gunning_fog_index

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

With no annotations provided, the description must disclose behavioral traits, but it only states the basic purpose. It does not mention input requirements (e.g., plain text), edge cases, or any internal behavior beyond the index calculation.

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?

Extremely concise: one sentence that front-loads the name and purpose. Every word is relevant, and no unnecessary information is present.

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 (single string input, standard readability metric) and the presence of an output schema (documenting return format), the brief description is mostly sufficient. However, it could briefly note the expected input format (e.g., plain text).

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?

The sole parameter 'text' has 0% schema description coverage, and the tool description adds no additional meaning beyond the parameter name. The agent gains no insight into expected format or constraints.

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 computes 'Gunning Fog Index' and explains it estimates 'years of formal education needed to understand text', distinguishing it from sibling readability tools like Flesch Kincaid Grade or SMOG 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?

No explicit guidance on when to use this tool versus alternatives like flesch_kincaid_grade or smog_grade_index. Usage is implied by the purpose but lacks context or exclusions.

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