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Glama

transcribe_file

Converts existing audio or video files, including WhatsApp voice notes and meeting recordings, into text with optional speaker separation for multi-person conversations.

Instructions

Transcribe an existing audio or video file (WhatsApp voice note .opus, .m4a, .mp3, .mp4, meeting recording...), copied into a new recording folder.

speakers: total number of people speaking in the file (above 1, their voices are separated into Speaker 1, Speaker 2...).

Input Schema

NameRequiredDescriptionDefault
pathYes
titleNo
languageNo
speakersNo
wait_secondsNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior3/5

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

No annotations exist, so the description carries the burden; it does disclose that the source is copied into a new recording folder and that speakers above 1 trigger voice separation. However, it says nothing about wait_seconds behavior (blocking vs async), whether transcription is synchronous, or what happens on unsupported formats.

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?

Two short paragraphs, front-loaded with the core action and followed by the one parameter that has non-obvious semantics. No filler sentences, though the parenthetical format list runs slightly long.

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

Completeness3/5

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

An output schema exists so return values needn't be explained, and the file-format hints help. But with 5 parameters at 0% schema coverage, the unexplained wait_seconds (a timing/blocking concern) and language defaults leave real gaps for an agent deciding how to call it.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

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. Only 'speakers' is given real meaning ('total number of people speaking... separated into Speaker 1, Speaker 2'); title, language, and wait_seconds remain entirely unexplained, and 'path' format is only loosely implied by the listed extensions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a clear verb+resource ('Transcribe an existing audio or video file') and enumerates concrete file formats, which distinguishes it from the recording-oriented siblings without naming them. The addition of 'copied into a new recording folder' clarifies the scope is an existing on-disk file rather than a live capture.

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 word 'existing' implicitly points to file input versus the record-then-transcribe flow of transcribe_recording, but no sibling is named and no when-to-use/when-not guidance is given. An agent must infer the routing from the word 'existing' alone.

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