MCP server for text-to-speech conversion using Kokoro TTS
Koroko speech mcp mcp server lets Claude, Cursor and other MCP clients work with Koroko Speech MCP directly. MCP server for text-to-speech conversion using Kokoro TTS.
A Model Context Protocol server that provides text-to-speech capabilities using the Kokoro TTS model.
Once connected, the assistant can call this tool directly:
Development — The Development tool exposed by this serverThe server is distributed via npm as speech-mcp-server, so most clients can run it without a manual build step. Add it to your MCP client's configuration and restart the client to pick it up — the copy-paste configs for Claude Desktop, Claude Code and Cursor are on this page.
The server reads one environment variable: MCP_DEFAULT_VOICE. Keep credentials in your client's env block or a secrets manager rather than committing them.
AI-service servers chain other models into your assistant, turning a single chat into a small production pipeline. Koroko Speech MCP sits in that group, and the shape of its toolset — Development — tells you what it is really for. Worth comparing against the other ai services servers in this directory before you commit to one, since several overlap in scope but differ sharply in setup cost and permissions.
| Tool | What it does |
|---|---|
| Development | The Development tool exposed by this server. |
{
"mcpServers": {
"speech": {
"command": "npx",
"args": [
"-y",
"speech-mcp-server"
],
"env": {
"MCP_DEFAULT_SPEECH_SPEED": 1.3,
"MCP_DEFAULT_VOICE": "af_bella"
}
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| MCP_DEFAULT_VOICE | Configuration value read at startup. | Optional |
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.