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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is fully covered structurally. The description adds the computation semantics and an example of input/output shape, but says nothing about error behavior on empty or invalid arrays (only the schema's 'Non-empty list' note hints at it).

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: the formula first, then a concrete illustration. Nothing is wasted and the core definition is front-loaded.

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?

An output schema exists, so return-value explanation is not required, and the single parameter is fully described in the schema. The description is sufficient to call the tool correctly; only minor edge-case behavior for degenerate inputs is left implicit.

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?

With a single parameter at 100% schema description coverage, the schema already carries the 'Non-empty list of numbers' constraint and an examples array. The description's example echoes the same sample data rather than adding new meaning about the parameter, 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?

The description gives the exact operation as a formula ('Range = max - min') plus a worked example, so the resource and computation are unambiguous. It does not explicitly distinguish itself from nearest siblings like calculator_std, calculator_variance, or calculator_mean, which is the only thing keeping it from a 5.

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?

There is no statement of when to use this versus alternatives such as calculator_std or calculator_average, and no guidance on edge cases (single-element list, negatives, empty input). Usage is only inferable from the formula itself.

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