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Ichigo3766

Audio Transcriber MCP Server

by Ichigo3766
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

OpenAI 语音转文本转录 MCP 服务器

使用 OpenAI 的 API 提供音频转录功能的 MCP 服务器。

安装

设置

  1. 克隆存储库：

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

  1. 安装依赖项：

npm install

  1. 构建服务器：

npm run build

  1. 在您的环境变量中设置您的 OpenAI API 密钥。

  2. 将服务器配置添加到您的环境中：

{
  "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
      }
    }
  }
}

将/path/to/audio-transcriber-mcp替换为您克隆存储库的实际路径。

Related MCP server: whisper-telegram-mcp

特征

工具

  • transcribe_audio - 使用 OpenAI 的 API 转录音频文件

    • 将文件路径作为必需参数

    • 可选参数：

      • save_to_file：布尔值，用于将转录保存到文件

      • 语言：ISO-639-1 语言代码（例如“en”、“es”）

执照

此 MCP 服务器采用 MIT 许可证。这意味着您可以自由使用、修改和分发该软件，但须遵守 MIT 许可证的条款和条件。更多详情，请参阅项目仓库中的 LICENSE 文件。

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.

  1. 1 tool updatev1.0.0
    • First observedtranscribe_audio
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

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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