cuba_alarma

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

The description discloses automatic pattern detection (≥3 similar = warning) and Hebbian learning (similar errors boosted). This is useful behavioral context. However, it does not explain side effects like whether entries are created or notifications sent, and no annotations exist to supplement.

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 two sentences, front-loading the core action. No wasted words, but the second sentence could be more structured. Overall efficient.

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?

Given 4 parameters, no output schema, and no annotations, the description covers the tool's behavior adequately but lacks details on return values, error handling, or prerequisites. It is minimally complete.

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 the description does not need to add much. It repeats parameter details already in the schema. The extra behavioral info about patterns and boosting relates to tool function, not parameter semantics. Baseline 3 is appropriate.

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's purpose: 'Report errors immediately.' It also mentions auto-detection of patterns and Hebbian learning, which adds specificity. However, it does not explicitly differentiate from sibling tools like cuba_vigia or cuba_centinela.

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 only says 'Report errors immediately,' implying when to use, but provides no guidance on when not to use or alternatives among siblings. No explicit context for usage.

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