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

No annotations are provided, so the description carries the full burden. It does disclose genuinely useful behavioral context that structured fields lack: the pricing/access model (x402, 0.01 USDC on Base, pay-per-use). It says nothing about the output shape, latency, limits, or failure behavior, so the burden is only partially met.

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?

It is a single sentence, which is appropriately compact, and the core purpose is front-loaded after the 'Auto-created from research' prefix. The invoice-style payment bracket and the truncated wording ('ampliar el aba') dilute the sentence, so not every fragment 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?

There are no annotations, no output schema, and only one documented parameter, so the description is the only source of behavioral and return-value information. It explains the input motive but never says what the agent gets back (scenario list, counterarguments, structured vs. prose) or how the result is delivered, leaving the definition incomplete for a generative tool.

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?

There is a single parameter with 100% schema coverage, but the schema description is only the trivial label 'entrada'. The description partially compensates by implying that the input should carry both a decision and a model recommendation. Baseline for high coverage with one parameter is 3, and the description adds a modest hint about expected content.

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 names a specific verb and output (generates alternative future scenarios and counterarguments) plus the triggering input (a decision plus a model's recommendation), which is clearer than most. However, it does not distinguish itself from the many near-identical siblings such as futures-expander, future-options-expander, futures-spectrum-expander, and horizon-expander. The text is also visibly truncated ('ampliar el aba'), which weakens the promise of a complete statement of purpose.

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 frames a general motivation ('bias against faith in AI') but gives no explicit when-to-use, when-not-to-use, or alternative guidance. Given four sibling tools with almost the same name and function, the absence of any routing signal is a real gap. Usage is only implied by the stated input shape.

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