使用 macOS 内置say命令提供文本转语音功能的 MCP 服务器。
macOS（使用内置的say命令）
Node.js >= 14.0.0
Related MCP server: Edge TTS MCP
将以下内容添加到您的 MCP 设置配置文件中：
{
"mcpServers": {
"say": {
"command": "node",
"args": ["/path/to/say-mcp-server/build/index.js"]
}
}
}npm install say-mcp-serverspeak工具提供对 macOS 文本转语音功能的访问，并具有广泛的自定义选项。
使用 macOS 文本转语音功能大声朗读文本。
参数：
text （必填）：文字朗读。支持：
纯文本
停顿的基本标点符号
自然停顿换行符
[[slnc 500]] 静默 500 毫秒
[[rate 200]] 用于改变文本中间的速度
[[volm 0.5]] 用于在文本中间更改音量
[[emph +]] 和 [[emph -]] 用于强调
[[pbas +10]] 用于音调调整
voice （可选）：使用的语音（默认：“Alex”）
rate （可选）：每分钟的说话速度（默认值：175，范围：1-500）
background （可选）：在后台运行语音以允许进一步的 MCP 交互（默认值：false）
语音调制：
use_mcp_tool({
server_name: "say",
tool_name: "speak",
arguments: {
text: "[[volm 0.7]] This is quieter [[volm 1.0]] and this is normal [[volm 1.5]] and this is louder",
voice: "Victoria"
}
});动态利率变化：
use_mcp_tool({
server_name: "say",
tool_name: "speak",
arguments: {
text: "Normal speed [[rate 300]] now speaking faster [[rate 100]] and now slower",
voice: "Fred"
}
});重点和音调：
use_mcp_tool({
server_name: "say",
tool_name: "speak",
arguments: {
text: "[[emph +]] Important point! [[emph -]] [[pbas +10]] Higher pitch [[pbas -10]] Lower pitch",
voice: "Samantha"
}
});附有旁注的搜索：
// Search for a topic and have the results read aloud
const searchResult = await use_mcp_tool({
server_name: "marginalia-mcp-server",
tool_name: "search",
arguments: { query: "quantum computing basics", count: 1 }
});
await use_mcp_tool({
server_name: "say",
tool_name: "speak",
arguments: {
text: searchResult.results[0].description,
voice: "Daniel",
rate: 150
}
});附有 YouTube 成绩单：
// Read a YouTube video transcript
const transcript = await use_mcp_tool({
server_name: "youtube-transcript",
tool_name: "get_transcript",
arguments: {
url: "https://youtube.com/watch?v=example",
lang: "en"
}
});
await use_mcp_tool({
server_name: "say",
tool_name: "speak",
arguments: {
text: transcript.text,
voice: "Samantha",
rate: 175
}
});具有多种动作的背景演讲：
// Start long speech in background
await use_mcp_tool({
server_name: "say",
tool_name: "speak",
arguments: {
text: "This is a long speech that will run in the background...",
voice: "Rocko (Italian (Italy))",
rate: 69,
background: true
}
});
// Immediately perform another action while speech continues
await use_mcp_tool({
server_name: "marginalia-mcp-server",
tool_name: "search",
arguments: { query: "parallel processing" }
});使用 Apple Notes：
// Read notes aloud
const notes = await use_mcp_tool({
server_name: "apple-notes-mcp",
tool_name: "search-notes",
arguments: { query: "meeting notes" }
});
if (notes.length > 0) {
await use_mcp_tool({
server_name: "say",
tool_name: "speak",
arguments: {
text: notes[0].content,
voice: "Karen",
rate: 160
}
});
}例子：
use_mcp_tool({
server_name: "say",
tool_name: "speak",
arguments: {
text: "Hello, world!",
voice: "Victoria",
rate: 200
}
});列出系统上所有可用的文本转语音的声音。
例子：
use_mcp_tool({
server_name: "say",
tool_name: "list_voices",
arguments: {}
});将以下内容添加到您的 MCP 设置配置文件中：
{
"mcpServers": {
"say": {
"command": "node",
"args": ["/path/to/say-mcp-server/build/index.js"]
}
}
}macOS（使用内置的say命令）
Node.js >= 14.0.0
麻省理工学院
List available text-to-speech voices
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action but doesn't describe what the output looks like (e.g., list format, voice attributes), whether it's cached, or any rate limits. This leaves significant gaps for an agent to understand the tool's 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 a single, efficient sentence with no wasted words. It's appropriately sized for a simple tool and front-loads the core purpose immediately.
