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voice-io

voice-io

by Furkiozknn
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

speech_to_text

Transcribe local audio files into written text using automatic speech recognition. Supports common formats up to 25MB, accepts an optional language hint, and falls back to a local model when no API key is set.

Instructions

Transcribe a local audio file to text.

Tries Groq's whisper-large-v3-turbo first (free tier, no credit card - requires GROQ_API_KEY), falling back to a fully local, keyless model (faster-whisper, MIT) if Groq is unavailable or the key isn't set. The local fallback requires the optional local-stt extra (uv sync --extra local-stt) and auto-downloads its model weights on first use.

Args: audio_path: Absolute path to a local audio file (mp3/wav/m4a/flac/ ogg/webm/mp4/mpeg/mpga), max 25MB. language: Optional ISO-639-1 language hint (e.g. "en"). Ignored by the local fallback, which auto-detects language.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "audio_path": "<audio_path>"
    },
    "name": "speech_to_text"
  }
}

Input Schema

NameRequiredDescriptionDefault
languageNo
audio_pathYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.6/5.0
Behavior4/5

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

With no annotations, the description carries the full transparency burden. It discloses the Groq-first fallback, the GROQ_API_KEY requirement, the optional local-stt extra, first-use model weight download, and that the local fallback ignores language hints. It does not mention error handling or network assumptions, but the core operational behavior is well covered.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately concise and front-loaded with the main purpose, followed by a compact Args block. Every sentence adds useful operational or parameter detail; there is no filler.

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?

The tool has a fallback architecture and conditional setup requirements, and the description covers all of these: provider order, fallback trigger, setup extra, model weight download, and input constraints. Since an output schema exists, the absence of an explicit return-value description is acceptable, and an agent has enough information to invoke the tool correctly.

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?

The schema provides no descriptions for parameters, so the description must compensate. It fully explains audio_path as an absolute path with supported formats and max size, and language as an optional ISO-639-1 hint with example and fallback-specific 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?

The description states a specific action and resource: 'Transcribe a local audio file to text.' This is unambiguous and clearly different from sibling tools like text_to_speech, list_voices, and check_provider_health.

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?

The description gives clear usage context: it is for local audio files, lists supported formats and the 25MB limit, and explains the optional language hint. It does not explicitly name sibling alternatives or state 'when not to use,' but the transcription versus speech-synthesis distinction is obvious from the sibling names.

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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