Skip to main content
Glama
OnyxFalcon

Piper TTS MCP Server

by OnyxFalcon
OverviewSchemaSourceRelated ServersScoreDiscussionsAdmin
Python

🎤 Piper TTS MCP Server

A Model Context Protocol (MCP) server that integrates Piper TTS for high-quality text-to-speech functionality. This server provides a speak tool that converts text to speech and plays it directly through your speakers with customizable volume control.

✨ Features

  • 🔊 High-quality text-to-speech using Piper TTS

  • 🎚️ Volume control (0.01 to 1.00)

  • ⚡ Memory-based audio playback (no temporary files)

  • 🔧 Customizable voice parameters (speaker, speed, voice variation)

  • 🚀 Fast and lightweight MCP integration

  • 🔇 Silent operation (no console output)

Related MCP server: Kokoro TTS MCP Server

📋 Requirements

⚠️ Important Prerequisites:

  • Python 3.12+ installed on your system

  • Piper TTS service running on localhost:5000

You can set this up using the included Docker configuration or by running Piper TTS separately.

🚀 Quick Start

1. 🐳 Start Piper TTS Service

Clone this repo and start the TTS service:

git clone https://github.com/CryptoDappDev/piper-tts-mcp.git
cd piper-tts-mcp

Option A: Using Docker Compose (Recommended)

docker compose up -d

Option B: Using Docker Build

docker build -t piper-tts-mcp .
docker run -p 5000:5000 piper-tts-mcp

2. 🔧 Configure MCP Client

Add the server to your MCP configuration (e.g., .mcp.json for Claude Desktop):

{
  "mcpServers": {
    "speak": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/your/piper-tts-mcp",
        "run",
        "server.py"
      ]
    }
  }
}

3. 🎉 Enjoy!

The speak tool is now available in your MCP client!

🛠️ Usage

The MCP server provides a speak tool with the following parameters:

Parameter

Type

Default

Description

text

string

required

Text to convert to speech

speaker_id

int

0

Voice speaker ID

length_scale

float

1.1

Speech speed (lower = faster)

noise_scale

float

0.667

Voice variation control

noise_w_scale

float

0.333

Pronunciation variation

volume

float

0.15

Volume level (0.01 to 1.00)

Example Usage

# Basic usage
speak("Hello, world!")

# With custom volume
speak("This is louder!", volume=0.5)

# With custom voice settings
speak("Fast and varied speech", 
      length_scale=0.8, 
      volume=0.3, 
      noise_scale=0.8)

🔧 Development

Dependencies

  • Python 3.12+

  • mcp[cli] - MCP framework

  • requests - HTTP client for TTS API

  • pygame - Audio playback

Local Development

# Install dependencies
uv sync

# Run the server
uv run server.py

📦 Docker Configuration

The included Docker setup provides:

  • Piper TTS service on port 5000

  • Pre-configured voice models

  • Automatic startup

🎙️ Voice Models

The default voice model used in this repository is en_GB-cori-high (British English, female voice).

🔍 Exploring Voice Options

🛠️ Changing Voice Models

To use a different voice model:

  1. Choose a voice from the voice samples page

  2. Update the Dockerfile - Replace en_GB-cori-high with your chosen voice model:

    # Download your preferred voice model
RUN python3 -m piper.download_voices your-chosen-voice-model

# Update the server command
CMD ["sh", "-c", "python3 -m piper.http_server -m your-chosen-voice-model"]

  3. Rebuild the Docker image:

    docker build -t piper-tts-mcp .
docker compose up -d

🎨 Creating Custom Voices

Creating your own custom voice requires additional effort and research. Please refer to the Piper documentation for guidance on voice training and customization.

🤝 Contributing

Contributions are welcome! Please feel free to submit issues and pull requests.

📄 License

This project is open source and available under the MIT License.

🙏 Acknowledgments

Made with ❤️ for the MCP community

Available Tools

1 tool
speakA
Convert text to speech and play it through the speakers.

Args:
    text: The text to convert to speech
    speaker_id: Voice speaker ID (default: 0)
    length_scale: Speech speed control (default: 1.1, lower = faster)
    noise_scale: Voice variation control (default: 0.667)
    noise_w_scale: Pronunciation variation control (default: 0.333)
    volume: Volume level from 0.01 to 1.00 (default: 0.15)

Returns:
    Success or error message
ParametersJSON Schema
NameRequiredDescriptionDefault
textYes
speaker_idNo
length_scaleNo
noise_scaleNo
noise_w_scaleNo
volumeNo

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A4.1/5.0
Behavior3/5

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 discloses that the tool plays speech through speakers and returns a success or error message, which covers basic behavior. However, it lacks details on potential side effects (e.g., audio output interruption), permissions, or error handling, leaving gaps in transparency.

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 well-structured and front-loaded with the core purpose, followed by a clear breakdown of parameters and returns. Every sentence adds value without redundancy, making it efficient and easy to parse for an agent.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (6 parameters) and no annotations, the description does a good job covering parameters and basic behavior. With an output schema present, it doesn't need to detail return values. However, it could improve by addressing potential constraints like audio device requirements or usage limits.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds significant meaning beyond the input schema, which has 0% coverage. It explains each parameter's purpose (e.g., 'speaker_id: Voice speaker ID', 'length_scale: Speech speed control'), including default values and effects (e.g., 'lower = faster'), fully compensating for the schema's lack of descriptions.

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 tool's purpose with a specific verb ('Convert text to speech and play it through the speakers'), identifying both the action and resource. It distinguishes itself by specifying the exact functionality without ambiguity, and since there are no sibling tools, no differentiation is needed.

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 for text-to-speech conversion but provides no explicit guidance on when to use this tool versus alternatives. With no sibling tools mentioned, there's no context for comparison, leaving the agent to infer usage based on the stated purpose alone.

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 update
    • First observedspeak
Install Server
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

A3.9/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of confusion or overlap between tools. The single 'speak' tool has a clearly defined and distinct purpose of converting text to speech.

Naming Consistency5/5

The single tool name 'speak' follows a clear verb-based pattern that directly describes its function. With only one tool, consistency is inherently perfect as there are no other tools to compare against.

Tool Count2/5

A single tool feels thin for a TTS server that could reasonably support additional functionality like listing available voices, checking synthesis status, or managing audio output. While the core functionality is present, the tool surface is minimal.

Completeness2/5

The server provides basic text-to-speech conversion but lacks complementary tools that would create a complete TTS workflow. There are no tools for voice management, synthesis monitoring, or audio file handling, leaving significant gaps in the domain coverage.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Connectors

Related MCP Servers

  • ElevenLabs Text-to-Speech MCP

    georgi-io
    F
    license
    Not graded
    quality
    D
    maintenance
    Integrates ElevenLabs Text-to-Speech capabilities with Cursor through the Model Context Protocol, allowing users to convert text to speech with selectable voices within the Cursor editor.
    1
    -

  • Kokoro TTS MCP Server

    giannisanni
    A
    license
    D
    quality
    C
    maintenance
    Provides text-to-speech capabilities through the Model Context Protocol, allowing applications to easily integrate speech synthesis with customizable voices, adjustable speech speed, and cross-platform audio playback support.
    1
    10
    MIT

  • CastleCall MCP

    ctf2009
    F
    license
    Not graded
    quality
    D
    maintenance
    Exposes a text-to-speech announcement tool for the CastleCall home PA system on Raspberry Pi. It enables users to broadcast voice messages using local Piper or cloud-based ElevenLabs providers through the Model Context Protocol.
    -

Appeared in Searches