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, parameterless tool with no output schema, the description is minimally adequate. However, it lacks details about the output format or behavioral traits, which would help an agent use it correctly. Without annotations, the description should do more to compensate.
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 0 parameters, and schema description coverage is 100% (though empty). The description doesn't need to add parameter details, so it meets the baseline expectation for a parameterless tool without compensation 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?
The description clearly states the verb ('List') and resource ('available text-to-speech voices'), making the tool's purpose immediately understandable. It doesn't explicitly differentiate from its sibling 'speak', but the distinction is reasonably implied (listing vs. using voices).
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?
No guidance is provided on when to use this tool versus its sibling 'speak' or any alternatives. The description only states what it does, not when it should be selected over other options.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Use macOS text-to-speech to speak text aloud
| Name | Required | Description | Default |
|---|---|---|---|
| background | No | Run speech in background to unblock further MCP interaction | |
| rate | No | Speaking rate (words per minute) | |
| text | Yes | Text to speak | |
| voice | No | Voice to use (e.g., "Alex", "Victoria", "Daniel") | Alex |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses the core behavior (speaking text aloud) but lacks details about permissions needed, whether speech blocks interaction (though the 'background' parameter hints at this), error conditions, or what happens on completion. The description doesn't contradict any annotations since none exist.
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 a single, efficient sentence that front-loads the core purpose. Every word earns its place with no redundancy or unnecessary elaboration.
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 tool with 4 parameters, 100% schema coverage, and no output schema, the description provides adequate context about what the tool does but lacks details about behavioral aspects like error handling, platform dependencies, or interaction blocking. It's minimally complete but could be more informative given the absence of annotations.
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 100%, so the schema already documents all four parameters thoroughly. The description adds no parameter-specific information beyond what's in the schema, meeting the baseline for high schema coverage.
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 clearly states the specific action ('speak text aloud'), the technology used ('macOS text-to-speech'), and the resource ('text'). It distinguishes from the sibling tool 'list_voices' by focusing on speech output rather than voice enumeration.
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 usage context (macOS text-to-speech functionality) but doesn't explicitly state when to use this tool versus alternatives or any prerequisites. It mentions the sibling tool 'list_voices' only indirectly through the voice parameter example.
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.
v1.0.0list_voicesspeakScored across 2 tools
The two tools have clearly distinct purposes: list_voices retrieves available options, while speak performs the core text-to-speech action. There is no overlap or ambiguity between them, making it easy for an agent to select the correct tool.
Both tools follow a consistent verb_noun pattern (list_voices and speak), with clear, descriptive names that align with their functions. There are no deviations or mixed conventions in the naming style.
With only two tools, the server feels thin for a text-to-speech domain. While it covers basic functionality (listing and speaking), it lacks operations like stopping speech, adjusting voice parameters, or managing speech queues, which are common in such systems.
The tool surface is severely incomplete for a text-to-speech server. It provides list and speak functions but misses essential operations such as pausing, resuming, or canceling speech, and offers no control over voice settings like rate or volume, limiting agent workflows.
Text to speech for your AI. Your AI can send text to Doc Player to read it aloud. You will see a reader window with the text and you can control the playback sentence by sentence. Find an example here: https://documentplayer.com/connect-ai/
Text-to-speech API: neural voices, pay-per-credit in Bitcoin sats via BTCPay.
AI voice generation: text-to-speech and voice cloning from any MCP client.
Audio for your agent: transcribe, speak, translate, summarise, plus sound effects and music.
