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waveform-MCP

waveform-MCP

by jarmstrong158
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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audio_loudness_lufs

Measure audio loudness metrics including integrated LUFS, true peak, and dynamic range. Compare against Spotify, Apple, and broadcast targets to ensure true peak stays under -1.0 dBTP.

Instructions

Measure integrated LUFS, true peak (dBTP), and dynamic range (LRA) for an audio file using ffmpeg's loudnorm filter. Reference targets: Spotify -14 LUFS, Apple -16, broadcast -23. True peak should stay under -1.0 dBTP.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "file_path": "<file_path>"
    },
    "name": "audio_loudness_lufs"
  }
}

Input Schema

NameRequiredDescriptionDefault
file_pathYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

There are no annotations, so the description carries the burden of behavioral disclosure. It reasonably indicates the operation is analytical via 'Measure' and reveals the ffmpeg/loudnorm implementation and a true-peak ceiling. It does not, however, state whether the file is modified, describe return shape, or mention any failure modes or side effects.

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?

Three short sentences, front-loaded with the core action and output metrics, followed by useful reference targets. There is no filler or redundant restatement of the tool name.

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 single-parameter read-only measurement tool with no output schema, the description covers the essential metrics and practical targets. It is still somewhat thin on return format, path expectations, and how this compares to related analysis tools, so an agent may need to guess at invocation details.

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 coverage is 0% and the only parameter, file_path, is described only by name and type. The description loosely ties it to 'an audio file' but does not specify path semantics, supported formats, or whether URLs/local paths are accepted, forcing the agent to infer these details.

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'), a clear resource ('an audio file'), and the exact metrics returned (integrated LUFS, true peak/dBTP, dynamic range/LRA). It also names the underlying method (ffmpeg loudnorm filter), making it easy to distinguish from siblings like audio_detect_tempo or audio_spectrum.

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 reference targets (Spotify -14, Apple -16, broadcast -23) clearly imply usage in loudness assessment and streaming/broadcast preparation. However, it does not explicitly state when not to use it or mention any alternative tools for loudness or listening-station tasks.

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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