Skip to main content
Glama
Sign In
deenesjakoruzh
Bouyomi-chan MCP Server

Bouyomi-chan MCP Server

by uraoz
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Local

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_textC

テキストを棒読みちゃんで読み上げます

NameRequiredDescriptionDefault

No parameters

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 the full burden. It mentions the behavioral trait of using a monotone voice (棒読みちゃん), which adds some context beyond basic functionality. However, it doesn't disclose other important behaviors like whether it requires internet access, has rate limits, handles different languages, or what happens on errors. The description is minimal and lacks comprehensive behavioral details.

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, efficient sentence in Japanese that directly states the tool's function. It's front-loaded with the core action and includes a stylistic detail (monotone voice). There's no wasted text, but it could be slightly more informative without losing conciseness.

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?

Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is somewhat complete but has gaps. It explains what the tool does but lacks details on output format (e.g., audio file, stream), error handling, or performance characteristics. For a text-to-speech tool, more context on behavior and results would be helpful, though the low complexity mitigates this.

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 0 parameters with 100% schema description coverage, so the schema fully documents the inputs. The description doesn't need to add parameter information, and it doesn't contradict the schema. Baseline is 4 for zero parameters, as the description appropriately focuses on functionality rather than inputs.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the tool's purpose as 'reading text aloud with a monotone voice' (棒読みちゃん), which is clear but somewhat vague. It specifies the action (read aloud) and the style (monotone), but doesn't distinguish from siblings since none exist. The description isn't tautological but lacks specific details about the resource or 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?

No guidance is provided on when to use this tool versus alternatives. The description implies usage for text-to-speech with a monotone voice, but there's no mention of prerequisites, limitations, or context for when this specific style is appropriate. With no sibling tools, differentiation isn't needed, but general usage context is missing.

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 observedread_text
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

B3.3/5.0

Scored across 1 tool

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.

Resources

Related MCP Connectors

Related MCP Servers

  • Voicevox MCP Server

    Dosugamea
    A
    license
    B
    quality
    F
    maintenance
    A server that enables Claude 3.7 and other AI agents to access VOICEVOX-compatible speech synthesis engines (AivisSpeech, VOICEVOX, COEIROINK) through the Model Context Protocol.
    1
    12
    MIT

  • VOICEVOX TTS MCP

    kajidog
    A
    license
    A
    quality
    B
    maintenance
    A text-to-speech MCP server that enables AI assistants to speak using the VOICEVOX engine with support for multi-character conversations. It features queue management, low-latency streaming via FFplay, and cross-platform playback across Windows, macOS, and Linux.
    7
    149 npm
    16
    ISC

  • voiceroid_daemon-mcp

    mohemohe
    A
    license
    A
    quality
    D
    maintenance
    An MCP server that enables text-to-speech generation and phonetic kana conversion using VOICEROID2 via voiceroid_daemon. It supports customizable voice parameters and provides cross-platform audio playback for synthesized speech.
    3
    MIT