does not name any alternative tool for audio-sample workflows. The required audio_url/start_seconds/end_seconds triad hints at a sample-a-range operation, but the agent is left to infer when this beats cover_audio or remaster_audio.

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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