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shell-as-mcp

by meomeo-dev

ffmpeg__process_audio_for_stt

Convert audio or video input into a Whisper/STT-ready audio file by normalizing sample rate, channels, and removing silence for effective speech transcription.

Instructions

Build a Whisper/STT-ready compact audio output from either audio or video input. @remarks Returns a JSON summary in stdout with output_path and normalization parameters for LLM-friendly parsing. @param input_path Source media path (video or audio). @param output_path Destination audio path. @param start_time Optional start timestamp (for example 00:04:30). @param end_time Optional end timestamp (for example 00:05:30). @param sample_rate Output sample rate in Hz, default 16000. @param channels Output channels count, default 1. @param remove_silence Whether to remove long silent regions, default true. @param audio_format Output container format, default mp3. @param __mcp_response_mode Optional response mode: content (default) or structuredContent.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
channelsNo
end_timeNo
input_pathYes
start_timeNo
output_pathYes
sample_rateNo
audio_formatNo
remove_silenceNo
__mcp_response_modeNocontent
Behavior4/5

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

With no annotations provided, the description carries the full burden. It discloses that the tool returns a JSON summary in stdout with output_path and normalization parameters, and it mentions defaults and the compact audio output. This goes beyond the schema and gives the agent a clear picture of behavior, though it does not cover every possible side effect like overwrite policies.

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?

The description is well-structured: a one-sentence summary, followed by @remarks and @param lines. Every line adds value, and the most important information is front-loaded. Despite the length, it is concise because no words are wasted.

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 description is complete for a complex tool with 9 parameters and no output schema or annotations. It covers the purpose, return format, parameter defaults, and input/output paths. It even explains the optional response mode, leaving little ambiguity for an agent.

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 fully compensates. It provides @param lines for every parameter with examples (e.g., '00:04:30'), defaults, and value meanings (e.g., sample_rate default 16000, remove_silence default true). This adds substantial meaning that the schema lacks.

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 phrase: 'Build a Whisper/STT-ready compact audio output from either audio or video input.' This clearly states what the tool does and its scope, distinguishing it from sibling tools like ffmpeg__normalize_audio or ffmpeg__mix_audio_tracks.

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 implies its use case via 'Whisper/STT-ready', which is clear guidance for when to select this tool over generic audio processing. However, it does not explicitly mention when not to use it or name alternatives, so it stops short of full exclusion criteria.

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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