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@runapi.ai/elevenlabs-mcp

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TypeScript
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isolate_audio

Create an ElevenLabs audio isolation task on RunAPI from a source audio URL. Returns task ID, status, and output URLs for separating vocals or background audio.

Instructions

Create a ElevenLabs task on RunAPI (isolate audio). 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.
timeout_msNo
callback_urlNoDeclared type: string.
poll_interval_msNo
source_audio_urlYesDeclared type: string.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed6 schema fields changedv0.2.0
    • changedInput schema / additionalProperties
      Previous value: -falseNew value: +{}
    • addedInput schema / properties / callback_url / description
      Added value: +"Declared type: string."
    • removedInput schema / properties / model / enum
      Removed value: -[
-  "audio-isolation"
-]
    • addedInput schema / properties / poll_interval_ms / maximum
      Added value: +9007199254740991
    • addedInput schema / properties / source_audio_url / description
      Added value: +"Declared type: string."
    • addedInput schema / properties / timeout_ms / maximum
      Added value: +9007199254740991
  2. Changed3 schema fields changedv0.1.7
    • addedInput schema / properties / callback_url
      Added value: +{
+  "type": "string"
+}
    • addedInput schema / properties / source_audio_url / type
      Added value: +"string"
    • addedInput schema / required
      Added value: +[
+  "source_audio_url"
+]
  3. First observedv0.1.0

TDQS

C2.9/5.0
Behavior3/5

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

With no annotations, the description carries the full burden, and it does add useful context by disclosing the return shape (task id, status, output URLs) even though no output schema exists. It omits auth requirements, cost implications, provider throttling, and whether polling is synchronous by default (that detail lives only in the schema).

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 tight, front-loaded sentences with no filler; the operation and return values come first. Brevity comes at the cost of the details the other dimensions penalize, but structurally it is clean.

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?

For a 6-parameter async task tool with no annotations and no output schema, the description leaves the operational workflow (wait/poll behavior, get_task relationship, callback usage, timeouts) unexplained. It only covers purpose and return fields.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 67%, with several parameters documented only as placeholder prose ('Declared type: string'). The description contributes nothing about source_audio_url format, model slug selection, timeout, or callback semantics, so it fails to compensate for the coverage gap.

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 specific verb and resource ('Create a ElevenLabs task... isolate audio') and clarifies the async task-creation nature, which separates it from synchronous siblings like text_to_speech. It does not, however, distinguish itself from other task-creating model tools (text_to_sound, text_to_dialogue) beyond the parenthetical 'isolate audio'.

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?

No guidance on when to use this versus the sibling audio/model tools, and no mention of the natural follow-up relationship with get_task for polling a created task. The agent must infer all routing from the name.

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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