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normalize_project

Measures a project's integrated loudness and adjusts master volume to hit a target LUFS level for streaming, podcast, or broadcast delivery.

Instructions

Measure the project's integrated loudness, then adjust the master volume so the output hits the target LUFS level. Common targets: -14 LUFS (streaming), -16 LUFS (podcasts), -23 LUFS (broadcast).

Input Schema

NameRequiredDescriptionDefault
target_lufsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations, the description carries the full burden: it does disclose the mechanism (measure first, then write the master volume), which is useful. However it does not say whether the existing master volume is overwritten irreversibly, whether automation is respected, what happens with silence or already-normalized material, or whether the operation is offline or transport-dependent.

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?

Two sentences, zero filler: the action sequence is front-loaded and the reference values follow. Every clause earns its place.

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?

There is no output schema, yet the description never says what is returned (measured integrated loudness? the applied gain? the new master value) or what happens on failure, so an agent cannot predict the result shape. For a one-parameter mutation tool this is adequate but leaves the return contract unstated.

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?

Schema coverage is 0% and the single parameter has only a bare default of -14 with no description, so the description must compensate — and it does, explaining that target_lufs is an LUFS loudness level and giving concrete values tied to delivery contexts (-14 streaming, -16 podcast, -23 broadcast). It stops short of stating the default explicitly or allowable range.

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 ('Measure the project's integrated loudness, then adjust the master volume') and thereby implicitly separates itself from analyze_loudness and set_master_volume, though it never names those siblings. An agent can tell what it does, but must infer the boundary with the analysis and master-volume tools on its own.

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 common-targets list implies the post-mix/pre-render workflow, but there is no explicit 'use this instead of X' guidance against analyze_loudness, set_master_volume, apply_mastering_chain, or apply_limiter, several of which overlap in intent. Usage is implied rather than stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.