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charlesmooredev

speak-to-me-mcp

speak

Converts text into audible speech on the user's machine so Claude can announce completions, summarize changes, or narrate explanations. Returns immediately without waiting for playback.

Instructions

Speak text aloud using text-to-speech. Use this to give Claude a voice — summarize changes, announce completions, or narrate explanations audibly. Audio plays on the user's machine and the tool returns immediately without waiting for speech to finish.

Default engine is macOS system TTS (free, offline). Set engine to "openai" for higher quality (requires OPENAI_API_KEY env var).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe text to speak aloud. Keep it concise — aim for 1-3 sentences summarizing what happened.
voiceNoOverride voice name. Leave unset to use the system default (Siri). For openai: alloy, ash, ballad, coral, echo, fable, nova, onyx, sage, shimmer.
engineNoTTS engine: "system" (macOS say, free) or "openai" (premium, requires OPENAI_API_KEY). Defaults to session config.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.0

TDQS

A4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the burden and does well: it discloses non-blocking behavior ('returns immediately without waiting for speech to finish'), that audio plays on the user's machine, the default offline engine, and the OPENAI_API_KEY requirement. It omits failure behavior (e.g., what happens if the key is missing) and whether speech can be interrupted.

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?

Front-loaded purpose in the first clause, then usage, then behavior. Three short paragraphs with no filler; each sentence contributes routing or behavioral information.

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

Completeness4/5

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

For a 3-param, no-annotation, no-output-schema tool, the description covers purpose, usage, non-blocking behavior, engine choice, and the API-key prerequisite. Only error/failure handling is left uncovered, which is a minor gap.

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?

Schema description coverage is 100%, so the schema already documents all three parameters, making 3 the baseline. The description adds the default engine and the free-vs-premium tradeoff, but it also states 'Default engine is macOS system TTS' while the schema says 'Defaults to session config' – a mild inconsistency rather than a clarifying addition.

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

Purpose4/5

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

States a specific verb and resource: 'Speak text aloud using text-to-speech.' That is unambiguous and distinct from the sibling tools (list_voices, configure_speech, speech_style), but the description never names those siblings or clarifies the boundary, so an agent must infer the routing.

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?

Gives concrete when-to-use context: 'summarize changes, announce completions, or narrate explanations audibly.' That is clear positive guidance, but there is no exclusion guidance (e.g., when not to speak, or to use configure_speech first).

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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