Piper TTS MCP Server
Enables AI models to generate high-quality voice messages from text using the Piper TTS engine, with automatic model management and audio streaming.
README
Piper TTS MCP Server
A Model Context Protocol (MCP) server that provides Text-to-Speech (TTS) capabilities using the Piper engine. This server allows AI models to "speak" by generating high-quality voice messages from text.
Features
- High-Quality TTS: Uses Piper for fast, local speech synthesis.
- MCP Integration: Compatible with any MCP client supporting HTTP transport.
- Audio Streaming: Returns a URL to the generated audio in Ogg Opus format (optimized for web/mobile).
- Automatic Model Management: Automatically downloads requested models if they are not present locally.
- LRU Caching: Stores the last 3 generated audio files in memory for retrieval.
Installation & Setup
Prerequisites
- Docker and Docker Compose
- Or Python 3.12 with
uv
Using Docker (Recommended)
-
Build the image:
docker compose build -
Start the server:
docker compose up piper-mcpThe server will be running at
http://localhost:8000.
Local Development
-
Install dependencies:
uv sync -
Run the server:
# Use HTTP transport by default export MCP_TRANSPORT=http uv run server.py
MCP Server Connection
To connect, use the following configuration (HTTP transport):
{
"mcpServers": {
"piper-tts": {
"type": "http",
"url": "http://localhost:8000/mcp"
}
}
}
Testing
To run the automated tests using Docker (uses the test profile):
docker compose --profile test up tests
Or locally:
pytest tests/
MCP Tool
After connecting, the following tool will be available:
speak: Generates a voice message from text.- Arguments:
text(string) — the text to speak. - Result: A JSON object containing
status,audio_url, and metadata (size, format).
- Arguments:
Model Selection
The voice model is selected using the MODEL environment variable.
- Default Model:
ru_RU-denis-medium. - Logic:
- At startup, the server checks for
.onnxand.onnx.jsonfiles in the working directory. - If not found, it automatically downloads them from the official Piper repository.
- Change the
MODELvalue indocker-compose.ymlto switch voices.
- At startup, the server checks for
Project Repositories
- Piper Engine: https://github.com/OHF-Voice/piper1-gpl
- Piper Voices (Model List): https://github.com/OHF-Voice/piper1-gpl/blob/main/docs/VOICES.md — check this repository to see all available voices and their names.
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。