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Kokoro Text to Speech MCP Server

by mberg

Servidor MCP de texto a voz (TTS) de Kokoro

Servidor MCP de texto a voz Kokoro que genera archivos .mp3 con opción de cargar a S3.

Usos: https://huggingface.co/spaces/hexgrad/Kokoro-TTS

Related MCP server: MCP Voice Soundboard

Configuración

Agregue lo siguiente a sus configuraciones de MCP. Actualícelo con sus propios valores.

  "kokoro-tts-mcp": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/toyourlocal/kokoro-tts-mcp",
        "run",
        "mcp-tts.py"
      ],
      "env": {
        "TTS_VOICE": "af_heart",
        "TTS_SPEED": "1.0",
        "TTS_LANGUAGE": "en-us",
        "AWS_ACCESS_KEY_ID": "",
        "AWS_SECRET_ACCESS_KEY": "",
        "AWS_REGION": "us-east-1",
        "AWS_S3_FOLDER": "mp3",
        "S3_ENABLED": "true",
        "MP3_FOLDER": "/path/to/mp3"
      } 
    }

Instalar ffmmeg

Esto es necesario para convertir archivos .wav a .mp3

Para Mac:

brew install ffmpeg

Para ejecutar localmente, agréguelos a su archivo .env. Consulte env.example, cópielos a .env y modifíquelos con sus propios valores.

Variables de entorno admitidas

  • AWS_ACCESS_KEY_ID : Su ID de clave de acceso de AWS

  • AWS_SECRET_ACCESS_KEY : Su clave de acceso secreta de AWS

  • AWS_S3_BUCKET_NAME : nombre del depósito S3

  • AWS_S3_REGION : región S3 (por ejemplo, us-east-1)

  • AWS_S3_FOLDER : Ruta de la carpeta dentro del depósito S3

  • AWS_S3_ENDPOINT_URL : URL de punto final personalizada opcional para almacenamiento compatible con S3

  • MCP_HOST : Host al que vincular el servidor (predeterminado: 0.0.0.0)

  • MCP_PORT : Puerto para escuchar (predeterminado: 9876)

  • MCP_CLIENT_HOST : Nombre de host para las conexiones del cliente al servidor (predeterminado: localhost)

  • DEBUG : Habilitar el modo de depuración (establecido en "verdadero" o "1")

  • S3_ENABLED : Habilitar cargas S3 (establecido en "verdadero" o "1")

  • MP3_FOLDER : Ruta para almacenar archivos MP3 (el valor predeterminado es la carpeta 'mp3' en el directorio de scripts)

  • MP3_RETENTION_DAYS : Número de días que se conservarán los archivos MP3 antes de su eliminación automática

  • DELETE_LOCAL_AFTER_S3_UPLOAD : Si se deben eliminar los archivos MP3 locales después de una carga S3 exitosa (establecido en "verdadero" o "1")

  • TTS_VOICE : Voz predeterminada para el cliente TTS (predeterminado: af_heart)

  • TTS_SPEED : Velocidad predeterminada para el cliente TTS (valor predeterminado: 1.0)

  • TTS_LANGUAGE : idioma predeterminado para el cliente TTS (predeterminado: en-us)

Ejecución del servidor local

El método preferido es utilizar UV.

uv run mcp-tts.py

Uso del cliente TTS

El script mcp_client.py permite enviar solicitudes TTS al servidor. Se puede usar de la siguiente manera:

Configuración de conexión

Al ejecutar el servidor y el cliente en la misma máquina:

  • El servidor debe vincularse a 0.0.0.0 (todas las interfaces) o 127.0.0.1 (solo localhost)

  • El cliente debe conectarse al localhost o 127.0.0.1

Uso básico

python mcp_client.py --text "Hello, world!"

