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Bouyomi-chan MCP Server

by uraoz
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Boyomi-chan MCP 服务器 (Node.js 版本)

这是一个使用模型上下文协议 (MCP) 的服务器，通过 Boyomi-chan (Yukkuri Voice) 为 AI 助手提供文本转语音功能。在 Node.js/TypeScript 中实现。

概述

该服务器是允许 Claude 等 AI 助手使用 Boyomi-chan 的 MCP 服务器。

Related MCP server: MCP Simple AivisSpeech

功能

  • 文本转语音

  • 选择声音类型（女声、男声等）

  • 音量调节

  • 可调节语速

  • 音调调整

先决条件

  • Node.js 16 或更高版本

  • npm 7 或更高版本

  • 必须安装 Boyomi-chan。

  • Boyomi-chan 的 HTTP 链接正在端口 50080 上运行。

如何安装

  1. 克隆此存储库：

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

  1. 安装依赖项：

npm install

  1. 编译结果如下：

npm run build

如何使用

启动服务器

npm start

与 Claude for Desktop 集成

要使用 Claude for Desktop，您需要编辑配置文件：

  1. 打开 Claude for Desktop 配置文件：

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

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

  2. 添加以下内容（将路径替换为实际文件路径）：

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

  1. 重新启动 Claude 桌面版。

使用示例

Claude for Desktop 将通过以下方式向您大声朗读文本：

  • 读出“Hello, World”

  • 一个男声读出“这是一次测试”。

  • 加快阅读速度“我很着急”

参数说明

参数

解释

默认值

范围

文本

阅读文本

必需的

任何文本

嗓音

音频类型

0 (1 名女性)

0：女 1，1：男 1，2：女 2，...

体积

体积

-1（默认值）

-1：默认，0-100：音量

速度

速度

-1（默认值）

-1：默认，50-200：速度级别

语气

沥青

-1（默认值）

-1：默认，50-200：音高级别

执照

麻省理工学院

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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