Skip to main content
Glama

synthesize_pair_batch

Synthesizes text pairs into MP3 audio files, combining each pair's first and second text with a pause—ideal for vocabulary flashcards and language listening practice.

Instructions

Synthesize multiple text pairs and stitch each into MP3 files.

Each pair becomes [text1 audio] [pause] [text2 audio]. Use for vocabulary lists like [["strong","stark"], ["house","Haus"]].

Args: pairs: List of [text1, text2] pairs. With ElevenLabs eleven_v3, texts can include audio tags like [tired], [excited]. voice1: Voice for all first texts. Defaults to provider's default. If lang1 is provided without voice1, auto-selects. voice2: Voice for all second texts. Defaults to provider's default. If lang2 is provided without voice2, auto-selects. lang1: ISO 639-1 language code for first texts (e.g. 'en'). lang2: ISO 639-1 language code for second texts (e.g. 'de'). rate: Speech rate as percentage. Defaults to 90. pause_ms: Pause between pair segments in milliseconds. Defaults to 500. merge: If true, produce one merged file instead of separate files per pair. Defaults to false. auto_play: Play the audio 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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rateNo
lang1No
lang2No
mergeNo
pairsYes
styleNo
voice1No
voice2No
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

A4.8/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure, and it does so thoroughly. It states that files are stitched with a pause, that auto_play defaults to true, that merge changes output from separate files to one merged file, and that output_dir falls back to TTS_OUTPUT_DIR or ~/langlearn-audio/. These details expose important side effects beyond the schema.

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 front-loaded with a clear one-sentence summary and a concrete example, followed by a compact Args section and a Returns note. For a 14-parameter tool, the length is justified and every section earns its place without redundant fluff.

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?

Given the tool's complexity, the zero annotation coverage, and the 0% schema description coverage, the description is remarkably complete. It covers behavior, defaults, output format, provider-specific features, and return type, leaving nothing an agent needs to call it correctly.

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?

The schema has 0% description coverage, so the description must compensate, and it does: every parameter is explained with defaults, units, supported formats, and ElevenLabs-specific behavior. For example, it explains rate as a percentage, pause_ms as milliseconds, lang codes as ISO 639-1, and how voice1/voice2 auto-select when lang is provided.

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 opens with a specific verb and resource: 'Synthesize multiple text pairs and stitch each into MP3 files.' It also shows the exact output pattern ('[text1 audio] [pause] [text2 audio]') and a concrete vocabulary-list example, which makes the tool's purpose unmistakable and distinguishes it from the sibling tools synthesize, synthesize_batch, and synthesize_pair.

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 description gives clear usage context: 'Use for vocabulary lists like...' and clarifies that the tool handles multiple pairs, implying it is for pair-based, batched synthesis. It does not explicitly name alternatives or state when not to use it, but the context is strong enough for an agent to select it correctly.

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