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

No annotations are provided, so the description carries the behavioral disclosure burden. It usefully discloses the pay-per-use cost and the generative nature of the tool, but it does not describe expected input, output format, limitations, or failure 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 compact and front-loads the main purpose with a short cost note. The 'Auto-created from research' prefix is meta-information that does not directly help invocation, but it is minor and does not bloat the definition.

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 one-parameter generative tool without an output schema, the description gives a reasonable high-level purpose and cost warning, but it leaves the input contract vague and says little about what the returned scenarios might look like. It is usable but incomplete.

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%, but the parameter description is only 'entrada', which is tautological. The tool description indirectly implies that 'input' is the AI recommendation or context to expand, but it never explicitly defines the parameter's expected content, format, or language.

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 uses a specific verb ('Genera') and a concrete resource ('futuros alternativos y escenarios contrafactuales'), and explains its goal of countering narrowed options from AI overconfidence. It is clear, but it does not explicitly differentiate itself from the similar sibling 'futures-expander'.

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 states a clear use context: use when AI recommendations are overconfident and narrowing options. It does not say when not to use it or name a preferred alternative, so it lacks explicit exclusions.

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