voice-announce-mcp
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@voice-announce-mcpDeploy completed successfully. All tests passed."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
voice-announce-mcp
An MCP server that turns a short text summary into spoken audio and plays it locally. Built for Claude Code / Codex to speak a summary of what just happened at the end of a task -- the coding agent writes the summary itself (that's what it's already good at); this server only does text -> speech -> playback.
Uses FreyaTTS (Korean fork, distilled from Qwen3-TTS) for synthesis. Runs on CUDA, Apple Silicon (MPS), or CPU, auto-detected.
Status
Core pipeline (load model -> synthesize -> write wav) verified working
end-to-end on Linux/CPU with a local checkpoint, in a clean venv with only
the declared pyproject.toml dependencies (no manual PYTHONPATH). Not yet
tested on macOS or native Windows, and the audio-playback code for those
platforms is best-effort, not hardware-verified. GPU inference also untested
on this dev box (its driver is older than the CUDA version the default pip
torch wheel needs -- not an issue on a normal desktop/laptop with current
drivers).
Related MCP server: mcp-speak-when-done
How it works
One MCP tool,
announce(text: str). The calling model (Claude Code / Codex) is expected to write its own short (1-3 sentence) summary and pass it in -- this server does no summarization itself.The FreyaTTS model loads once at server startup (~10s) and stays resident, so repeated
announce()calls are fast. This is why it's an MCP server and not a per-call script/Skill.Model source is configurable: a local checkpoint directory, or any Hugging Face repo id that follows FreyaTTS's
config.json+model.safetensorslayout (FreyaTTS.from_pretrainedresolves both).
Configuration (environment variables)
Variable | Default | Notes |
|
| Not published to HF yet. Until it is, point this at a local checkpoint dir converted to |
| auto ( | Override if auto-detection picks wrong |
|
| ODE sampling steps; lower = faster, some quality loss. Untested below 32 so far -- see FreyaTTS's |
|
| Voice identity (FreyaTTS has no speaker embedding -- seed is the voice). |
Install
Non-developers (recommended): one command, no manual venv
Use uv rather than pipx. Both install a
Python CLI into its own isolated environment, but uv can also fetch and
use a specific Python version itself -- the user's python3 doesn't need
to already be a supported version. This matters here: a brand-new Python
(e.g. 3.14) routinely breaks pip/build tooling for packages like this one
that pull in compiled dependencies (torch, etc.), with confusing internal
errors instead of a clean version-mismatch message -- pinning the
interpreter uv uses sidesteps that entirely, regardless of what's already
on the user's machine.
# one-time, if uv isn't already installed:
# macOS/Linux: curl -LsSf https://astral.sh/uv/install.sh | sh
# Windows: powershell -c "irm https://astral.sh/uv/install.ps1 | iex"
uv tool install --python 3.12 "voice-announce-mcp @ git+https://github.com/ummjevel/voice-announce-mcp.git"--python 3.12 tells uv to download and use that interpreter for this
tool specifically (it does not touch or require any system Python). This
pulls in freyatts (and its own dependencies: torch, voxcpm, etc.)
automatically -- freyatts is now a proper pip package (see
FreyaTTS/pyproject.toml) -- so it's a single command with nothing to clone
or convert by hand, as long as VOICE_MCP_MODEL points at a ready-to-use
checkpoint (a published HF repo id, once one exists -- see Known gaps).
Note: the exact uv tool install flags above are our best understanding
of uv's CLI, not yet run against a real uv install end to end -- if the
syntax has drifted, uv tool install --help has the current form. pipx
works too (pipx install git+https://...), just without the
Python-version-pinning safety net -- only use it if the system's default
python3 is already in the >=3.10,<3.14 range this project supports.
Developers (editable install)
cd voice-announce-mcp
python3 -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
pip install -e .Verified (2026-07-24) in a clean venv against a local FreyaTTS checkout:
pip install -e . resolves freyatts and all other dependencies with no
manual PYTHONPATH needed, once both this repo and FreyaTTS's
pyproject.toml commit are pushed -- pip fetches freyatts straight from
github.com/ummjevel/FreyaTTS via git.
