get_playbook_rules

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

Without annotations, the description fully discloses the output behavior: it returns full rule pages including title, description, markdown body, and HTML/CSS code examples. It does not mention side effects or permissions, but for a read-only data retrieval tool, this is sufficient.

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 sentences cover the core function, output details, and use case. Front-loaded with the primary action and immediately provides valuable context. No wasted words.

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 a single required parameter, no output schema, and no annotations, the description fully explains the tool's purpose, input (category), output (detailed rule pages), and usage context (teaching patterns). No information gaps remain.

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 schema provides 100% coverage for the single parameter (category) with an enum and description. The description does not add further parameter semantics beyond referencing 'a given category,' so it meets the baseline of 3.

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 the tool returns full rule pages for a given category, listing included fields (title, description, markdown body, code examples). It differentiates from sibling tools (get_component, list_categories, list_components) by focusing on rule content for a specific category, with a concrete usage example (responsive layout).

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 provides explicit guidance on when to use the tool ('to teach a model the exact patterns for a specific concern'), which implies a teaching or learning context. It does not explicitly state when not to use or mention alternatives, but the sibling tools suggest complementary roles.

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