Drizz Voice Generator

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batch_text_to_speech

Convert multiple texts to speech in parallel. Assign each item its own voice, filename, and preset, with automatic text enhancement for natural pauses.

Instructions

Convert multiple texts to speech in parallel. Each item can have a different voice and filename. Supports presets and auto text enhancement.

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": {
      "items": "<items>"
    },
    "name": "batch_text_to_speech"
  }
}

Input Schema

Name	Required	Description	Default
`items`	Yes	Array of text-to-speech items to process in parallel

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Aug 9, 2026

TDQS

A3.6/5.0

Behavior2/5

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

No annotations are provided, so the description must carry the full burden. It discloses parallel processing and per-item configuration, but omits crucial behavior such as return format, error handling, rate limits, or where output files are stored. For a batch operation, this is a significant transparency gap.

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?

Two concise sentences with no redundancy. The first sentence front-loads the core action and parallelism; the second highlights key per-item features. Every word adds value.

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?

Despite a rich schema, the description is too brief for a batch tool with no annotations and no output schema. It lacks essential context about what the tool returns (e.g., file paths, IDs), how partial failures are handled, or any resource limitations. This makes it incomplete for reliable agent use.

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 100%, with all nested properties described. The description adds a summary of some parameters (voice, filename, presets, auto enhancement) but does not provide deeper meaning beyond the schema. It confirms per-item variability, which the schema already conveys.

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 tool converts multiple texts to speech in parallel, with per-item voice and filename customization. This distinguishes it from the sibling text_to_speech tool, which presumably handles a single text.

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 phrase 'in parallel' and 'multiple texts' clearly indicates when to use this tool, but it does not explicitly name alternatives or state when not to use it. The sibling list provides context, but the description itself lacks direct when-not 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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