Claude Code が作業している間に話しかける — Jarvis スタイルの音声コーディング。 ノイズが多いのはコードではなく、あなたの声です。
Claude は短い要約を読み上げます。常時稼働のリスナーがあなたの音声をメッセージに変換し、Claude はタスクの途中でも停止することなくそれを受け取ります — プッシュ送信やトランスクリプトのコピー＆ペーストは不要です。キーボードから離れても、エージェントを操作し続けられます。
中断なしのフロー — Claude が作業中に話しかけると、あなたの言葉はテキストボックスではなく実行中のセッションに届きます。
ハンズフリーのレビュー — Claude が調査結果を読み上げ、あなたは部屋の反対側から答えます。
ライブの「タクティカル HUD」ダッシュボード — 再生/呼び出し対応の会話ログ、リアルタイムオシロスコープ、ミュートボタン、コストとレイテンシを一目で確認できます。
エージェントごとのキャラクター — すべてのエージェントの音声、速度、性格をダッシュボードから調整できます。
ファイル設定は不要 — API キー、デバイス、言語、プッシュトークはすべて UI 内にあり、永続化されます。
あなたの声を遮らない — 同時に流れるのは常にひとつの音声だけ。聞き逃した音声は UNHEARD として待機し、CATCH UP ボタンで再生できます。
音声認識と音声合成は Grok (xAI) Voice API 上で動作します — 実際には非常に安価で、少額の一回限りの予算で数か月間毎日使えます。
Related MCP server: Elba MCP Server
バックエンドはハードウェア不要の Docker イメージ（noisy/noisy-coding）として提供されます。ダッシュボードのブラウザタブがマイクとスピーカーになります。必要なのは Docker とブラウザだけ — Python も git も環境変数も不要です。
# terminal: marketplace + plugin in one line
claude plugin marketplace add noisy/noisy-coding && claude plugin install noisy-coding@noisy# inside Claude Code (new session):
/noisy-coding:setupセットアップコマンドは公開イメージを起動し、最初の接続を案内します。その後、http://127.0.0.1:8765 でブラウザ上から仕上げます：xAI API キー（console.x.ai）を貼り付け、琥珀色の ENABLE TAB AUDIO バナーをクリックしてください — その 1 クリックでタブがマイクとスピーカーになります。タブを開いたまま、ただ話しかけるだけです。
Claude Code の中から操作したいですか？ 同じことを 4 つのコマンドで行えます：
/plugin marketplace add noisy/noisy-coding →
/plugin install noisy-coding@noisy → /reload-plugins →
/noisy-coding:setup。
その他のセットアップ — プラグインなしの素の Docker、ハードウェアのマイク/スピーカーを使うネイティブインストール、リモートホスト、すべての設定項目 — は docs/INSTALL.md にあります。
すべての音声ロジックは 1 つの リスナーデーモン に集約されています — マイク、再生キュー、スピーカーを単独で管理します。MCP サーバーは speak リクエストを転送するだけの軽量メッセンジャーです。Claude Code のフックが、書き起こした音声をセッションに戻します（docs/hooks.md 参照）。
mic (hardware or browser tab via WS :8766)
-> VAD -> Grok STT -> transcript queue -> HTTP :8765
^ polled by Claude Code hooks
speak (MCP, stdio or HTTP :8767) -> POST /speak -> daemon queue
-> Grok TTS -> speakers (hardware or browser tab)ツール | 説明 |
|
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| ファイア・アンド・フォーゲット方式：即座に戻り、バックグラウンドで再生します。 |
| このエージェントの音声を意図的に切り替えます（永続化され、ダッシュボードに表示されます）。 |
| 組み込みの Grok 音声（ |
docs/INSTALL.md — 素の Docker、ネイティブインストール、リモートホスト、環境変数、開発コマンド
docs/hooks.md — Claude があなたの声をどう聞くか
docs/ports.md — 各ポートの用途
docs/local-development.md — noisy-coding 自体を開発するための情報
MIT © Krzysztof Szumny
Speak a quick spoken update WITHOUT waiting for it to finish.
