Deep learning for Text to Speech by Coqui.
TTS MCP server exists for a simple reason — assistants are far more useful when they can act on TTS directly instead of describing what you should do. Deep learning for Text to Speech by Coqui.
🛠️ Tools for training new models and fine-tuning existing models in any language.
Once TTS is connected, these are the calls the assistant has available:
Vocoders — The Vocoders tool exposed by this serverYou will need one environment variable: OUTPUT_PATH. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.
Installation goes through your MCP client rather than a global install: point it at TTS on PyPI and it is fetched when the client starts. The copy-paste blocks for Claude Desktop, Claude Code and Cursor are further down this page.
Among the monitoring and observability options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. TTS's toolset — Vocoders — is a fair guide to whether it matches your workflow. It is maintained by Eren Gölge; worth a glance at recent repository activity before you build anything load-bearing on it.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
| Tool | What it does |
|---|---|
| Vocoders | The Vocoders tool exposed by this server. |
{
"mcpServers": {
"tts-1": {
"command": "uvx",
"args": ["TTS"],
"env": {
"OUTPUT_PATH": "your-value"
}
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
| Variable | Description | Required |
|---|---|---|
| OUTPUT_PATH | Filesystem location the server is allowed to use. | Optional |
Give your coding agent the full DevTools toolbox: traces, network, console, heap snapshots and Lighthouse.
Dashboards, Prometheus and Loki queries, incidents and alerts — observability by conversation.
Errors with full context — stack traces, issue triage and AI-powered root-cause analysis from Sentry's server.
Enables enhanced web research capabilities for large language models through intelligent search queuing and advanced content extraction.
Automates browser interactions and enables Large Language Models (LLMs) to interact with web pages through Playwright and Chrome DevTools Protocol
Guides tool usage by providing recommendations for MCP tools at each problem-solving stage.