Skip to main content
Glama
deenesjakoruzh
pinkpixel-dev

Blabber-MCP

by pinkpixel-dev
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Hybrid

📢 Blabber-MCP 🗣️

blabber-mcp MCP server

一个 MCP 服务器，通过 OpenAI 的文本转语音 API 为你的 LLM 提供语音功能！ 🔊

✨ 功能

  • 文本转语音： 将输入的文本转换为高质量的语音音频。

  • 声音选择： 可从多种 OpenAI 声音中选择（alloy、echo、fable、onyx、nova、shimmer）。

  • 模型选择： 使用标准模型 (tts-1) 或高清模型 (tts-1-hd)。

  • 格式选项： 获取 mp3、opus、aac 或 flac 格式的音频输出。

  • 文件保存： 将生成的音频保存到本地文件。

  • 可选播放： 使用可配置的系统命令自动播放生成的音频。

  • 可配置默认值： 通过配置设置默认声音。

Related MCP server: NijiVoice-MCP

🔧 配置

要使用此服务器，你需要将其配置添加到 MCP 客户端的设置文件中（例如 mcp_settings.json）。

  1. 获取 OpenAI API 密钥： 你需要从 OpenAI 获取 API 密钥。

  2. 添加到 MCP 设置： 将以下代码块添加到设置文件中的 mcpServers 对象中，并将 "YOUR_OPENAI_API_KEY" 替换为你的实际密钥。

{
  "mcpServers": {
    "blabber-mcp": {
      "command": "node",
      "args": ["/full/path/to/blabber-mcp/build/index.js"], (IMPORTANT: Use the full, absolute path to the built index.js file)
      "env": {
        "OPENAI_API_KEY": "YOUR_OPENAI_API_KEY",
        "AUDIO_PLAYER_COMMAND": "xdg-open", (Optional: Command to play audio (e.g., "cvlc", "vlc", "mpv", "ffplay", "afplay", "xdg-open"; defaults to "cvlc")
        "DEFAULT_TTS_VOICE": "nova" (Optional: Set default voice (alloy, echo, fable, onyx, nova, shimmer); defaults to nova)
      },
      "disabled": false,
      "alwaysAllow": []
    }
  }
}

重要提示： 请确保 args 路径指向 blabber-mcp 项目目录中 build/index.js 文件的正确位置。请使用完整的绝对路径。

🚀 使用方法

配置并运行后，你可以通过 MCP 客户端使用 text_to_speech 工具。

工具： text_to_speech 服务器： blabber-mcp（或你在配置中使用的键名）

参数：

  • input (字符串，必填)：要合成的文本。

  • voice (字符串，可选)：要使用的声音（alloy、echo、fable、onyx、nova、shimmer）。默认为配置中设置的 DEFAULT_TTS_VOICE，或 nova。

  • model (字符串，可选)：模型（tts-1、tts-1-hd）。默认为 tts-1。

  • response_format (字符串，可选)：音频格式（mp3、opus、aac、flac）。默认为 mp3。

  • play (布尔值，可选)：设置为 true 以在保存后自动播放音频。默认为 false。

工具调用示例（带播放功能）：

<use_mcp_tool>
  <server_name>blabber-mcp</server_name>
  <tool_name>text_to_speech</tool_name>
  <arguments>
  {
    "input": "Hello from Blabber MCP!",
    "voice": "shimmer",
    "play": true
  }
  </arguments>
</use_mcp_tool>

输出：

该工具将音频文件保存到 blabber-mcp 项目文件夹内的 output/ 目录中，并返回如下 JSON 响应：

{
  "message": "Audio saved successfully. Playback initiated using command: cvlc",
  "filePath": "path/to/speech_1743908694848.mp3", 
  "format": "mp3",
  "voiceUsed": "shimmer"
}

📜 许可证

本项目采用 MIT 许可证授权 - 详情请参阅 LICENSE 文件。

🕒 更新日志

有关版本历史的详细信息，请参阅 CHANGELOG.md 文件。

Available Tools

1 tool
text_to_speechA

Converts text into spoken audio using OpenAI TTS (default voice: alloy), saves it to a file, and optionally plays it.

NameRequiredDescriptionDefault
inputYesThe text to synthesize into speech.
modelNoThe TTS model to use.tts-1
playNoWhether to automatically play the generated audio file.
response_formatNoThe format of the audio response.mp3
voiceNoOptional: The voice to use. Overrides the configured default (alloy).

TDQS

A3.5/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that audio is saved to a file and optionally played, which covers some output behavior, but lacks details on file location, naming, permissions, error handling, rate limits, or authentication needs. For a tool with no annotations, this leaves significant gaps in understanding its operational traits.

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, well-structured sentence that efficiently conveys the core functionality, default settings, and optional features without any wasted words. It's front-loaded with the primary purpose and includes all necessary details concisely.

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 complexity (5 parameters, no output schema, no annotations), the description is adequate but incomplete. It covers the basic purpose and some behavioral aspects (saving and playing), but lacks details on output format, error handling, and other operational context that would be helpful for an agent to use it effectively without annotations.

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 already documents all parameters thoroughly. The description adds minimal value beyond the schema by mentioning the default voice (alloy) and the optional play feature, but doesn't provide additional syntax, format, or usage context for parameters. This meets the baseline for high schema coverage.

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

Purpose5/5

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

The description clearly states the specific action ('Converts text into spoken audio'), identifies the resource ('using OpenAI TTS'), and provides implementation details ('saves it to a file, and optionally plays it'). It distinguishes itself by mentioning the default voice (alloy) and the optional play feature, which would be relevant if there were sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage through the phrase 'optionally plays it,' suggesting when the play parameter might be used, but provides no explicit guidance on when to use this tool versus alternatives (though none are listed as siblings). There's no mention of prerequisites, limitations, or specific scenarios for choosing different models or voices.

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 observedtext_to_speech
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

A3.5/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The tool has a clearly distinct purpose of converting text to speech, and no other tools exist to cause confusion.

Naming Consistency5/5

The single tool name 'text_to_speech' follows a clear verb_noun pattern (text_to_speech), and with only one tool, consistency is inherently perfect as there are no other names to compare against.

Tool Count2/5

A single tool is too few for most server purposes, as it severely limits functionality and scope. While the tool itself is well-defined, the server lacks breadth, making it feel thin and under-scoped for typical MCP use cases.

Completeness2/5

The server is severely incomplete for a text-to-speech domain. It only provides conversion to speech, with no tools for managing audio files (e.g., list, delete, play controls), voice selection beyond the default, or other related operations like speech-to-text, leading to significant gaps in coverage.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

Related MCP Connectors

Related MCP Servers

Appeared in Searches