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Glama

fablecut_denoise

Reduce background noise such as hiss, hum, and room tone on audio clips with ffmpeg's FFT denoiser, then switch clips and linked stems to the cleaned FLAC.

Instructions

Reduce background noise (hiss, hum, room tone) on audio clips: ffmpeg's FFT denoiser renders a cleaned FLAC of each clip's whole source file into media/, and the clip — with its linked stems — switches to it (the picture keeps its own file, so links and timing stay intact). The same as the inspector's Noise control. amount 'off' switches back to the original. Re-running reuses a file already rendered. Needs ffmpeg + ffprobe on PATH. Refuses locked clips unless force:true.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
forceNoAlso change clips the user locked (only when they asked)
amountNoHow hard to pull the noise down (default medium); off restores the original audio
clipIdsYesAudio clips, or video clips with linked audio stems

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.10.0

TDQS

A4.4/5.0
Behavior5/5

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

No annotations are provided, so the description carries the full burden and does so well: discloses that it renders a new FLAC into media/, that linked stems switch while the picture file stays (preserving links/timing), that re-runs are idempotent (reuses rendered file), the ffmpeg+ffprobe PATH prerequisite, and locked-clip refusal behavior. These are exactly the side effects an agent must know before invoking.

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?

Dense but front-loaded: purpose first, then mechanism, revert, idempotency, prerequisites, and force. Every sentence earns its place, though the parenthetical about picture files is slightly nested.

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 mutation tool with no annotations and no output schema, the description covers purpose, side effects, file layout, idempotency, prerequisites, and refusal semantics – more than enough for correct invocation.

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

Parameters3/5

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

Schema coverage is 100% – all three params are documented including the enum, defaults, and semantics of 'off'. The description adds the 'off restores original' behavior and force rationale, but the schema already carries most of this, so baseline 3 is appropriate.

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?

States a specific verb and resource ('Reduce background noise... on audio clips') and names concrete noise types (hiss, hum, room tone). Distinguishes itself from siblings like fablecut_normalize_audio and fablecut_auto_duck by identifying the exact mechanism (FFT denoiser / Noise control), so an agent can route correctly without opening a schema.

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?

Explains when to use and what state changes occur, and covers the 'off' revert and force/locked-clip condition. Lacks explicit contrast with sibling audio tools (normalize_audio, auto_duck), which is the only gap.

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