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Transcribe Audio

transcribe_audio
Read-onlyIdempotent

Transcribe audio to text with word-level timestamps.

Converts spoken English audio into text with optional word-level timestamps and per-word confidence scores.

Args: audio_base64: Base64-encoded audio data (WAV, MP3, OGG, FLAC, WebM). audio_format: Audio format hint. Auto-detected from magic bytes if omitted. include_timestamps: Whether to include word-level timing (default: true).

Returns: dict with keys: - text (str): Full decoded transcript - words (list): Per-word results with timestamps, each containing: - word (str): The transcribed word - start (float): Start time in seconds - end (float): End time in seconds - confidence (float 0-1): Word-level confidence - audioDurationMs (int): Audio duration in milliseconds - metadata (dict): Processing time, audio length, model version - audioQuality (dict): Audio metrics (SNR, peak/RMS dB, etc.)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
audio_base64YesBase64-encoded audio data. Supports WAV, MP3, OGG, FLAC, and WebM formats.
audio_formatNoAudio format hint — 'wav', 'mp3', 'ogg', 'flac', 'webm'. Auto-detected if omitted.
include_timestampsNoIf true, include word-level start/end times and confidence.

TDQS

A3.5/5.0
Behavior4/5

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

Annotations already declare read-only, idempotent, and non-destructive behavior, so the description is not required to restate safety. It adds useful behavioral detail: optional word-level timestamps, confidence scores, audio format auto-detection via magic bytes, and a detailed return dict including metadata and audio quality. It does not discuss failure cases or size limits, but the return contract is unusually explicit.

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?

The description is front-loaded with a concise one-sentence summary followed by structured Args and Returns sections. It is easy to scan and information-dense. There is some redundancy between the first two sentences, but overall the structure is effective.

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?

With no output schema, the description correctly carries the burden of explaining return values, and it does so thoroughly: text, word-level details, duration, metadata, and audio quality. Combined with full schema coverage and safety annotations, an agent has enough to invoke the tool correctly. It could more explicitly state that only English is supported and clarify when to choose transcribe_audio_pro, but these are limited gaps.

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 description coverage is 100%, so the baseline is 3; the schema already documents all three parameters. The description's Args section mostly repeats the schema, adding only 'magic bytes' and tying in per-word confidence scores. This is marginal added value beyond the structured parameter definitions.

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?

The description states a specific verb and resource: 'Transcribe audio to text with word-level timestamps.' It also clarifies it handles spoken English audio with optional per-word confidence scores. However, it does not distinguish itself from the sibling transcribe_audio_pro, so an agent would need to infer which transcription tool is appropriate.

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

Usage Guidelines2/5

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

There is no explicit when-to-use or when-not-to-use guidance, and no alternatives such as transcribe_audio_pro or check_whisper_service are mentioned. The only implied usage is 'transcribe audio,' but no selection criteria or exclusions are provided relative to the sibling tools.

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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TDQS

A3.8/5.0
Disambiguation4/5

Most tools map to clearly distinct actions, but transcribe_audio and transcribe_audio_pro are near-duplicates and the four check_*_service tools require careful reading. The detailed descriptions make the distinctions recoverable, so confusion should be rare.

Naming Consistency4/5

The set overwhelmingly follows a snake_case verb_* pattern, and the voice_id_* family is internally consistent. Minor deviations like get_phoneme_inventory vs list_tts_voices and the _pro suffix on transcribe_audio_pro keep it from being perfect.

Tool Count4/5

Fourteen tools is within a reasonable range, but the server bundles pronunciation, STT, TTS, voice ID, and four health checks, making it feel broader than the 'Pronunciation' name suggests. A few health checks could be consolidated, but nothing is excessive.

Completeness3/5

Core pronunciation, transcription, and synthesis workflows are present, but the voice ID portion has create/list/verify/identify with no way to delete or unenroll a speaker. That is a notable lifecycle gap in an otherwise fairly complete speech toolkit.

Resources