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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is fully covered. The description adds the enumerated metrics but says nothing about the return shape or failure modes for very short inputs, which is the only behavior an agent might need here.

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 short sentences, metric list front-loaded, zero filler. The second sentence is a soft usage hint rather than a requirement, so it is slightly expendable but not wasteful.

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?

A single-parameter, read-only utility with annotations and an output schema, so return values need not be described. The description covers purpose and metric set; only edge-case handling (empty/short text) is unstated, which is minor.

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?

Only one parameter ('input'), and the schema documents it at 100% coverage with an example. The description adds no syntax, length, or language constraints beyond the schema, so the high-coverage baseline of 3 applies.

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?

States a specific verb ('Calculate') and resource ('readability scores') and names the four concrete metrics produced (Flesch, Flesch-Kincaid, Coleman-Liau, ARI). This separates it from generic siblings like text_stats or word_frequency without naming them explicitly.

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?

'Useful for evaluating LLM output quality' gives an implied context but no explicit when-to-use versus siblings such as text_stats, word_frequency, or response_quality_score. The agent must infer the selection criteria.

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