Kokoro Text to Speech MCP Server
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
Clonar a un repositorio local.
Descargue los pesos Kokoro Onnx para kokoro-v1.0.onnx y voices-v1.0.bin y guárdelos en el mismo repositorio.
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 ffmpegPara 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 AWSAWS_SECRET_ACCESS_KEY: Su clave de acceso secreta de AWSAWS_S3_BUCKET_NAME: nombre del depósito S3AWS_S3_REGION: región S3 (por ejemplo, us-east-1)AWS_S3_FOLDER: Ruta de la carpeta dentro del depósito S3AWS_S3_ENDPOINT_URL: URL de punto final personalizada opcional para almacenamiento compatible con S3MCP_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 automáticaDELETE_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.pyUso 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) o127.0.0.1(solo localhost)El cliente debe conectarse al
localhosto127.0.0.1
Uso básico
python mcp_client.py --text "Hello, world!"Leer texto de un archivo
python mcp_client.py --file my_text.txtPersonalización de la voz y la velocidad
python mcp_client.py --text "Hello, world!" --voice "en_female" --speed 1.2Deshabilitar la carga de S3
python mcp_client.py --text "Hello, world!" --no-s3Opciones de la línea de comandos
python mcp_client.py --helpGestión 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_FOLDERen su archivo.envpara especificar dónde se almacenan los archivos MP3Los 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 antigüedadEstablezca
DELETE_LOCAL_AFTER_S3_UPLOAD=truepara eliminar archivos locales inmediatamente después de una carga S3 exitosa
Integración S3
Habilitar o deshabilitar las cargas S3 con
S3_ENABLED=trueoDISABLE_S3=trueConfigurar las credenciales de AWS y la configuración del depósito en el archivo
.envLas cargas S3 se pueden deshabilitar por solicitud utilizando la opción
--no-s3del cliente
Available Tools
1 tooltext_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
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | ||
| voice | No | en_sarah | |
| speed | No | ||
| lang | No | en-us | |
| filename | No | ||
| upload_to_s3 | No |
TDQS
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.
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.
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.
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.
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.
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 tool update
v1.0.0- Changed
text_to_speech2 fields changed- changed
Input schema / properties / speed / defaultPrevious value: -1New value: +1.2 - changed
Input schema / properties / voice / defaultPrevious value: -"af_heart"New value: +"en_sarah"
1 tool update
- First observed
text_to_speech
TDQS
Scored across 1 tool
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.
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.
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.
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
Related MCP Connectors
Hosted text-to-speech MCP server: 66 voices in 9 languages, long-form narration, podcasts.
MCP server for Text-to-Speech
MCP server exposing the AceDataCloud Fish Audio API (text-to-speech with voice conditioning)
Hosted pay-per-use TTS: 54 neural voices, 9 languages incl. Brazilian Portuguese. $10 free credits.
Related MCP Servers
- AlicenseBqualityDmaintenanceA Model Context Protocol server that provides text-to-speech capabilities using the Kokoro TTS model, offering multiple voice options and customizable speech parameters.421 npm2MIT
- AlicenseNot gradedqualityAmaintenanceA text-to-speech MCP server with 48 voices across 9 languages, supporting emotion spans, SFX tags, and multi-speaker dialogue. Deployable via a single npx command with built-in guardrails and swappable backends.MIT
- AlicenseNot gradedqualityDmaintenanceAn OpenAI-compatible text-to-speech API server that enables voice cloning, long-form generation, and can be used as a Claude Code tool for speech synthesis.MIT
- AlicenseNot gradedqualityDmaintenanceHeadless text-to-speech and speech-to-text server with REST and MCP API, supporting Kokoro TTS and Whisper STT.MIT