Fire-and-forget: use this to tell the user what you just did and keep
working ("done with X, moving on") — it returns immediately and plays in
the background, queued behind any current speech. Use speak instead when
you are asking a question or otherwise waiting for the user's reply.
Like speak, it carries only text — voice/speed/language live in the daemon.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes |
| 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 full behavioral burden. It discloses that the tool returns immediately, plays in the background, queues behind current speech, and carries only text. This is thorough and gives the agent a clear model of runtime behavior.
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?
The description is compact and well-structured, front-loading the core behavior in the first sentence and building on it with usage guidance. Every sentence adds value, with no redundancy or fluff.
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?
For a simple one-parameter tool with an output schema present, this description covers purpose, usage, behavior, and parameter meaning fully. It is complete and leaves no significant gaps for an agent to operate correctly.
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 single 'text' parameter has zero schema documentation, but the description compensates by stating the tool 'carries only text' and that voice/speed/language live in the daemon, clarifying that the text parameter is the complete content with no hidden options.
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: 'Speak a quick spoken update WITHOUT waiting for it to finish.' It directly distinguishes the tool from its sibling 'speak' by emphasizing the fire-and-forget behavior, making the purpose unmistakable.
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?
Explicit guidance is provided: use this for quick updates while continuing work, and 'Use `speak` instead when you are asking a question or otherwise waiting for the user's reply.' This clearly states when to use this tool versus an alternative.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Deliberately switch this agent's speaking voice from now on.
Updates your character in the listener daemon: the dashboard shows the new voice and every later speak/announce uses it (it also persists across restarts). Use list_voices to see the options. Speak itself carries no voice information — this call is the only way to change how you sound, so use it consciously (e.g. when the user asks for it).
Args: voice_id: Which voice to switch to. speaker: Move a named SPEAKER's voice instead of your own — the personas you address with speak(speaker=...). A voice already held by someone else is refused rather than duplicated, so two speakers never become indistinguishable by ear.
| Name | Required | Description | Default |
|---|---|---|---|
| speaker | No | ||
| voice_id | Yes |
| 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 full responsibility for behavior. It discloses that the change persists across restarts, affects the listener daemon, applies to future speak/announce calls, and that duplicate voices are refused to prevent indistinguishable speakers. This dramatically exceeds baseline explanation.
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?
The description is organized in clear sections with the proactive sentence first, followed by practical details and then argument semantics. Every sentence adds relevant information; there is no redundancy or filler.
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?
This is a persistent state-changing tool with a lightness of given structured metadata. The description covers side effects, persistence, usage context, the valid arguments, and the duplicate-owner failure behavior. There is enough to invoke intentionally and correctly.
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 schema provides no parameter descriptions (0% coverage), so the description is the only source of meaning. It explains voice_id as the voice to switch to and elaborates on speaker, including its use for named SPEAKER-defined personas and the duplicate-refusal behavior. It could be slightly stronger on voice_id's allowed values, but overall it compensates well.
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: 'Deliberately switch this agent's speaking voice from now on' and details the state change. It distinguishes itself from the sibling speech tools by explicitly stating that speak carries no voice information and that this is the only way to change how one sounds.
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 clear context: use this call when the user asks for a voice change, use list_voices to see options, and don't expect speak/announce to carry voice information. This effectively tells the agent when to use this tool versus its siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List the Grok TTS voices available for the speak tool.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so description carries full burden. 'List' clearly signals a read-only operation and adds the scope 'for the speak tool'. It does not disclose return format or dynamic behavior, but for a simple listing tool this is adequate.
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?
A single concise sentence with a clear verb and object. No redundant information, every word adds value.
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 tool is simple with zero parameters and an output schema present. The description fully covers purpose and intended use, making it complete for its complexity.
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 has zero parameters, so the baseline score of 4 applies. The description correctly adds no unnecessary parameter details.
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 'List' with resource 'Grok TTS voices' and clarifies they are for the speak tool, distinguishing from sibling tools that speak or change voice.
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?
Implies use before speak to discover available voices, and the phrase 'available for the speak tool' gives context. No explicit exclusions or alternatives, but the purpose is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Speak a short message aloud to the user through their speakers.
