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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds value by clarifying that the tool returns explanation guidance rather than applying a fix, which tells the agent the output is advisory content, not a mutation or a remediation action.

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?

Three short sentences, front-loaded with the core action, then usage, then output nature. Every sentence earns its place with no repetition of the name or title.

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 a full output schema present, the description does not need to explain return values, and annotations cover safety. It is complete for the core call, but the unresolved overlap with the get_rule sibling leaves a small ambiguity an agent must resolve elsewhere.

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 exists and schema description coverage is 100%, so the schema already documents the slug format and the catalogue constraint. The description adds nothing about the slug, which is the expected baseline when the schema does the work.

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 gives a specific verb and resource ('Explains one frontend rule') and enumerates the content it covers (background, user/business impact, related categories). It distinguishes itself from fix_rule by stating it returns explanation guidance rather than applying a fix, but it never clarifies how it differs from the sibling get_rule, which an agent can only infer.

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?

It states a clear trigger condition: 'Use it when the user asks why a practice matters or requests educational context.' That is explicit when-to-use guidance. There is no when-not guidance beyond the implicit contrast with fix_rule, so it falls short of naming alternatives outright.

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