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

With no annotations, the description carries the full burden and does so well: it discloses the execution model ('fetches the URL and probes the origin via HEAD/GET for each artifact'), that no browser is needed, and a caching policy ('~24h per URL'). These are non-obvious operational traits an agent cannot infer from the schema.

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?

Front-loaded with purpose, then probes, exclusions, execution, and return shape in a logical order; every sentence earns its place. It is dense but not padded, though the full inline return-shape listing is the only slightly heavy element.

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?

With no annotations and no output schema, the description fully compensates by explaining the JSON return contract (ok, url, score, grade, pass/warn/fail/total, checks[]), the execution model, and caching. An agent has everything needed to call and interpret this tool without opening anything else.

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 coverage is 100% and the single 'url' parameter is fully documented in the schema, including its default. The description adds no format constraints, validation rules, or edge-case meaning beyond what the schema already states, so the baseline 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 ('Score') and resource ('a URL for design-system AI readiness'), and enumerates the exact 10 artifacts probed, so an agent knows precisely what the tool evaluates. It also positions itself within the designesy family ('the 6th maturity axis') and the sibling set, distinguishing it clearly from adjacent scoring tools.

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?

Provides explicit when-to-use ('verify whether a design system is the default context AI tools build from') and an explicit 'When NOT to use' clause routing to designesy_score for design-contract scoring and designesy_drift_score for AI-drift detection. Nothing is left to inference about tool selection.

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