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

Annotations already provide readOnlyHint=true, so the description does not need to state safety. It adds behavioral context by listing exactly which statistics will be computed. However, it does not mention any permissions, rate limits, or edge cases (e.g., behavior with NaN or extreme values).

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 a single paragraph that front-loads the core purpose and lists statistics, followed by usage examples. It is efficient and avoids redundancy, though it could be slightly more structured (e.g., bullet points for statistics).

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 description covers the tool's purpose, output statistics, usage scenarios, and chainability. With no output schema, listing the computed statistics is sufficient. The description is complete for this tool's complexity and rich annotations.

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?

Schema coverage is 100%, so baseline is 3. The description adds no additional meaning to parameters beyond the schema; it reiterates that 'valores' is a list of numbers but does not provide extra syntax or formatting details.

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 calculates principal statistical descriptors of numerical datasets, listing many specific metrics (media, mediana, etc.). It distinguishes from sibling calculators by focusing on statistical analysis of numerical data, as opposed to other 'calcular_' tools for specific domains.

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 usage context: useful for analyzing returns, prices, expenses, and chainable with any tool returning numerical series. It includes an example query. However, it does not explicitly state when NOT to use it or mention alternatives among siblings.

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