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Ichigo3766

Audio Transcriber MCP Server

by Ichigo3766
OverviewSchemaSourceRelated ServersScoreDiscussionsAdmin
JavaScript
Remote

OpenAI Speech-to-Text transcriptions MCP Server

A MCP server that provides audio transcription capabilities using OpenAI's API.

Installation

Setup

  1. Clone the repository:

git clone https://github.com/Ichigo3766/audio-transcriber-mcp.git
cd audio-transcriber-mcp

  1. Install dependencies:

npm install

  1. Build the server:

npm run build

  1. Set up your OpenAI API key in your environment variables.

  2. Add the server configuration to your environment:

{
  "mcpServers": {
    "audio-transcriber": {
      "command": "node",
      "args": [
        "/path/to/audio-transcriber-mcp/build/index.js"
      ],
      "env": {
        "OPENAI_API_KEY": "",
        "OPENAI_BASE_URL": "", // Optional
        "OPENAI_MODEL": "" // Optional
      }
    }
  }
}

Replace /path/to/audio-transcriber-mcp with the actual path where you cloned the repository.

Related MCP server: whisper-telegram-mcp

Features

Tools

  • transcribe_audio - Transcribe audio files using OpenAI's API

    • Takes filepath as a required parameter

    • Optional parameters:

      • save_to_file: Boolean to save transcription to a file

      • language: ISO-639-1 language code (e.g., "en", "es")

License

This MCP server is licensed under the MIT License. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the MIT License. For more details, please see the LICENSE file in the project repository.

Available Tools

1 tool
transcribe_audioC

Transcribe an audio file using OpenAI Whisper API

ParametersJSON Schema
NameRequiredDescriptionDefault
filepathYesAbsolute path to the audio file
languageNoLanguage of the audio in ISO-639-1 format (e.g. "en", "es"). Default is "en".
save_to_fileNoWhether to save the transcription to a file next to the audio file

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries full burden. It mentions the API but doesn't disclose key behavioral traits: whether it's read-only or mutative, error handling, rate limits, authentication needs, or what happens with the 'save_to_file' option. The description is minimal and misses critical operational context.

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 a single, efficient sentence with zero waste. It's front-loaded with the core purpose and implementation detail. Every word earns its place, making it easy to parse quickly.

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?

Given no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., transcription text, file path), error conditions, or behavioral details. For a tool with 3 parameters and potential side effects (saving files), more context is needed for effective use.

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 schema fully documents parameters. The description adds no additional meaning beyond implying audio file processing. It doesn't explain parameter interactions or provide examples, so it meets the baseline but doesn't enhance understanding.

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 clearly states the action ('Transcribe') and resource ('an audio file'), specifying the implementation method ('using OpenAI Whisper API'). It's specific enough to understand the core function, though without sibling tools, differentiation isn't applicable. The purpose is unambiguous but could be slightly more detailed about output format.

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 alternatives, prerequisites, or typical use cases. It mentions the API but doesn't explain limitations or ideal scenarios. With no sibling tools, this is less critical, but still lacks context for effective agent decision-making.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 1 tool updatev1.0.0
    • First observedtranscribe_audio
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TDQS

B3.1/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined and singular, making it impossible for an agent to misselect between non-existent alternatives.

Naming Consistency5/5

The single tool name follows a clear verb_noun pattern (transcribe_audio), and with only one tool, there is no inconsistency to evaluate. The naming is straightforward and aligns with common conventions.

Tool Count2/5

A single tool is too few for a server named 'Audio Transcriber MCP Server', as it suggests a limited scope that may not cover related operations like transcription status checks, file management, or configuration. This minimal set could hinder agent workflows that require more comprehensive functionality.

Completeness2/5

The tool set is severely incomplete for audio transcription tasks. While it provides a core transcription function, it lacks essential operations such as listing available transcriptions, retrieving transcription results, handling errors, or managing audio files, which are typical in such domains and could lead to agent failures.

Maintenance

ActivityInactive
ResponsivenessResponsive

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