Skip to main content
Glama

audio_loudness

Measure integrated loudness (LUFS), true peak, and a per-window loudness curve for an asset or rendered file so you can check levels against YouTube's ~-14 LUFS normalization.

Instructions

Integrated loudness (LUFS), true peak, and a per-window loudness curve for an asset or a rendered file. YouTube normalises to about -14 LUFS.

Input Schema

NameRequiredDescriptionDefault
windowNo
projectYes
asset_idNo
render_pathNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.1/5.0
Behavior2/5

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

No annotations are supplied, so the description carries the full behavioral burden. It discloses the return content (LUFS, true peak, curve), which is genuinely useful with no output schema, but says nothing about whether this is a pure read, whether it decodes arbitrarily long media, whether asset_id and render_path are mutually exclusive, or what happens if neither is given.

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, front-loaded with the returned measurements, followed by one actionable calibration fact. No filler and nothing repeated from the schema or title.

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?

For a four-parameter tool with no annotations and no output schema, the description covers the outputs adequately but leaves the input contract and the read-only/performance profile unaddressed. An agent could call it, but would be guessing about window units and asset_id/render_path precedence.

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 0% and parameters carry only titles. The phrase 'an asset or a rendered file' hints at asset_id vs render_path, but gives no conflict resolution, and 'per-window loudness curve' only vaguely gestures at the window parameter without stating units or meaning. project is entirely unexplained.

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?

Names a specific measurement resource (integrated loudness in LUFS, true peak, per-window curve) applied to either an asset or a rendered file. That is a precise verb-less but unambiguous analysis purpose, and it is separable from audio_waveform and detect_silence by what it returns. It stops short of explicitly framing itself as a read-only measurement pass, so not a 5.

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 YouTube -14 LUFS sentence implies a use case (checking whether a master will be normalised down), which is real guidance, but usage is implied rather than stated. There is no explicit when-to-use versus audio_waveform or detect_silence, and no exclusions or prerequisites.

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