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synthesize_batch

Synthesize multiple texts into MP3 files at once, with control over voice, language, and speech rate. Merge segments into one audio file for continuous playback.

Instructions

Synthesize multiple texts to MP3 files.

Args: texts: List of texts to synthesize. With ElevenLabs eleven_v3, embed audio tags like [tired], [excited], [whisper] in text. voice: Voice name for all texts. Default: provider's default voice (currently matilda for ElevenLabs, joanna for Polly, nova for OpenAI). If language is provided without voice, auto-selects. language: ISO 639-1 language code (e.g. 'de', 'ko'). rate: Speech rate as percentage. Defaults to 90. merge: If true, produce one merged file instead of separate files per text. Defaults to false. pause_ms: Pause between segments in milliseconds when merging. Defaults to 500. auto_play: Open the file(s) in the default audio player after synthesis. Defaults to true. output_dir: Directory for output files. Defaults to TTS_OUTPUT_DIR env var or ~/langlearn-audio/. stability: ElevenLabs voice stability (0.0-1.0). similarity: ElevenLabs voice similarity boost (0.0-1.0). style: ElevenLabs voice style/expressiveness (0.0-1.0). speaker_boost: ElevenLabs speaker boost toggle.

Returns: JSON string with list of results, each containing path, text, voice, and language fields.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rateNo
mergeNo
styleNo
textsYes
voiceNo
languageNo
pause_msNo
auto_playNo
stabilityNo
output_dirNo
similarityNo
speaker_boostNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.7.2

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations provided, the description carries the full disclosure burden and does it well. It reveals defaults (rate=90, merge=false, auto_play=true, pause_ms=500), provider-specific voice defaults, the auto-select behavior when language is given without voice, the side effect of opening files via auto_play, and the exact return format. It does not mention overwrite behavior or rate limits, but for a synthesis tool this is a strong disclosure.

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 organized as a docstring with an opening one-sentence summary, a clean Args list, and a Returns section. Despite covering many parameters, every line adds value and the structure makes it easy to scan. No filler or tautology is present.

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?

Given the complexity of 12 parameters and no annotations, the description covers essentially everything needed to call the tool: all params, defaults, merge behavior, return payload, and output location. It stops short of stating file overwrite policy or constraints on input array size, but these are minor for the tool's context.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must compensate for all 12 parameters. It does so thoroughly: each parameter gets a clear meaning, default, or conditional behavior beyond the raw schema. For example, it explains the [tired]/[whisper] audio tags for texts, provider-specific voice defaults, and the role of pause_ms when merging. This fully bridges the schema gap.

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: 'Synthesize multiple texts to MP3 files.' This clearly establishes the batch nature of the tool and the output format. However, it does not explicitly distinguish itself from sibling tools like synthesize_pair_batch, so an agent must infer the differentiation from the word 'multiple' and merge semantics.

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?

The description implies batch usage by saying 'multiple texts' and describing merge behavior across texts, but it never explicitly states when to choose this tool over siblings such as synthesize or synthesize_pair. It offers no exclusions or alternative routing, so the usage context is present only implicitly.

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