smog_grade_index

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

Without annotations, the description must disclose behavioral traits. It states it counts polysyllabic words, which is relevant, but does not mention that it is a read-only operation or any other behavioral aspects. The presence of an output schema partially compensates.

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?

The description is extremely concise with two short sentences, front-loading the key information. Every sentence adds value, and there is no redundant or irrelevant content.

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 the tool's simplicity (one parameter) and the existence of an output schema, the description is adequate but minimal. It does not explain the SMOG formula or return value details, which might leave some ambiguity.

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?

With 0% schema coverage, the description adds meaning by specifying the text should be from healthcare/medical texts and that polysyllabic words are counted. However, it does not elaborate on the format or constraints of the input 'text' parameter.

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 that the tool calculates the SMOG Grade and specifies it is best for healthcare/medical texts, which differentiates it from sibling readability indexes. However, it could be more explicit that it computes a readability index.

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?

The description provides some context by stating it is 'Best for healthcare/medical texts,' suggesting when to use it. However, it lacks explicit guidance on when not to use it or how it compares to sibling tools like Flesch-Kincaid or Gunning Fog.

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