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Bouyomi-chan MCP Server

Bouyomi-chan MCP Server

by uraoz
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
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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_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.

Maintenance

ActivityInactive
ResponsivenessNo recent community issues

Resources

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