Kokoro Text to Speech MCP Server
Kokoro テキスト読み上げ (TTS) MCP サーバー
S3 にアップロードするオプションを備えた .mp3 ファイルを生成する Kokoro Text to Speech MCP サーバー。
使用場所: https://huggingface.co/spaces/hexgrad/Kokoro-TTS
Related MCP server: MCP Voice Soundboard
構成
ローカルリポジトリにクローンします。
kokoro-v1.0.onnxとvoices-v1.0.binのKokoro Onnx Weightsをダウンロードし、同じリポジトリに保存します。
MCP 設定に以下を追加してください。ご自身の値に更新してください。
"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"
}
}ffmmegをインストールする
これは.wavファイルを.mp3ファイルに変換するために必要です
Macの場合:
brew install ffmpegローカルで実行するには、これらを.envファイルに追加してください。env.exampleを参照し、.envにコピーして、ご自身の値に変更してください。
サポートされている環境変数
AWS_ACCESS_KEY_ID: AWSアクセスキーIDAWS_SECRET_ACCESS_KEY: AWSシークレットアクセスキーAWS_S3_BUCKET_NAME: S3バケット名AWS_S3_REGION: S3リージョン(例:us-east-1)AWS_S3_FOLDER: S3バケット内のフォルダパスAWS_S3_ENDPOINT_URL: S3互換ストレージのオプションのカスタムエンドポイントURLMCP_HOST: サーバーをバインドするホスト (デフォルト: 0.0.0.0)MCP_PORT: リッスンするポート (デフォルト: 9876)MCP_CLIENT_HOST: サーバーへのクライアント接続のホスト名 (デフォルト: localhost)DEBUG: デバッグモードを有効にする(「true」または「1」に設定)S3_ENABLED: S3アップロードを有効にする(「true」または「1」に設定)MP3_FOLDER: MP3ファイルを保存するパス(デフォルトはスクリプトディレクトリ内の「mp3」フォルダ)MP3_RETENTION_DAYS: MP3ファイルを自動的に削除するまで保存する日数DELETE_LOCAL_AFTER_S3_UPLOAD: S3アップロードが成功した後にローカルのMP3ファイルを削除するかどうか(「true」または「1」に設定)TTS_VOICE: TTSクライアントのデフォルトの音声(デフォルト: af_heart)TTS_SPEED: TTSクライアントのデフォルト速度(デフォルト: 1.0)TTS_LANGUAGE: TTSクライアントのデフォルト言語(デフォルト: en-us)
ローカルでサーバーを実行する
推奨される方法はUVを使用する
uv run mcp-tts.pyTTSクライアントの使用
mcp_client.pyスクリプトを使用すると、TTS リクエストをサーバーに送信できます。使い方は以下です。
接続設定
サーバーとクライアントを同じマシン上で実行する場合:
サーバーは
0.0.0.0(すべてのインターフェース) または127.0.0.1(ローカルホストのみ) にバインドする必要がありますクライアントは
localhostまたは127.0.0.1に接続する必要があります
基本的な使い方
python mcp_client.py --text "Hello, world!"ファイルからテキストを読み込む
python mcp_client.py --file my_text.txt音声と速度のカスタマイズ
python mcp_client.py --text "Hello, world!" --voice "en_female" --speed 1.2S3アップロードを無効にする
python mcp_client.py --text "Hello, world!" --no-s3コマンドラインオプション
python mcp_client.py --helpMP3ファイル管理
TTSサーバーはMP3ファイルを生成し、ローカルに保存するか、オプションでS3にアップロードします。これらのファイルの管理方法を設定できます。
ローカルストレージ
.envファイルでMP3_FOLDERを設定して、MP3ファイルが保存される場所を指定します。ファイルは自動的に削除されない限りこのフォルダに保存されます
自動クリーンアップ
MP3_RETENTION_DAYS=30(または任意の数字)を設定すると、その日数より古いファイルが自動的に削除されます。DELETE_LOCAL_AFTER_S3_UPLOAD=trueを設定すると、S3 アップロードが成功した直後にローカルファイルを削除します。
S3統合
S3_ENABLED=trueまたはDISABLE_S3=trueで S3 アップロードを有効/無効にする.envファイルで AWS 認証情報とバケット設定を構成するS3アップロードはクライアントの
--no-s3オプションを使用してリクエストごとに無効にすることができます。
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