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

Goes well beyond the annotations by disclosing response contents (datasets used with links to the official portal, data quality/coverage warnings, remaining monthly quota) and the exact cost model: 1 question deducted per answer, 10 free per month, with errors, timeouts, and identical questions repeated within 5 minutes not deducted. This is the kind of billing and failure-mode context that annotations cannot express and that materially affects how an agent should call it.

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?

Front-loads the purpose, then examples, then response contents, then cost and fallback routing — a logical order with no filler sentences. It is somewhat long (four examples plus a three-part billing clause), but each block carries information the agent needs and none is redundant with the schema or annotations.

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 natural-language query tool with an output schema already defining return structure, the description supplies everything else an agent needs: scope, example inputs, cost and quota behavior, error/timeout exemptions, and preferred alternatives. Nothing material to correct invocation is missing.

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 'pregunta' parameter has 0% schema description coverage, so the description must carry the semantics — and it does, via four representative questions that establish free-text natural-language input, expected topics (unemployment, IPC, legislature, budget), and acceptable phrasing. It stops short of stating any length or formatting constraints.

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 specific verb+resource: answering a natural-language question over official Argentine public data, with sources. The four concrete example questions make the scope unambiguous, and it distinguishes itself from sibling tools by telling the agent to prefer buscar_datasets + obtener_datos or agregar_datos when those suffice.

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 explicitly names alternatives (buscar_datasets, obtener_datos, agregar_datos) and a preference rule to use them instead when possible, which is real routing guidance. The deciding condition ('si podés responder con...') is self-referential and never says what this tool uniquely offers (multi-dataset synthesis, natural-language questions), so the agent must infer it.

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