Skip to main content
Glama

check_loudness

Verify media file loudness compliance with broadcast standards (EBU R128, ATSC A/85, BS 1770). Get pass/fail verdict, measured deltas, and exact gain corrections.

Instructions

Loudness compliance verdict for a media file against a formal published standard. Measures integrated loudness, loudness range, and true peak (via ffmpeg), then evaluates them against the named standard's targets and tolerances.

Args: path: media file to check (any format ffmpeg can read). standard: EBU_R128 (broadcast, -23 LUFS), ATSC_A85 (US TV, -24 LKFS), or BS_1770 (measurement only — verdict "measured", no gates). stream: zero-based audio stream index for multi-track files. detailed: also report max momentary / max short-term loudness (one extra ffmpeg pass).

Returns: dict with: verdict (pass|fail|measured), per-metric measured/target/delta and pass booleans, failures (plain-English causes), remediation (exact corrections, e.g. "apply -2.3 LU gain"), and measurement_context (ffmpeg version, stream info).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYes
streamNo
detailedNo
standardNoEBU_R128
Behavior5/5

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

With no annotations, the description fully bears behavioral transparency. It discloses the use of ffmpeg, the three metrics, the effect of the 'detailed' flag (extra ffmpeg pass), and the 'standard' parameter's behavior (e.g., BS_1770 yields 'measured' verdict). It also describes the return dict including failures and remediation.

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?

The description is somewhat long but well-structured with a summary and Args block. It front-loads the purpose and every sentence adds value. Minor redundancy in the return description could be trimmed, but overall effective.

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?

The tool has 4 parameters and no output schema, but the description fully covers parameter semantics and provides a detailed return value description (dict with verdict, metrics, failures, remediation, context). It is complete for a compliance checking tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate, which it does excellently. It explains each parameter: 'path' is any format ffmpeg can read, 'standard' has detailed values with explanations, 'stream' is zero-based index, and 'detailed' is boolean for extra pass.

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 clearly states 'Loudness compliance verdict for a media file against a formal published standard' with specific metrics (integrated loudness, loudness range, true peak) and distinguishes from sibling tool 'list_standards' which likely lists standards.

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?

The description implicitly gives usage context by listing standards with their applications (e.g., 'EBU_R128 (broadcast, -23 LUFS)'), but does not explicitly state when to use this tool versus alternatives like 'list_standards'.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/chaoz23/loudcheck'

If you have feedback or need assistance with the MCP directory API, please join our Discord server