Leer texto de un archivo

python mcp_client.py --file my_text.txt

Personalización de la voz y la velocidad

python mcp_client.py --text "Hello, world!" --voice "en_female" --speed 1.2

Deshabilitar la carga de S3

python mcp_client.py --text "Hello, world!" --no-s3

Opciones de la línea de comandos

python mcp_client.py --help

Gestión de archivos MP3

El servidor TTS genera archivos MP3 que se almacenan localmente y, opcionalmente, se suben a S3. Puede configurar la gestión de estos archivos:

Almacenamiento local

  • Establezca MP3_FOLDER en su archivo .env para especificar dónde se almacenan los archivos MP3

  • Los archivos se guardan en esta carpeta a menos que se eliminen automáticamente

Limpieza automática

  • Establezca MP3_RETENTION_DAYS=30 (o cualquier número) para eliminar automáticamente los archivos con más días de antigüedad

  • Establezca DELETE_LOCAL_AFTER_S3_UPLOAD=true para eliminar archivos locales inmediatamente después de una carga S3 exitosa

Integración S3

  • Habilitar o deshabilitar las cargas S3 con S3_ENABLED=true o DISABLE_S3=true

  • Configurar las credenciales de AWS y la configuración del depósito en el archivo .env

  • Las cargas S3 se pueden deshabilitar por solicitud utilizando la opción --no-s3 del cliente

Available Tools

1 tool
text_to_speechB
    Convert text to speech using the Kokoro TTS service.
    
    Args:
        text: The text to convert to speech
        voice: Voice ID to use (default: af_heart)
        speed: Speech speed (default: 1.0)
        lang: Language code (default: en-us)
        filename: Optional filename for the MP3 (default: auto-generated UUID)
        upload_to_s3: Whether to upload to S3 if enabled (default: True)
        
    Returns:
        A dictionary with information about the generated audio file
    
ParametersJSON Schema
NameRequiredDescriptionDefault
textYes
voiceNoen_sarah
speedNo
langNoen-us
filenameNo
upload_to_s3No

TDQS

B3.3/5.0
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 the output is an MP3 file and that upload to S3 is optional, which adds some context. However, it doesn't cover important aspects like rate limits, authentication requirements, error conditions, or what the returned dictionary contains. For a tool with 6 parameters and no 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and appropriately sized. It starts with the core purpose, then lists parameters with clear explanations, and ends with return information. Every sentence earns its place, though the formatting with 'Args:' and 'Returns:' sections is slightly verbose but still efficient.

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 complexity (6 parameters, no annotations, no output schema), the description is moderately complete. It excels at parameter documentation but lacks behavioral context and usage guidelines. The absence of an output schema means the description should ideally explain the return dictionary structure, which it doesn't. It's adequate but has clear gaps.

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 provides excellent parameter semantics beyond the input schema. With 0% schema description coverage, the description fully compensates by explaining each parameter's purpose, default values, and optionality. It clarifies that 'filename' is auto-generated if not provided and that 'upload_to_s3' depends on whether S3 is enabled. This adds significant value over the bare schema.

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: 'Convert text to speech using the Kokoro TTS service.' It specifies the verb ('convert') and resource ('text to speech'), and mentions the specific service. However, without sibling tools, it cannot demonstrate differentiation from alternatives, so it doesn't reach the highest score.

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 any prerequisites, constraints, or typical use cases. The only contextual information is the service name (Kokoro TTS), but this doesn't help an agent decide when this tool is appropriate.

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
    • Changedtext_to_speech2 fields changed
      • changedInput schema / properties / speed / default
        Previous value: -1New value: +1.2
      • changedInput schema / properties / voice / default
        Previous value: -"af_heart"New value: +"en_sarah"
  2. 1 tool update
    • First observedtext_to_speech

TDQS

B3.4/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool has a clearly defined purpose of converting text to speech, which cannot be confused with any other functionality.

Naming Consistency5/5

The single tool name 'text_to_speech' follows a clear verb_noun pattern and uses snake_case consistently. With only one tool, naming consistency is inherently perfect as there are no other tools to compare against.

Tool Count2/5

A single tool is too few for a server's apparent scope of text-to-speech functionality. While the tool itself is well-defined, a complete TTS service would typically include additional tools such as listing available voices, managing audio files, or configuring settings, making this server feel thin and incomplete.

Completeness2/5

The server is severely incomplete for a text-to-speech domain. It lacks essential operations such as listing available voices, checking service status, managing generated audio files (beyond the single generation tool), or handling configuration. This creates significant gaps that will limit agent workflows and cause failures in more complex tasks.

Maintenance

ActivityInactive
ResponsivenessNo issues

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