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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the lower bar applies. The description adds genuinely useful context by enumerating what comes back — resolved query, rows, nested matrix, gaps, sources, alternative indicators — which is important since no output schema exists. It does not mention pagination, latency, or breadth limits, keeping it short of a 5.

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?

Two tightly packed sentences with zero filler: the first defines the tool and its inputs, the second enumerates the response shape. Front-loaded correctly, though the return-value list is dense enough that it could be trimmed slightly.

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 no output schema and only two simple parameters, the description carries the burden of explaining returns and does so explicitly, including gaps and alternative indicators. What is missing is routing guidance against sibling data tools, which matters in a 35-tool family.

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 description coverage is 100%, so baseline is 3: 'The question, in Spanish or English' and 'Restrict to one topic' are already in the schema. The description restates the language support for the question but never mentions the domain filter or when to use it, adding essentially no semantics beyond the structured fields.

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?

States a concrete verb (answers), the input type (a plain-language question in Spanish or English) and the resource (Central American public data), backed by two realistic example queries. It implicitly contrasts with structured-query siblings like query_data, but never names an alternative outright, so the differentiation is inferential rather than explicit.

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 makes the use case clear (natural-language questions, bilingual) and the examples show the expected question shape. However, it gives no when-not guidance and never names query_data, find_indicators, or search as the structured alternatives, leaving routing between the family of data tools to inference.

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