automated_readability_index

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

No annotations are provided, and the description only states the basic input-output relationship (character and word counts). It does not disclose any behavioral traits such as expected output range, handling of non-standard text, or computational complexity.

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 one sentence, front-loaded with the tool's purpose. It is concise and avoids redundancy, though it could be slightly expanded without losing efficiency.

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 presence of an output schema (not shown but indicated), the description is too brief. It omits the output format (numeric grade level), any interpretation guidance, and edge cases. The tool is simple but the description lacks completeness for proper usage.

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 single parameter 'text' has no description in the schema (0% coverage). The description adds that the tool uses 'character and word counts,' implying text input, but does not specify constraints like expected encoding, byte length, or whether preprocessing is needed.

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 it calculates the Automated Readability Index (ARI) grade level from character and word counts. It names the specific metric and input derivation, but lacks differentiation from sibling readability tools like Flesch-Kincaid or Coleman-Liau.

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 on when to use ARI versus other readability indices or text analysis tools. There is no mention of prerequisites, typical use cases, or exclusions.

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