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uraoz

Bouyomi-chan MCP Server

by uraoz
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막대기 읽기 MCP 서버 (Node.js 버전)

Model Context Protocol(MCP)을 사용하여 막대 읽기(천천히 음성)에 의한 음성 독서 기능을 AI 어시스턴트에 제공하는 서버입니다.

개요

이 서버는 Claude와 같은 AI 어시스턴트에서 막대기 읽기를 사용할 수있는 MCP 서버입니다.

Related MCP server: MCP Simple AivisSpeech

기능

  • 텍스트 읽기

  • 음성 타입 선택(여성·남성 등)

  • 볼륨 조절

  • 읽기 속도 조정

  • 음정 조정

전제 조건

  • Node.js 16 이상

  • npm 7 이상

  • 막대기 읽기가 설치된 것

  • 막대기 읽기의 HTTP 연결이 포트 50080에서 시작되었습니다.

설치 방법

  1. 이 저장소를 복제합니다.

git clone https://github.com/uraoz/bouyomichan-mcp-nodejs.git
cd bouyomichan-mcp-nodejs

  1. 종속성을 설치합니다.

npm install

  1. 컴파일 :

npm run build

사용방법

서버 시작

npm start

Claude for Desktop과의 협력

Claude for Desktop과 함께 작동하려면 구성 파일을 편집해야 합니다.

  1. Claude for Desktop 구성 파일을 엽니다.

    • MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json

    • Windows: %APPDATA%\Claude\claude_desktop_config.json

  2. 다음 내용을 추가합니다 (경로를 실제 파일 경로로 바꾸십시오) :

{
  "mcpServers": {
    "bouyomi": {
      "command": "node",
      "args": [
        "/絶対パス/bouyomichan-mcp-nodejs/build/index.js"
      ]
    }
  }
}

  1. Claude for Desktop을 다시 시작합니다.

사용 예

Claude for Desktop에서 다음과 같이 지시하면 텍스트가 음성으로 들립니다.

  • "안녕하세요, 세계"라고 읽고

  • 남성의 목소리로 "이것은 테스트입니다."

  • 속도를 빨리 "서둘러요"라고 읽고

파라미터 설명

매개변수

설명

기본값

유효 범위

텍스트

읽는 텍스트

필수

모든 텍스트

음성

음성 유형

0 (여성 1)

0: 여성 1, 1: 남성 1, 2: 여성 2,...

volume

볼륨

-1 (기본값)

-1: 기본값, 0-100: 볼륨 레벨

속도

속도

-1 (기본값)

-1: 기본값, 50-200: 속도 레벨

tone

음정

-1 (기본값)

-1: 기본값, 50-200: 음정 레벨

라이센스

MIT

Available Tools

1 tool
read_textB

テキストを棒読みちゃんで読み上げます

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

B3.1/5.0
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 reading happens 'with a monotone voice' which is useful behavioral context, but doesn't disclose other important traits: whether this is a synchronous or asynchronous operation, what happens with long texts, error conditions, or what the output actually is (audio file, playback, etc.). For a tool with zero annotation coverage, this is insufficient.

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 Japanese sentence that directly states what the tool does. Every word earns its place: 'テキスト' (text), '棒読みちゃん' (monotone voice), '読み上げます' (reads aloud). No wasted words or unnecessary elaboration.

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 the tool has no annotations, no output schema, and zero parameters, the description should provide more complete context. While it states the basic purpose, it doesn't explain what form the output takes (audio stream, file, immediate playback), performance limitations, or error handling. For a text-to-speech tool, this leaves significant gaps in understanding how to use it effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, and schema description coverage is 100%. The description doesn't need to explain parameters, and it correctly implies text input through its purpose statement. Baseline for zero parameters with full schema coverage is 4, as there's nothing to compensate for.

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 tool's purpose: 'テキストを棒読みちゃんで読み上げます' translates to 'Reads text aloud with a monotone voice'. This specifies the verb ('reads aloud') and resource ('text'), though it doesn't need to distinguish from siblings since none exist. The mention of 'monotone voice' adds specificity about the reading style.

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. It doesn't mention prerequisites, context for usage, or any exclusions. While no sibling tools exist to differentiate from, it lacks basic usage context like input format expectations or performance characteristics.

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 observedread_text
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TDQS

B3.3/5.0
Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The tool 'read_text' has a singular, clear purpose that cannot be confused with any other tool in this set.

Naming Consistency5/5

A single tool inherently exhibits perfect naming consistency, as there are no other tools to compare it against. The name 'read_text' follows a clear verb_noun pattern, which is consistent with itself.

Tool Count2/5

A single tool for a text-to-speech server is too minimal for practical use. While it covers the core functionality, typical MCP servers benefit from additional tools (e.g., for configuration, status checks, or voice control), making this count feel thin and limiting for agent interactions.

Completeness3/5

The tool 'read_text' provides the essential action for a text-to-speech server, but there are notable gaps. Missing operations might include stopping speech, adjusting speed or volume, checking status, or managing voice settings, which could hinder agent workflows in more complex scenarios.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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