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ZahiriNatZuke

whisper-transcribe-mcp

transcribe_file

Transcribes audio files (mp3, wav, m4a, etc.) to text with optional language auto-detection and GPT post-processing for grammar and spelling correction.

Instructions

Transcribe an audio file to text.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
file_pathYesAbsolute path to the audio file (mp3, wav, m4a, ogg, flac, etc.)
languageNoLanguage code (e.g. 'es', 'en', 'fr'). Auto-detected if not provided.
model_sizeNoLocal model size: tiny, base, small, medium, large-v3. 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

Behavior2/5

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

The description does not disclose any behavioral traits beyond the basic action. It does not mention if the tool requires network access, writes temporary files, uses local or remote processing, or any error conditions. Since no annotations are provided, the description carries the full burden but fails to provide meaningful behavioral context.

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 a single, concise sentence that is immediately clear. It is appropriately front-loaded but could benefit from minimal structured details (e.g., supported formats) without becoming verbose. The extreme brevity does not hurt clarity but leaves some gaps.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite the presence of an output schema and full parameter documentation, the description fails to provide context about when to use this tool vs. the sibling 'transcribe_base64', or about any side effects or limitations. For a tool with five parameters and multiple options, the description is insufficiently 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?

The input schema has 100% description coverage, so the schema already documents all five parameters thoroughly. The tool description adds no additional semantic value beyond what is in the schema, resulting in a baseline score of 3.

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 action ('transcribe') and the resource ('audio file'), immediately distinguishing the tool from its siblings 'list_models' (which lists models) and 'transcribe_base64' (which transcribes base64-encoded audio). The verb+resource combination is specific and unambiguous.

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?

The description provides no guidance on when to use this tool versus its siblings or alternatives. It does not mention file size limits, audio duration constraints, or any prerequisites. Users receive no contextual advice on selecting this tool over transcribe_base64 or list_models.

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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