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AudacityMCP

AudacityMCP

by xDarkzx
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Python
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effect_vocal_reduction

Remove or isolate vocals from stereo tracks using adjustable frequency cutoffs and effect strength.

Instructions

Vocal Reduction and Isolation: remove or isolate vocals from a stereo track.

Args: action: 0=Remove Vocals, 1=Isolate Vocals, 2=Remove Vocals (Mono), 3=Isolate Vocals (Mono), 4=Remove Center, 5=Isolate Center. Default: 0 low_cutoff: Low frequency cutoff in Hz. Default: 120 high_cutoff: High frequency cutoff in Hz. Default: 9000 strength: Effect strength (0-50). Default: 1.0

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "effect_vocal_reduction"
  }
}

Input Schema

NameRequiredDescriptionDefault
actionNo
strengthNo
low_cutoffNo
high_cutoffNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.5

TDQS

A3.7/5.0
Behavior3/5

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

The argument documentation reveals meaningful behavior, such as the action modes (Remove/Isolate, Mono/Center) and default low/high cutoffs. With no annotations, the description still omits side-effect information, such as whether the operation destructively modifies audio or only processes a selection, so transparency is only 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 summary line is front-loaded and the parameter block is neatly organized, keeping the description easy to scan. A minor redundancy is that defaults are repeated from the schema, but this does not impair readability.

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

Completeness3/5

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

All four parameters and their defaults are documented, which covers the core calling contract. However, with no output schema and no annotations, the description does not explain return behavior, selection requirements, or the effect's permanence, leaving meaningful gaps for a mutation-style effect tool.

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

Parameters5/5

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

Schema description coverage is 0%, and the description fully compensates by explaining every parameter, including the meaning of each action integer value, cutoff units in Hz, and the strength range 0-50. This adds substantial semantic value beyond the bare schema defaults.

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?

The description opens with a clear verb and object: 'remove or isolate vocals from a stereo track,' making the effect's purpose immediately evident. It does not explicitly contrast itself with related siblings such as raw_karaoke or auto_cleanup_vocal, so it falls short of full sibling differentiation.

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 phrase 'from a stereo track' provides relevant context for when the tool applies, and the action list clarifies the available modes. However, there is no direct guidance on when to choose this effect over sibling tools or any exclusions or alternatives.

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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