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

Annotations already declare readOnly, idempotent, and non-destructive hints, but the description adds a crucial behavioral constraint: it must quote verbatim from docs/reliability-theory.md and must not paraphrase from training-data recall. This goes beyond the annotations and meaningfully shapes agent behavior.

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 three sentences that front-load the tool's function, then give usage triggers and an anti-fabrication note. It is compact, clear, and every sentence earns its place.

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?

The tool has no output schema, but the description fully enumerates the content domains, provides a source reference, and explains when to use it. For a textual explainer, this is complete and leaves no significant gaps.

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 input schema has zero parameters and the description explicitly states 'No inputs,' so there are no parameter semantics to explain. Per the rubric, a zero-parameter tool receives a baseline of 4.

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 opens with a specific verb and resource ('Return a textbook-tier explainer of reliability fundamentals') and enumerates the exact topics covered (R(t)/F(t)/f(t)/h(t), MTBF/MTTF/MTTR, availability identity, bathtub curve, series/parallel). This clearly distinguishes it from siblings like explain_advanced_reliability_patterns and explain_distributions_for_reliability.

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?

The description provides explicit trigger queries ('Use when a user asks...'), giving the agent concrete conditions for selection. However, it does not explicitly state when-not-to-use or name alternative tools, so it stops short of full guidance (which would merit a 5).

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