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AbletonMCP

AbletonMCP

by juampicosta
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

check_gain_staging

Analyzes all tracks to identify gain staging issues. Recommends adjustments for optimal headroom, targeting -18 to -12 dBFS RMS per track and master peak below -6 dBFS.

Instructions

Check gain staging across all tracks. Identifies tracks that are too hot or too quiet, and gives recommendations for optimal headroom. Target: individual tracks at -18 to -12 dBFS RMS, master bus peak below -6 dBFS.

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": "check_gain_staging"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description transparently explains the tool's read-only behavior: it checks levels, identifies issues, and recommends targets. It mentions no side effects, and the action is clearly an analysis. The disclosed target levels add behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences, each serving a purpose: stating the action, explaining the output, and providing specific targets. No redundant or vague language.

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

Completeness5/5

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

Given the tool has no parameters and an output schema exists, the description adequately covers what the tool does and its expected output. The simple nature of the tool doesn't require additional context.

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

Parameters4/5

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

The tool has zero parameters, so according to the rules, baseline is 4. The description adds no parameter-specific information, nor is it needed.

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 checks gain staging across all tracks, identifies issues, and provides recommendations with specific dBFS targets. It distinguishes itself from sibling tools like analyze_track or analyze_mix by focusing on gain staging and headroom optimization.

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 implies usage for gain staging analysis but lacks explicit guidance on when to use this tool versus alternatives such as mixing_suggestions_for_track or analyze_mix. No when-not or context is provided.

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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