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transcribe_to_file

Convert audio to text and export as subtitle or text files using Whisper models. Specify output format (SRT, VTT, TXT) and language for accurate transcription.

Instructions

[EXPERIMENTAL] Transcribe audio and export to a subtitle or text file. Requires separate setup — see installation guide.

ALWAYS tell the user where the file will be saved BEFORE starting. If user doesn't specify a path, call get_default_export_folder to get a real path. NEVER guess paths like /home/user/... — always use absolute Windows paths like C:\Users\Name\Documents\transcript.srt

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

Args: path: Absolute path for the output file (e.g. "C:/Users/You/Documents/transcript.srt") format: Output format - "srt", "vtt", or "txt" model_size: Whisper model - "tiny", "base", "small", "medium", "large-v3" language: ISO language code or None for auto-detect

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYes
formatNosrt
model_sizeNosmall
languageNo
Behavior5/5

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

No annotations provided, but description fully compensates by disclosing: [EXPERIMENTAL] status, background execution model ('returns a job_id immediately'), external setup dependency, and file system side effects. No contradictions with implicit safe behavior.

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?

Information-dense but well-structured with clear sections (status, purpose, prerequisites, workflow rules, Args). No wasted sentences, though Args formatting is compact. Appropriate length given zero schema documentation and complex async behavior.

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?

Comprehensive coverage for a complex background job tool: references sibling tools (get_default_export_folder, check_transcription_status), explains return value (job_id) despite missing output schema, covers platform constraints, experimental warnings, and setup requirements.

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 with detailed Args section documenting all 4 parameters: path (with platform-specific example), format (valid values listed), model_size (Whisper context + valid values), and language (ISO format + auto-detect behavior).

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?

Clear specific verb ('Transcribe audio') plus output destination ('subtitle or text file'). Effectively distinguishes from sibling tools like transcribe_to_labels and transcribe_audio by emphasizing file export functionality.

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?

Extensive workflow guidance including: prerequisite setup requirement, mandatory user notification about save location, explicit instruction to use get_default_export_folder for paths, platform-specific path constraints (Windows absolute paths), and post-invocation monitoring via check_transcription_status.

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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