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compare_tts

Compare TTS audio to expected text to detect mispronunciations, word insertions/deletions, and compute Word Error Rate.

Instructions

Compare TTS audio output against expected text — identifies mispronunciations, inserted/deleted words, and Word Error Rate (WER)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
languageNoLanguage code (default: en)en
audio_pathYesPath to the TTS audio file
expected_textYesThe text that was supposed to be spoken
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses the analytical outputs (mispronunciations, inserted/deleted words, WER), giving insight into what the tool returns. It does not explicitly state whether the tool is read-only or mention side effects, but the comparison nature implies non-destructive use.

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 a single, concise sentence that front-loads the action and then lists the key outputs. There is no unnecessary information or redundancy.

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?

The tool is simple, with all parameters documented in the schema and no output schema present. The description explains what the tool identifies but does not specify the return format or potential limitations. However, given the straightforward purpose and the listed output types, it provides sufficient context for an agent to decide when to invoke it.

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?

The input schema fully describes all three parameters (audio_path, expected_text, language) with 100% coverage. The description adds no additional parameter semantics beyond what the schema already provides, so it stays at the baseline for high schema coverage.

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's function: comparing TTS audio output against expected text, and specifies the exact outputs (mispronunciations, inserted/deleted words, WER). This distinguishes it from sibling tools like `transcribe` (which does not compare) and `quality_score` (which does not use expected 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 description implies use when you have a TTS audio file and the expected text to compare, providing clear context. It does not explicitly mention alternatives or say when not to use it, but the purpose is distinct enough to guide tool selection.

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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