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Glama

measure_loudness

Calculates dialogue loudness (integrated LUFS and true peak) from storyline clips with clip volume applied, before Final Cut's Loudness enhancement, to help check reels near -14 LUFS.

Instructions

Measure the edit's dialogue loudness (integrated LUFS, true peak) from the storyline clips with their clip volume applied. Final Cut's Loudness enhancement isn't simulated, so treat it as the level BEFORE that effect. Reels usually sit around -14 LUFS.

Input Schema

NameRequiredDescriptionDefault
endNo
startNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior4/5

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 disclose the most important behavioral caveat: the measurement excludes Final Cut's Loudness enhancement, so the number reflects the pre-effect level. It does not explicitly confirm the operation is read-only or say what happens when the edit contains no storyline clips, which leaves minor gaps.

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?

Three short sentences, front-loaded with what is measured and what is returned before the caveat and the benchmark. Nearly every clause earns its place; only the phrasing 'so treat it as' is slightly loose.

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?

It usefully compensates for the missing output schema by naming the return values (LUFS, true peak) and for the missing annotations by disclosing the enhancement caveat. It is still incomplete on the two undocumented parameters and on the read-only nature of the call, which matters for a tool with no annotations.

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?

Two parameters exist with 0% schema description coverage, and the description never mentions them. It says nothing about how start and end scope the measurement window, so an agent must guess whether they are timestamps, seconds, or frame positions, and whether omitting them measures the whole edit.

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 names a specific verb (measure) and resource (the edit's dialogue loudness) and specifies exactly which metrics are returned (integrated LUFS and true peak). It also pins the measurement scope to storyline clips with clip volume applied, which distinguishes it from level-changing siblings such as normalize_audio or set_volume.

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?

It gives interpretive context ('treat it as the level BEFORE that effect') and a useful benchmark ('Reels usually sit around -14 LUFS'), which helps an agent judge results. However, it never states when to choose this over normalize_audio, nor any exclusions or prerequisites, leaving the selection decision implicit.

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