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remove_filler_words

Remove filler words like 'um' and 'uh' from Final Cut Pro timelines using audio transcripts. Non-destructive editing creates a cleaned copy of the project.

Instructions

Cut filler words (um, uh, erm...) out of the timeline with ripple, using word-level transcripts of the real source audio. Conservative default filler list — words like 'like' and 'so' are only cut if you pass them explicitly. Uses each media file's _transcript.json (auto-transcribes if missing). Non-destructive: writes a _defillered copy.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoWhisper model size if transcription is neededbase
fillersNoFiller words/phrases to cut (default: um, uh, uhh, umm, erm, ehm, mmm, hmm, mhm)
paddingNoSeconds to widen each cut on both sides (0-2, default 0.02)
filepathYesPath to FCPXML file
clip_nameNoOnly clean the clip with this name
output_pathNoOutput path (default: adds _defillered suffix)
Behavior4/5

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

With no annotations, the description covers key behaviors: uses real source audio transcripts, auto-transcribes if missing, non-destructive with copy, ripple editing, and conservative default list. Lacks performance or side-effect details.

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?

Four sentences, front-loaded with purpose, no wasted words. Each sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers how it works, defaults, and output behavior. No output schema, but explains the resulting copy. Missing details on return value or error cases, but sufficient for a modification tool.

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 coverage is 100%, but description adds meaning to the fillers parameter (conservative default, explicit passing for 'like'/'so') and padding (seconds to widen). No extra detail for other parameters.

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 the tool removes filler words from the timeline using word-level transcripts, distinguishing it from siblings like remove_media_silence and transcribe_media.

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?

Provides context about conservative default list and non-destructive nature, but lacks explicit guidance on when to use this tool versus alternatives like remove_media_silence or remove_silence_candidates.

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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