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transcribe_selection

Transcribe selected audio regions in Audacity using Whisper AI models. Get text from specific audio segments for transcription or translation tasks.

Instructions

[EXPERIMENTAL] Transcribe only the currently selected audio region. Requires separate setup — see installation guide.

Runs in BACKGROUND — returns a job_id immediately. Use check_transcription_status to monitor progress.

Select a region first, then call this tool.

Args: model_size: Whisper model - "tiny", "base", "small", "medium", "large-v3" language: ISO language code or None for auto-detect task: "transcribe" or "translate"

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
model_sizeNosmall
languageNo
taskNotranscribe
Behavior4/5

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

With no annotations, description carries full burden and discloses critical behavioral traits: experimental status, background execution ('returns a job_id immediately'), and state dependency (requires pre-selection). Minor gap: does not specify failure modes if no region is selected or setup is incomplete.

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?

Well-structured with front-loaded experimental warning and purpose. Information hierarchy is logical: purpose → prerequisites → behavior → workflow → parameters. Minor formatting quirk with 'Args:' section but no wasted sentences.

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?

Despite no output schema and 0% schema coverage, description explains the return value ('returns a job_id') and full async workflow. Covers experimental nature and setup requirements. Could improve by mentioning error states or timeout behavior, but adequate for tool complexity.

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 has 0% description coverage, but description fully compensates by documenting all 3 parameters with valid values (model_size options listed, task enum values, language format 'ISO language code') and default behaviors ('None for auto-detect').

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?

Description opens with specific verb 'Transcribe' and precise scope 'only the currently selected audio region', clearly distinguishing it from sibling tools like transcribe_audio (full audio) and transcribe_to_file/to_labels (different outputs). The [EXPERIMENTAL] tag adds important context.

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?

Explicitly states prerequisite 'Select a region first, then call this tool' and external requirement 'Requires separate setup'. Explicitly names sibling check_transcription_status as the monitoring alternative for the async workflow, providing clear when-to-use guidance for the full lifecycle.

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

Install Server

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