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Glama

transcribe

Transcribe video or audio URLs and local files into text, prioritizing existing subtitles and falling back to Whisper speech recognition.

Instructions

Transcribe a video URL or a local audio/video file. Subtitles-first: existing captions via yt-dlp need no API key; otherwise Whisper (Groq/OpenAI if configured, else keyless local faster-whisper). Use this when the user wants spoken words, not the page. Do not use read_url for a transcript. prefer_subtitles defaults true; set false to force audio. cookies_from_browser is opt-in (chrome/firefox/…) and uses THIS machine's browser cookies; never used by read_url. Returns the transcript text, or an 'Error: ...' string on failure.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sourceYes
providerNoauto
prefer_subtitlesNo
cookies_from_browserNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.10.1

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations, the description carries the full behavioral burden and does so thoroughly. It discloses the subtitles-first strategy, fallback to Whisper with an API-key-free local option, that cookies_from_browser uses the machine's own browser cookies, and the exact return shape ('transcript text, or an 'Error: ...' string on failure'). This is highly transparent about dependencies and failure modes.

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 front-loaded with the main action. Each sentence adds distinct value: scope, subtitles strategy, usage decision, parameter defaults, and error behavior. There is no filler or repetition.

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?

Given the absence of annotations and low schema coverage, the description is remarkably complete: it covers inputs, processing strategy, parameter behavior, error output, and distinguishes itself from siblings. The only shortfall is the provider parameter detail, but it is a minor gap in an otherwise fully usable definition.

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 0%, so the description must compensate. It explains source (video URL or local audio/video file), prefer_subtitles ('defaults true; set false to force audio'), and cookies_from_browser (opt-in, browser cookie source). However, 'provider' is not explicitly defined: the description mentions Groq/OpenAI/faster-whisper but does not enumerate valid values or explain what 'auto' selects, leaving a clear gap.

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 clear verb ('Transcribe') and resource ('a video URL or a local audio/video file'), and differentiates itself from siblings by explicitly stating 'Do not use read_url for a transcript' and 'when the user wants spoken words, not the page.' This makes the tool's purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives explicit when-to-use guidance ('when the user wants spoken words, not the page'), excludes a specific alternative ('Do not use read_url for a transcript'), and provides conditional logic for choosing subtitles vs Whisper. The note about cookies_from_browser being opt-in and never used by read_url further clarifies boundaries.

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