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Patience-dot-devl

voice-analysis-mcp

transcribe

Convert speech from audio files to timestamped text with detected language and words-per-minute. Supports per-channel transcription for stereo call recordings and optional word-level timestamps.

Instructions

Transcribe speech to timestamped text using a local Whisper model (no API key).

Returns segments with start/end times plus detected language and overall
words-per-minute. On stereo call recordings, pass channel=0 or channel=1 to
transcribe one speaker at a time and interleave the results by timestamp for
an accurate who-said-what transcript. model_size: tiny/base/small/medium/
large-v3/large-v3-turbo — larger is more accurate but slower; the model is
downloaded on first use. word_timestamps=True adds per-word timing (useful
for locating exact moments, at some cost in output size).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYes
channelNo
end_timeNo
languageNo
model_sizeNobase
start_timeNo
word_timestampsNo
Behavior5/5

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

Discloses key behavioral traits: local model (no API key), return structure (segments with times, language, WPM), first-use model download, and channel behavior. Fully compensates for missing annotations.

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?

Front-loaded with purpose and key info, followed by detailed parameter guidance. Slightly dense but efficient; minimal redundancy.

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?

Covers main functionality and crucial usage details, but omits descriptions for start_time, end_time, and language parameters. Without an output schema, the return structure is only partially described (segments, language, WPM).

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?

Explains channel, model_size, and word_timestamps well, adding meaning beyond the schema. However, start_time, end_time, and language parameters are not described, and path is only implied. Given 0% schema coverage, more completeness is needed.

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 clearly states the tool transcribes speech to timestamped text using a local Whisper model, distinguishing it from sibling tools like analyze_conversation or get_audio_info.

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

Usage Guidelines4/5

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

Provides explicit guidance on handling stereo recordings by selecting a channel and interleaving results. Also explains model size trade-offs and word_timestamps usage, but lacks explicit when-not-to-use or alternative 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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