vocametrix_detect_phonemes

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

The description notes the use of a deep-learning classifier, output of labels with confidence scores, and language support, but does not disclose processing time, file integrity handling, or whether the tool is read-only. Without annotations, the description carries the burden but remains basic.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, each carrying essential information: purpose and output, then language support. No superfluous words, front-loaded with the verb 'detect'.

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

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

Given no output schema and only 2 parameters, the description provides good context: it explains what the tool does, what it returns, and the supported languages. It could mention that the audio file must be a local WAV file (implied by schema but not stated in description).

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

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

Schema coverage is 100%, so parameters are already documented. The description adds value by explaining the output nature (labels with confidence scores) and specifying languages, which enhances understanding beyond schema types and enums.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool detects phonemes in audio using a deep-learning classifier and returns labels with confidence scores. It distinguishes itself from sibling tools by specifying phoneme detection and listing supported languages (French, Estonian).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives like transcribe_audio or assess_pronunciation. The description implies use for phoneme detection but does not exclude other scenarios or reference sibling tools.

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