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., "@mcp-kokoro-tts speak 'Hello, this is a test of the Kokoro text-to-speech server.'"
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.
Local Kokoro-82M text-to-speech MCP server. When your agent calls speak, it synthesizes speech and plays it on your machine so you can hear the harness talk.
Works with any MCP client: Claude Desktop, Claude Code, Cursor, VS Code, opencode, Cline, and more. One short config block, no API keys — synthesis runs locally with Kokoro-82M.
On first start, the server provisions two things that are not on PyPI: the Kokoro-82M weights (~312 MB) into a local cache, and spaCy's English model (en_core_web_sm) into the same Python environment the server is running in. That second install is required because Kokoro's G2P pipeline loads spaCy, and a uvx / uv tool environment will not have the model unless this package puts it there.
Add to your client's MCP config:
{
"mcpServers": {
"mcp-kokoro-tts": {
"command": "uvx",
"args": ["mcp-kokoro-tts"]
}
}
}Requires Python 3.12 and uv. The first server start provisions Kokoro weights and the spaCy English model automatically.
To pre-download both without starting the MCP server:
uvx mcp-kokoro-tts-provisionRelated MCP server: MCP TTS Server
Add one line to your AGENTS.md / CLAUDE.md / system prompt:
When the user wants to hear something spoken aloud, call the `speak` tool with clear, natural text.speakSynthesizes speech, writes a WAV file, and plays it locally.
Param | Required | Description |
| yes | Text to speak (max 500 chars) |
| no | Voice id (e.g. |
| no | Playback speed multiplier (default |
list_voicesLists available Kokoro voices and the currently selected default.
Resolution order:
TTS_VOICE env var — voice id or absolute .pt path
A file in the package voices/ folder whose name starts with default
First .pt file in voices/ (alphabetical)
The model's bundled af_heart voice
{
"mcpServers": {
"mcp-kokoro-tts": {
"command": "uvx",
"args": ["mcp-kokoro-tts"],
"env": {
"TTS_VOICE": "af_heart"
}
}
}
}Variable | Description |
| Default voice id or absolute |
| Override model cache directory |
| Override Hugging Face hub cache directory |
| Directory for generated WAV files |
| Set to |
| Optional Hugging Face token for faster downloads |
OS | Synthesis | Playback |
macOS | yes |
|
Linux | yes |
|
Windows | yes | PowerShell |
espeak-ng is optional. English works without it; install it for better out-of-vocabulary coverage and some non-English languages.
Tagging a version runs GitHub Actions publish.yml, which uploads to PyPI then the MCP Registry.
Publishing to PyPI uses the repo secret PYPI_TOKEN (a PyPI API token). GitHub trusted publishing can also be configured on the PyPI project; this workflow authenticates with the token so a first release does not depend on pending-publisher matching.
Bump version in pyproject.toml (and server.json if you are not tagging yet)
Commit and tag: git tag v0.1.2 && git push origin v0.1.2
GitHub Actions runs publish.yml:
release — typecheck, test, build wheel/sdist
pypi-publish — upload to PyPI with PYPI_TOKEN
mcp-registry — OIDC → MCP Registry (after PyPI succeeds)
cd mcps-tts
python3.12 -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"
pyright
pytest
python -m mcp_kokoro_ttsApache-2.0. See LICENSE and NOTICE. Kokoro-82M model weights are downloaded separately under their Apache-2.0 license.
List Kokoro voices available to the speak tool and the currently selected default voice. Resolution order: TTS_VOICE env var, voices/default* file, first file in voices/, then the model's default af_heart voice.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does exceptionally well: it reveals the exact resolution order (env var, file, first file, fallback model default), which behaviorally affects what the tool returns. This is precisely the kind of contextual detail an agent needs beyond the schema.
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?
Two sentences, the first clearly scopes the listing and the second enumerates the resolution order. Every phrase adds value and the default-voice resolution rule is stated in a compact, order-relevant manner.
