Skip to main content
Glama

download_audio

Retrieve audio from online videos to your downloads, with options for quality and format. Convert to MP3, WAV, or other types for transcription.

Instructions

Download a video's audio track into the downloads dir via yt-dlp.

quality: "best" (default, highest bitrate), "high" (>=128 kbps floor), "standard" (<=128 kbps, smaller), or "low". output_format: "orig" keeps the platform's container; one of mp3/wav/m4a/opus/flac/aac re-encodes with ffmpeg as a postprocess.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes
qualityNobest
output_formatNoorig

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral disclosure burden. It discloses the local download destination, reliance on yt-dlp, ffmpeg re-encoding behavior, and the effect of quality/output_format choices. It does not mention overwrite behavior, network requirements, or failure modes, but the core behavior is clear.

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 compact and well-structured: purpose first, then parameter semantics. There is no filler, and every clause adds practical operational detail.

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?

Combined with the output schema, the definition is largely complete for selecting and calling the tool. The remaining gaps are explicit usage routing and side-effect caveats like file overwriting or network dependency, which prevent a perfect score.

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 coverage is 0%, but the description compensates thoroughly. It explains every quality option with bitrate meaning and every output_format category with container/transcoding behavior. The url parameter is obvious from the first sentence.

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 sentence 'Download a video's audio track into the downloads dir via yt-dlp' names the action, object, destination, and mechanism. It clearly distinguishes this tool from siblings like video_info, fetch_transcript, and transcribe_video.

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?

The use case is implied: an agent should use this when it needs an audio file extracted from a video. However, it never explicitly states when to prefer this over siblings or mentions exclusions or prerequisites.

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