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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, but the description adds significant behavioral context: 'Pure-math, fully deterministic,' 'ANTI-FABRICATION: every number is an exact closed-form value,' and the instruction to quote verbatim. It also discloses the conditional behavior when evaluateAtT is provided, which is not visible in annotations. No contradictions with annotations.

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 and front-loaded with the core purpose and output list, followed by usage context and a crucial anti-fabrication warning. Every sentence earns its place; there is no redundant or filler content.

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?

For a purely mathematical tool with no output schema, the description is exceptionally complete: it lists all summary statistics, the exact formula for MTTF, the conditional evaluation at time t, deterministic guarantees, and appropriate usage context. It covers everything an agent needs to decide when to call and what to expect.

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 provides 100% coverage for all three parameters, but the description adds substantive meaning: it gives the MTTF formula, clarifies that characteristic life is simply η, and explains the optional evaluateAtT parameter triggers R(t), F(t), and h(t) outputs. This goes beyond the schema's individual parameter descriptions.

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: 'Given Weibull two-parameter (β, η), return all the closed-form summary statistics,' enumerating exact outputs like MTTF, B10/B50/B90 life, and conditional R(t), F(t), h(t). This clearly distinguishes it from sibling tools like explain_distributions_for_reliability or interpret_weibull_shape, which are explanatory rather than computational.

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 when-to-use guidance: 'Use when the user has a fit and wants the numbers downstream tools normally compute' and warns against an alternative action ('don't recompute these from training-data recall'). However, it does not name specific sibling tools for alternative use cases, so it stops short of fully explicit exclusion guidance.

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