Use this to deliver a spoken TL;DR alongside (not instead of) your written answer: 1-3 conversational sentences summarizing the outcome, a finding, or a question. Never read code, file paths, or long explanations aloud.
You send only the text: voice, speed and language belong to the daemon (the user controls them on the dashboard). To deliberately switch your voice, call change_voice.
Concurrent speech is serialized: by default a new call WAITS for the current utterance to finish (queued), and for the user to finish speaking. Set interrupt=True to cut the current utterance off and speak immediately — use it only when your previous words are now stale (e.g. the user corrected you mid-answer).
Args: text: What to say. Plain conversational prose. Mark the key words the listener must catch with markdown bold (like this) — they get vocal emphasis and show bold on the live dashboard. Also supports inline speech tags like [pause] or [laugh] and wrapping tags like text. interrupt: Cut off any utterance currently playing and speak now. speaker: ONLY for subagents. If you are a subagent (Task/Agent tool), pass your role name here (e.g. "researcher") — the dashboard shows the message under that name with its own portrait, and the daemon gives you a stable voice distinct from the main agent's. The main agent must leave this empty.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | ||
| speaker | No | ||
| interrupt | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden and does so thoroughly: it discloses serialized/concurrent speech queuing, interrupt semantics, that only text is sent (voice/language controlled by daemon), and speaker-role constraints for subagents. This is rich behavioral context 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?
The description is long but well-structured: purpose first, then usage guidelines, then behavioral notes, then parameter details. Every section earns its place, though the opening sentence and the second sentence partially overlap in saying it's a short spoken message.
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 tool's complexity (queueing, interrupt, subagent speaker, formatting tags), no annotations, and an output schema, the description covers all necessary context: when to use, exclusions, behavior, parameter semantics, and related tools. It is a complete standalone guide.
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 description coverage is 0%, so the description must compensate and does. It explains text formatting (markdown bold, [pause], [laugh], <soft> tags), the exact meaning of interrupt, and the speaker parameter's subagent-only usage with dashboard implications.
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 immediately states 'Speak a short message aloud to the user through their speakers,' a specific verb+resource pairing. It further scopes usage to 'a spoken TL;DR alongside (not instead of) your written answer' and explicitly contrasts with change_voice, distinguishing it from sibling tools.
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?
Provides explicit when-to-use guidance: deliver 1-3 conversational sentences summarizing outcome/finding/question, never read code/paths/long explanations. It also names the alternative change_voice for switching voices and explains interrupt behavior, giving clear conditions for interrupt=True.
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.
v2.16.0change_voice1 field changedInput schema / properties / speakerAdded value: +{
+ "default": "",
+ "title": "Speaker",
+ "type": "string"
+}v2.13.4announcechange_voicelist_voicesspeakScored across 4 tools
The set is mostly distinct: speak delivers a blocking utterance, announce is fire-and-forget, change_voice and list_voices have clearly separate roles. speak and announce both produce speech, so their overlap could cause an agent to misselect when the blocking behavior matters, but the descriptions offer strong guidance.
Naming is simple and readable with all verbs as commands, but it mixes one-word verb names (speak, announce) with verb_noun patterns (change_voice, list_voices). This minor inconsistency is not confusing and the style remains predictable.
Four tools is well-scoped for a voice/speech server: each tool covers a necessary function—speaking, quick updates, voice switching, and voice enumeration. There is no bloat or obvious missing core capability for the stated purpose.
The tool set covers the main lifecycle of spoken interaction: speak, gently tell what you're doing, switch voices persistently, and discover available voices. One possible gap is lack of a way to query the currently active voice, but this is a minor issue that does not block common workflows.
Real-time chat hub for AI agents — Claude Code, Cursor, Cline, Codex over MCP or REST.
Real-time chat for AI agents. Claude Code, Cursor, Cline and Codex join channels over MCP.
Source-checked CLI guides and model-aware planning for Claude Code, Codex, and Grok Build.
Coding agents from Claude Code, Cursor and Codex claim jobs and lock files on one shared board.
