io.github.Engr-FaizanAli/text-to-speech

Text to Speech MCP Server

<!-- mcp-name: io.github.Engr-FaizanAli/text-to-speech -->

    

Give your AI assistant a voice — locally, with no API key, no account, and no cloud service.

Text to Speech is an open-source Model Context Protocol (MCP) server that lets AI assistants read text aloud on the user's computer. It uses the speech synthesizer already present on the host operating system, so nothing you ask it to say ever leaves your machine.

Runs on Windows, macOS, and Linux. Install is one line:

uvx text-to-speech-mcp

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.

Why this one

Most text-to-speech MCP servers wrap a cloud API, which means an account, a key, per-character billing, and your text leaving the machine. This one uses the speech engine your operating system already ships, so it works offline, costs nothing, and keeps text local — which matters if you work anywhere that regulates where data may go.

It also ships an agent narration skill, so an assistant knows how to narrate, not just that it can.

Features

• Local playback through the platform's built-in synthesizer by default:

Windows SAPI, macOS say, or espeak-ng on Linux.

• No cloud API and no API key for the default setup.
• FIFO playback: concurrent requests are spoken one at a time, in order.
• Blocking tool completion: each call returns after its audio finishes.
• Bounded input and queue sizes to prevent unbounded resource use.
• Temporary generated WAV files are removed after playback by default.
• Standard MCP stdio transport through the official Python SDK.
• Optional Piper, Transformers MMS, and local HTTP backends for advanced users.

The MCP server source is open source under the MIT License. Windows SAPI and the macOS say command are proprietary components of their operating systems; they are not open-source speech engines. espeak-ng is separately licensed open-source software.

Requirements

• Python 3.10 or newer.
• An MCP client that supports stdio MCP servers.
• uv/uvx is recommended for package-based MCP installation.

Per platform, for the zero-configuration default:

| Platform | Synthesis | Playback | Extra install | | --- | --- | --- | --- | | Windows 10/11 | SAPI via PowerShell | System.Media.SoundPlayer | None | | macOS | say | afplay | None | | Linux / other Unix | espeak-ng or espeak | aplay, paplay, play, or ffplay | espeak-ng and one player |

On Debian or Ubuntu that is typically:

sudo apt install espeak-ng alsa-utils

Set TEXT_TO_SPEECH_BACKEND or TEXT_TO_SPEECH_PLAYER to override either choice. If a required command is missing, the server reports which one and how to install it rather than failing silently.

Install

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.

Install from source

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.

Prompt Examples

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.

Tool Contract

| 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 a built-in backend | 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.

Privacy

With any of the built-in backends, text is passed from the MCP client to a local Python process and then to the operating system's speech components. It is not sent to this project, an external API, or a cloud TTS provider. Generated WAV files are written to a text-to-speech-mcp directory inside the system temporary directory (%TEMP% on Windows, /tmp on macOS and Linux) and deleted after playback unless TEXT_TO_SPEECH_KEEP_AUDIO=true is set.

The http backend is the exception: whether text leaves the machine depends entirely on the endpoint you configure.

Do not ask an AI assistant to speak secrets, credentials, private keys, hidden reasoning, or sensitive tool output.

Optional Backends

The default requires no configuration. TEXT_TO_SPEECH_BACKEND is unset and the server selects sapi, say, or espeak to match the host platform.

To pin one explicitly, or to use a backend that is not built into the OS, set TEXT_TO_SPEECH_BACKEND to sapi, say, espeak, piper, transformers_mms, or http. The last three require their own local model, binary, Python dependencies, or endpoint. TEXT_TO_SPEECH_FALLBACK_BACKEND names a second backend to try if the first fails. See backend configuration.

Agent Narration Skill

A speech tool alone does not tell an assistant when or how to speak. Left to improvise, agents narrate hidden reasoning, skip the parts you actually needed, or read a paraphrase instead of what is on screen.

skills/project-tts-responder/SKILL.md is a ready-made narration policy built on speak_text. Copy it into your project's .claude/skills/ directory:

| Mode | Behaviour | | --- | --- | | Batch (default) | One playback at the end of a turn, covering every visible update plus the final answer | | Streaming | Narrate each update as it appears — good for demos and walkthroughs | | Read on request | Read a named file or block of text verbatim |

It also handles the parts that are easy to get wrong:

• *Interactive questions are narrated before the picker opens.* An

interactive question tool is itself the pause, and its options live in the tool's parameters rather than in visible text — so any rule that narrates "once the options are visible" fires only after the user has already answered. This is the most common way narration silently fails.

• Speaks exactly what is on screen, never a paraphrase.
• Never speaks hidden reasoning, secrets, credentials, or raw tool output.
• One playback call per turn, never parallel, with defined behaviour when a

call fails.

The skill applies when you ask for audio. To make a project narrate every response, say so in that project's own agent instructions — for example "narrate every response in Batch mode unless I opt out".

MCP Compatibility

• MCP transport: stdio
• MCP tool implementation: official Python MCP SDK
• Registry metadata: server.json using the 2025-12-11 schema
• Package registry: PyPI
• Registry ownership marker: this README's mcp-name comment
• Registry namespace: io.github.Engr-FaizanAli/text-to-speech

License

MIT. See LICENSE.