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Loudness and subtitle standards

loudness_and_subtitles
Read-onlyIdempotent

Look up delivery standards for audio and captions. topic=loudness_targets gives integrated loudness (LUFS) and true-peak limits for Spotify, Apple Music, YouTube, Netflix, Prime Video, EBU R128, ATSC A/85, podcasts and social video; subtitle_standards gives characters per line, lines, reading speed and timing rules (Netflix, BBC and others); frame_rates_timecode explains frame rates and drop-frame timecode. Every value sourced. Use it when mixing, exporting or captioning for a platform. Offline data, max 40 rows.

Input Schema

NameRequiredDescriptionDefault
nameNoOptional filter, case-insensitive substring across the row, e.g. Netflix, Spotify, podcast, 29.97.
topicNoWhich table to read.loudness_targets

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • changedInput schema / properties / name / description
      Previous value: -"filter, e.g. Netflix, Spotify, 29.97"New value: +"Optional filter, case-insensitive substring across the row, e.g. Netflix, Spotify, podcast, 29.97."
    • addedInput schema / properties / topic / default
      Added value: +"loudness_targets"
    • addedInput schema / properties / topic / description
      Added value: +"Which table to read."
  2. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so safety is covered. The description adds behavioral context the annotations do not: 'Every value sourced' (provenance) and 'Offline data, max 40 rows' (a result cap), which materially affects how the agent should call and interpret it.

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?

Front-loaded purpose, then topic-by-topic payoff, then a when-to-use clause, then operational limits — each sentence carries distinct information. It is dense and slightly long in the middle enumeration, but nothing is redundant.

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?

For a two-parameter, zero-required lookup with no output schema, the description supplies everything an agent needs: what each topic returns, the filter semantics, provenance, and a row cap. Return-value explanation is present even though no output schema exists to carry it.

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 description coverage is 100%, so the baseline is 3, but the description goes beyond 'Which table to read' by spelling out what each enum value actually yields — LUFS/true-peak targets, characters-per-line and reading-speed rules, frame-rate and drop-frame explanation. That adds real semantic value over the raw enum.

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 ('Look up delivery standards for audio and captions') and enumerates exactly what each topic returns, so an agent knows the domain is audio/caption delivery specs rather than video specs. It never names the nearest sibling (social_video_specs) to draw the boundary explicitly, which keeps it from 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 Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives a clear triggering context: 'Use it when mixing, exporting or captioning for a platform.' That maps the tool to concrete workflows, but there are no exclusions or named alternatives, so an agent facing both this and social_video_specs gets no explicit routing rule.

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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