QuickBuild token-saver tools + x402

Text to speech (WAV)

text_to_speech

Read-onlyIdempotent

[PAID $0.005 USDC per call via x402 on Base; errors are free] Turn text (up to 2000 chars) into spoken audio with MeloTTS on Cloudflare Workers AI, in English, Spanish, French, Chinese, Japanese or Korean. Returns JSON with audio_base64 + mimeType (audio/wav, 44.1 kHz mono), or the raw audio bytes with format=wav. Good for voice replies, narration, accessibility and language practice. Text with sexual, hateful or violent content is refused (HTTP 400, not charged). All params optional: with none it speaks a short English sample. GET with query params or POST a JSON body.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "text_to_speech"
  }
}

Input Schema

Name	Required	Description	Default
`lang`	No	Language of the text / voice (default en).
`text`	No	Text to speak (max 2000 chars). Optional: defaults to a short English sample.

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 20, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive), yet the description adds substantial non-derivable behavior: per-call pricing of $0.005 USDC via x402 on Base, free errors, the content-refusal policy with HTTP 400 and no charge, the 2000-char limit, and the default no-param sample behavior.

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?

Dense but front-loaded: pricing and the core transform lead, then output formats, use cases, refusal policy and defaults. Every sentence carries information, though the parenthetical-heavy phrasing makes it slightly harder to scan than it could be.

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

Completeness5/5

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

There is no output schema, and the description compensates fully by specifying the return shape (JSON with audio_base64 + mimeType audio/wav, 44.1 kHz mono) or raw bytes with format=wav. Pricing, refusal behavior, defaults and transport are all covered, so an agent can call this correctly without further context.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3, but the description adds real meaning: the 2000-char cap, the fact that both params are optional and default to a short English sample, and the GET-query-params vs POST-JSON-body invocation styles. It also references a 'format=wav' option that does not appear in the schema, a small inconsistency.

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?

States a specific verb+resource ('Turn text ... into spoken audio'), names the engine (MeloTTS on Cloudflare Workers AI) and the six supported languages. No sibling tool does text-to-speech, so the purpose is unambiguous at a glance.

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 use cases (voice replies, narration, accessibility, language practice) which tell the agent when this tool fits. It does not state when NOT to use it or point at an alternative, but no sibling overlaps, so the omission is minor.

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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