Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"io-github-engr-faizanali-text-to-speech": {
"args": [
"text-to-speech-mcp"
],
"command": "uvx"
}
}
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Text to Speech is an open-source Model Context Protocol (MCP) server that lets AI assistants read text aloud on the user's computer. On Windows it uses the built-in Speech API (SAPI) by default, so no API key, account, subscription, or cloud text-to-speech service is required.
Run this in your terminal to verify the server starts. Then let us know if it worked — your result helps other developers.
uvx 'text-to-speech-mcp' 2>&1 | head -1 && echo "✓ Server started successfully"
After testing, let us know if it worked:
Five weighted categories — click any category to see the underlying evidence.
No known CVEs.
Checked text-to-speech-mcp against OSV.dev.
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Text to Speech is an open-source Model Context Protocol (MCP) server that lets AI assistants read text aloud on the user's computer. On Windows it uses the built-in Speech API (SAPI) by default, so no API key, account, subscription, or cloud text-to-speech service is required.
The server exposes one model-controlled tool:
speak_text(text: string)
Use it for user-provided text, assistant answers, accessibility workflows, or spoken progress updates while an agent works.
stdio transport through the official Python SDK.The MCP server source is open source under the MIT License. Windows SAPI is a proprietary component included with Windows; it is not an open-source speech engine.
uv/uvx is recommended for package-based MCP installation.Configure an MCP client to run the published PyPI package:
uvx text-to-speech-mcp
For MCP clients that accept command-based server configuration, use:
command = "uvx"
args = ["text-to-speech-mcp"]
startup_timeout_sec = 30
tool_timeout_sec = 300
enabled = true
Some clients use TOML, JSON, or a graphical settings page. Use
uvx text-to-speech-mcp as the server command and restart the client after
changing its configuration.
git clone https://github.com/engr-faizanali/text-to-speech-mcp.git
cd text-to-speech-mcp
python -m pip install .
Then configure the client to run text-to-speech-mcp directly.
Read arbitrary text:
Use the Text to Speech tool to read aloud: The deployment completed successfully.
Read the final answer:
Use the Text to Speech tool to read your final response aloud before displaying it.
Read visible intermediate progress updates in order:
Use the text_to_speech MCP server's speak_text tool for spoken progress updates.
For every meaningful intermediate update that you display to me:
1. Call speak_text with the exact update text you are about to display.
2. Wait for the call to finish before producing or speaking the next update.
3. Then display the same update in text.
Also call speak_text with the exact final answer before displaying it. Never
narrate hidden reasoning, chain-of-thought, secrets, credentials, raw tool
output, terminal logs, or source code unless I explicitly ask you to read that
content aloud. Do not invoke speech calls in parallel. If the tool is
unavailable, continue normally in text and report the failure once.
The text_to_speech portion is an example client-side server name. Clients may
display a different namespace while keeping the tool name speak_text.
| Field | Value |
|---|---|
| Tool name | speak_text |
| Input | text, required string, 1-50,000 characters |
| Result | Completion message after local playback finishes |
| Ordering | FIFO, one active playback at a time |
| Queue limit | 32 pending requests |
| Network use with SAPI | None |
The tool is model-controlled under MCP. The user decides when to ask the model to call it, and the MCP client may show or require approval for tool calls.
With the default SAPI backend, text is passed from the MCP client to a local Python process and then to Windows speech