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voice-summary

voice-summary

by eduplopez
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

speak_summary

Reads a summary aloud through system speakers, letting users hear recent code actions instead of reading full responses.

Instructions

Reads a summary out loud. Match its length to the work done: one short sentence for a small change, a fuller summary for a large task. First person, no literal code, no credentials or personal data.

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": "speak_summary"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

A3.5/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=false, openWorldHint=true, idempotentHint=false and destructiveHint=false, so the safety profile is covered. The description adds safety-relevant content constraints ('no literal code, no credentials or personal data') and tone ('first person'), which is genuine value. It does not describe playback behavior, blocking semantics, or error conditions, so it remains a moderate addition.

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

Conciseness4/5

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

The purpose is front-loaded in the first sentence, followed by compact, non-redundant guidance. Every sentence carries information and there is no filler, though the two clauses could be organized slightly more tightly.

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?

An output schema exists, so return-value explanation is unnecessary. For a one-parameter TTS tool, the description covers purpose and content rules adequately; the only real gap is explicit routing relative to the sibling configuration tools.

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 coverage is 0% and the single 'text' parameter is undocumented in the schema, so the description must compensate. It does provide meaningful content semantics – length scaling, first person, and prohibited content – which constrains what the value should be. It still omits format, length limits, and any concrete syntax.

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?

The description states a specific verb and resource: 'Reads a summary out loud.' This clearly distinguishes the speaking action from the sibling tools set_language and list_voices, which handle voice configuration. It stops short of explicitly naming those siblings, but the core purpose is unambiguous.

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

Usage Guidelines3/5

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

It gives content guidance ('Match its length to the work done: one short sentence for a small change, a fuller summary for a large task') rather than tool-selection guidance. There is no explicit statement of when to call this versus a plain text output, nor any exclusion condition, so usage is only implied.

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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