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remove_silence

Trim silent gaps from video or audio files while keeping tracks in sync. Set silence thresholds and padding to cut dead air and output a cleaner file.

Instructions

Cut silent gaps from a video or audio file while keeping video and audio perfectly in sync.

Args: input_path: Full file path to the source video or audio. output_path: Optional explicit output path. Defaults to '{name}_no_silence.{ext}'. db_threshold: Volume threshold in dB below which sound is considered silence (e.g. -30.0). min_silence_duration: Minimum silence length in seconds to trigger a cut (default: 0.5). padding: Seconds of voice buffer retained before and after speech to avoid clipping (default: 0.1).

Returns: JSON string containing cut statistics, durations, reduction percentage, and output path.

Input Schema

NameRequiredDescriptionDefault
paddingNo
input_pathYes
output_pathNo
db_thresholdNo
min_silence_durationNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses that silence is destructively removed from the output, that sync is preserved, and that output defaults to '{name}_no_silence.{ext}' (implying a new file rather than in-place edit). It omits overwrite behavior when output_path already exists and any failure/error characteristics.

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 purpose sentence is front-loaded and the Args block is well organized and easy to scan; there is minor waste in restating defaults already present in the schema and in a Returns section that partly duplicates the output schema.

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?

For a five-parameter media tool with one required input and an output schema already documenting return values, the description supplies nearly everything needed - parameter meaning, output naming, sync guarantee. It falls short only on overwrite/error behavior and environment prerequisites.

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, and it does: every one of the five parameters is explained with meaning plus a concrete example or default (dB example -30.0, 0.5s minimum silence, 0.1s padding rationale of avoiding clipped speech).

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 opens with a specific verb and resource ('Cut silent gaps from a video or audio file') plus the key quality guarantee ('keeping video and audio perfectly in sync'), which immediately separates it from siblings like smart_crop_vertical and apply_lut_grade.

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?

Usage is implied by the purpose - an agent knows to call this when removing dead air - but there is no explicit guidance on when not to use it, no prerequisites (e.g. ffmpeg availability), and no comparison to the other media-processing siblings.

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