readability_score

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

No annotations are provided, so the description carries the behavioral-disclosure burden. It names the calculated outputs but does not state that this is a pure/deterministic calculation, what the reading level scale is, or what the return structure looks like. For a simple text-analysis tool the risk is low, but the disclosure is still thin.

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 a single, front-loaded sentence with no wasted words. It immediately states the operation and the resource.

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?

For a one-parameter text-analysis tool with no output schema, the description covers the essential operation and names the output concepts. It could be more complete by clarifying the exact scores returned or distinguishing itself from the sibling text_readability tool, but it is largely sufficient.

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 description coverage is 100%, and the single 'text' parameter is already documented in the schema as 'Text to score.' The description adds only the implicit fact that the text is analyzed for Flesch-Kincaid readability, which is already evident from the purpose statement.

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 states a specific verb and resource: it calculates Flesch-Kincaid readability scores and a reading level for text. That is clear enough to know what the tool does. However, it does not differentiate itself from the sibling tool text_readability, which appears to cover a similar text-scoring operation.

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?

There is no guidance on when to use this tool instead of alternatives, nor any stated exclusions or prerequisites. The phrase 'for text' establishes only the input type, not the usage context.

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