Register with Claude Code
claude mcp add voice-announce -- voice-announce-mcpor, with an explicit local model while unpublished:
claude mcp add voice-announce -e VOICE_MCP_MODEL=/path/to/checkpoints/distill_voiceA/final -- voice-announce-mcpRegister with Codex
Codex's MCP config syntax may differ by version -- check
codex mcp --help / the current Codex docs before trusting this verbatim.
As of this writing it's roughly a ~/.codex/config.toml entry:
[mcp_servers.voice-announce]
command = "voice-announce-mcp"
env = { VOICE_MCP_MODEL = "/path/to/checkpoints/distill_voiceA/final" }Audio playback per platform
Platform | Method |
macOS |
|
Windows | PowerShell |
WSL2 (WSLg / Win11) |
|
WSL2 (no WSLg) | Crosses the interop boundary, plays via |
Linux |
|
Known gaps / next steps
Core pipeline verified on Linux/CPU only so far -- next step is a smoke test on real macOS and Windows hardware (playback code especially).
VOICE_MCP_MODELdefault points at an unpublished HF repo. Publish it already converted toconfig.json+model.safetensorsformat (i.e. push thehf/output ofconvert_ckpt.py, not the raw trainingmodel.pt) so end users never need to run the converter themselves.No quality/latency tuning done here yet --
FreyaTTS/README.md's Evaluation section has the levers (ODE step count, model size) ifannounce()turns out too slow in practice.Not yet on PyPI, so
pipx installcurrently meanspipx install git+...(slower first install, rebuilds from source) rather than a prebuilt wheel.
Available Tools
1 toolannounceA
Speak a short summary out loud through the local speakers.
Call this with a very short summary (1-2 lines), written entirely in Korean, of what you just did or found -- write the summary yourself first, then pass only that summary text here. Do not pass long text or non-Korean text; this is for a spoken announcement, not a document.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the disclosure burden and does state the key behavioral trait: this produces spoken audio through local speakers, not a document. It does not cover error behavior, latency, or that audio is externally audible, so it stops short of full transparency.
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?
Front-loaded with the core action, and the operational constraints are useful. However, the 'short / 1-2 lines / not long' and 'Korean / not non-Korean' requirements are restated in several forms, which is slightly redundant.
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?
An output schema exists, so return values need not be described. For a single-required-parameter announcement tool, the description covers purpose, input content rules, and the speech side effect completely.
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?
Schema coverage is 0% (the single 'text' property has no description), so the description must compensate, and it does: it defines content length (1-2 lines), language (entirely Korean), authorship (agent-written summary), and the negative case (no long or non-Korean text).
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?
Specific verb ('Speak ... out loud') plus resource ('short summary') plus delivery channel ('local speakers'). No siblings exist, but the action and its medium are unambiguous without opening the schema.
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?
Tells the agent exactly when to call it ('what you just did or found'), what content to prepare ('write the summary yourself first'), and explicit when-not constraints ('Do not pass long text or non-Korean text'). The workflow ordering is spelled out.
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
v0.1.0- First observed
announce
TDQS
Scored across 1 tool
With only a single tool in the set, there is no possibility of confusion or overlapping purposes. The 'announce' tool has a uniquely clear purpose.
A single tool named 'announce' presents no inconsistency; the name is a clear, readable verb matching its function. There is no mixed convention to penalize.
One tool is minimal but appropriate for a tightly scoped server whose only job is speaking a summary aloud. It is thin, but it earns its place without redundancy.
For a single-purpose announcement server, the surface covers its stated job fully. Minor optional extras like stopping speech or adjusting voice are not required for the core function.
Maintenance
Related MCP Connectors
MCP server for Text-to-Speech
MCP server exposing the AceDataCloud Fish Audio API (text-to-speech with voice conditioning)
AI voice generation: text-to-speech and voice cloning from any MCP client.
MCP server for RiverScript, an AI transcription platform - fetches transcripts shared via a link.
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