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

Annotations already declare read-only, open-world, idempotent, and non-destructive behavior. The description adds useful context by stating that verdicts are correlated across independent sources, which goes beyond the annotations. It does not discuss failure modes or data sufficiency, but the annotation coverage lowers that burden.

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 compact sentence that front-loads the tool's purpose, output labels, and domain. It contains no filler and is easy for an agent to parse.

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?

With an output schema and strong annotations, return values and safety are covered. However, the input parameter remains underspecified: no examples, no required fields, and no clarification of how an identity should be supplied. This is a meaningful gap for a tool with one generic 'input' parameter.

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?

The single input parameter has 100% schema description coverage, so the baseline is 3. The description hints that the subject is a decentralised social identity, but it does not specify the exact identity format or expected JSON/text fields, leaving the generic 'parametros del producto' description to carry most of the meaning.

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 specifies the tool's function: it returns a brand-impersonation verdict with explicit outcome values (AUTHENTIC / DOUBTFUL / ACTIVE_IMPERSONATION) for decentralised social identities. This scope and result format distinguish it from the sibling risk and market-analysis tools.

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 implies use cases: brand-impersonation assessment over decentralised social identities. However, it gives no explicit when-to-use or when-not-to-use guidance, and does not name any alternative tool for comparison.

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