VOICEVOX MCP Server
voicevox-mcp
Bei diesem Projekt handelt es sich um einen MCP-Server (Model Context Protocol), der mit der VOICEVOX-Engine zusammenarbeitet, um Sprache zu synthetisieren und Sprecherinformationen zu erhalten. Es ist in TypeScript implementiert und verwendet das MCP SDK.
Funktion
Holen Sie sich Lautsprecherinformationen für die VOICEVOX-Engine (/speakers)
Text auf einem angegebenen Lautsprecher in Sprache umwandeln und lokal abspielen (/speak)
Nur Mac
aufstellen
Starten der VOICEVOX-Engine (Docker empfohlen)
docker compose up -dDadurch wird die VOICEVOX-Engine auf localhost:50021 gestartet.
Related MCP server: AivisSpeech MCP Server
Installieren und Erstellen von Abhängigkeiten
npm install
npm run build Anwendung
Beispiel für die Cursoreinstellung
{
"mcpServers": {
"voicevox-mcp": {
"command": "node",
"args": ["${Path to Repository}/dist/index.js"],
"env": {
"SPEAKER_ID": 8,
"SPEED_SCALE": 1.2,
"VOICEVOX_API_URL": "http://localhost:50021"
}
}
}
}Legen Sie VOICEVOX_API_URL nach Bedarf fest.
Sie können mithilfe des Sprechertools eine Liste der Sprecher von Ihrem MCP-Client abrufen.
Das Sprachtool kann Text in Sprache umwandeln und lokal wiedergeben (Mac wird empfohlen, da es den Befehl afplay verwendet).
Hauptabhängigkeiten
@modelcontextprotocol/sdkzodtypescript
Vorsichtsmaßnahmen
Zukünftige Verbesserungen
Die Sprachsynthese ist nur verfügbar, wenn die VOICEVOX-Engine auf localhost:50021 ausgeführt wird.
Wenn Sie eine andere Umgebung als Mac verwenden, ändern Sie den Afplay-Teil entsprechend.
Lizenz
MIT-Lizenz
Available Tools
2 toolsspeakD
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
speakersD
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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.
2 tool updates
v1.0.0- First observed
speak - First observed
speakers
TDQS
Scored across 2 tools
The two tools have clearly distinct purposes: 'speak' implies text-to-speech synthesis, while 'speakers' likely lists available voice options. There is no overlap or ambiguity between them.
Both tools use simple, consistent naming: 'speak' and 'speakers' are both lowercase nouns, with 'speak' as a verb-like noun and 'speakers' as a plural noun. The pattern is uniform and predictable.
With only two tools, the server feels thin for a VOICEVOX text-to-speech domain. Expected operations like adjusting voice parameters, controlling playback, or managing audio output are missing, making the set under-scoped.
The tool surface is severely incomplete for a VOICEVOX server. Core functionalities such as voice customization, audio format settings, playback control, or synthesis status checks are absent, leaving significant gaps that will hinder agent workflows.
Maintenance
Related MCP Connectors
A comprehensive Model Context Protocol (MCP) server that enables AI assistants to interact with yo…
MCP server for Text-to-Speech
MCP server exposing the AceDataCloud Fish Audio API (text-to-speech with voice conditioning)
Hosted text-to-speech MCP server: 66 voices in 9 languages, long-form narration, podcasts.
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
- FlicenseDqualityDmaintenanceA Model Context Protocol server that enables AI assistants to utilize AivisSpeech Engine's high-quality voice synthesis capabilities through a standardized API interface.11-
- AlicenseBqualityFmaintenanceA server that enables Claude 3.7 and other AI agents to access VOICEVOX-compatible speech synthesis engines (AivisSpeech, VOICEVOX, COEIROINK) through the Model Context Protocol.112MIT
- AlicenseBqualityDmaintenanceA Model Context Protocol server that provides text-to-speech functionality for AI agents using Microsoft Edge's text-to-speech technology, supporting multiple voices, languages, and voice customization.28MIT