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?
Given the output schema exists, the description does not need to explain return format. It fully explains what this tool does (list voices and default voice) and even how the default is determined, which is the behavioral nuance essential for an agent to pick the right voice via speak.
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?
The tool takes zero parameters, so the schema is already complete (100% coverage) and there is nothing for the description to elaborate on. Baseline 4 applies since there is simply no parameter information needed.
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?
States a specific verb ('List') and resource ('Kokoro voices available to the speak tool') and also includes the currently selected default voice. The mention of 'speak tool' clearly distinguishes it from the sibling speak tool, making its function unambiguous.
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?
The description implies this tool should be used to discover available voice options before using the speak tool, especially because it names the speak tool as the consumer. It clearly states the resolution order for the default voice, giving immediate context, but it does not explicitly say when not to use it or mention alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Synthesize speech with Kokoro-82M and play it on the user's machine. Call this when the user wants the harness to talk, read something aloud, or hear a spoken response. Always pass clear, natural text to speak. Default voice resolution: TTS_VOICE env var, voices/default* in the package, first file in voices/, then the bundled af_heart voice from the model.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | ||
| speed | No | ||
| voice | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden, and it makes a reasonable effort. It discloses the side effect of audio playback ('play it on the user's machine') and thoroughly documents the voice resolution fallback order (TTS_VOICE env var, voices/default*, first file in voices/, then af_heart). However, it doesn't disclose potential blocking behavior, absence of audio hardware, or other side effects that could be expected given the nature of the tool.
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?
Four sentences, with the core purpose front-loaded first and the resolution detail last. Every sentence earns its place — the usage guidance and voice resolution order are both essential for the agent. The voice resolution sentence is dense but packs critical behavioral information. A slight trim could occur since the resolution order could be seen as implementation detail.
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?
The description captures the core essence—synth and play, when to trigger, how voice is resolved—which is solid for a 3-parameter tool. No annotations or schema descriptions exist to offload this burden to. Missing context includes behavior of the speed parameter, the relationship to the sibling list_voices, and failure modes. Given the total absence of annotations and schema descriptions, the description could do more to guide an agent.
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?
With 0% schema description coverage, the description needed to compensate, and it partially does. It adds value for the text parameter ('clear, natural text to speak') and implicitly for the voice parameter by explaining the resolution order. However, the speed parameter is entirely unexplained (what units, what range), and the voice format is only hinted at. Given the extremely low schema coverage, this partial compensation keeps it at a decent but not exceptional level.
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?
The description opens with a specific verb and resource ('Synthesize speech with Kokoro-82M and play it on the user's machine') that clearly identifies what the tool does. It goes further to elaborate intended use cases ('when the user wants the harness to talk, read something aloud, or hear a spoken response'). It doesn't explicitly contrast itself with the sibling list_voices, which keeps it just short of a 5.
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?
The description gives explicit when-to-use guidance with concrete examples ('when the user wants the harness to talk, read something aloud, or hear a spoken response'). It includes a practical instruction to 'Always pass clear, natural text to speak.' However, it stops short of explicitly naming alternatives (e.g., list_voices) for comparison or stating when not to use it, so it lacks the explicit exclusions of a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.1.4list_voicesspeakScored across 2 tools
The two tools have clearly distinct purposes: one performs speech synthesis ('speak'), the other lists available voices ('list_voices'). No ambiguity between them, as they serve complementary roles in the TTS workflow.
Both tool names follow an imperative verb style ('speak', 'list_voices'), which is concise and predictable. 'list_voices' uses a verb_noun pattern, but the naming is consistent in tone and clarity for a small set.
With only 2 tools, the server is minimal yet well-scoped for its purpose: a text-to-speech harness. The count is appropriate; additional tools would likely be redundant.
The core operation (speak) is present, and the companion list_voices provides necessary context for voice selection. Minor gaps exist, such as no pause/stop or voice configuration tool, but these are not critical for basic TTS functionality.
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