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mac-say-mcp

mac-say-mcp

by viktor-silakov
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

tts_speak

Speak any text aloud instantly using macOS text-to-speech. Adjust speaking rate and choose voice as needed.

Instructions

Convert text to speech and play it immediately

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "text": "<text>"
    },
    "name": "tts_speak"
  }
}

Input Schema

NameRequiredDescriptionDefault
rateNoSpeaking rate in words per minute (default: 200)
textYesText to convert to speech
voiceNoVoice to use (optional, default: Alex)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.3

TDQS

A3.6/5.0
Behavior2/5

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 only states 'play it immediately', omitting details about potential audio output, blocking behavior, errors, or return values. This is insufficient for an agent to anticipate side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, concise sentence that communicates the essential function without any wasted words. It is perfectly sized for the simplicity of the tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations, no output schema, and a simple one-line description, the tool lacks critical context such as whether 'play' produces audible output, how the agent will know if the text is too long, or confirmation of completion. The description is minimal and leaves significant gaps for an AI agent to use it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema provides full descriptions for all three parameters, so the baseline is 3. The description adds no additional semantic detail about parameters beyond what the schema already states, like the default voice or rate constraints.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Convert text to speech') and the immediate behavior ('play it immediately'), which distinguishes it from sibling tools like tts_save_audio. It is specific about both the resource and the scope.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for immediate playback with 'play it immediately', which provides clear context for when to use the tool. However, it does not explicitly mention alternatives or when not to use it, stopping short of full guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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