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whisper-transcribe-mcp

by ZahiriNatZuke
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

transcribe_file

Convert an audio file to text using local Whisper models or OpenAI, with optional GPT post-processing to correct spelling, grammar, and punctuation.

Instructions

Transcribe an audio file to text.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
languageNoLanguage code (e.g. 'es', 'en', 'fr'). Auto-detected if not provided.
file_pathYesAbsolute path to the audio file (mp3, wav, m4a, ogg, flac, etc.)
model_sizeNoLocal model size: tiny, base, small, medium, large-v3 (plus the .en, turbo, and distil variants). Ignored when using the OpenAI backend. Defaults to the WHISPER_MODEL environment variable (default: 'base').
post_processNoIf True, passes the transcription through GPT to fix spelling, grammar, and punctuation. Requires the openai package.
post_process_promptNoCustom system prompt for post-processing. Use this to provide domain-specific context, proper nouns, or product names that Whisper may have misspelled. Falls back to a generic correction prompt if not provided.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.1.3
    • changedInput schema / properties / model_size / description
      Previous value: -"Local model size: tiny, base, small, medium, large-v3.\n        Ignored when using the OpenAI backend.\n        Defaults to the WHISPER_MODEL environment variable (default: 'base')."New value: +"Local model size: tiny, base, small, medium, large-v3 (plus the .en,\n        turbo, and distil variants). Ignored when using the OpenAI backend.\n        Defaults to the WHISPER_MODEL environment variable (default: 'base')."
  2. First observedv1.1.1

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations, the description carries the full burden and adds nothing behavioral: it doesn't mention the local-vs-OpenAI backend, whether it is a long-running operation, cost, or any side effects. The important behaviors (model_size, post_process requiring openai) exist only in the schema, not the description.

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?

A single front-loaded sentence with zero filler, which is appropriate for a simple call. It leans toward under-specification rather than being overly brief for its own sake.

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?

The output schema and full parameter descriptions cover return values and inputs, so the description needn't explain them. Still, for a tool with backend selection and optional GPT post-processing, the absence of any behavioral framing leaves the definition only minimally complete.

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 every parameter including language, model_size, and post_process_prompt is already documented in the schema with defaults and caveats. The description adds no parameter meaning beyond that, so the baseline 3 applies.

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?

States a specific verb and resource ('Transcribe an audio file to text'), so the agent immediately knows the operation. However, it offers no distinction from the sibling transcribe_base64, which presumably does the same thing from a different input form.

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 indication of when to use this tool versus transcribe_base64 or list_models, and no prerequisites or exclusions are stated. The agent must infer selection purely from the name.

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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