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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and closed-world, so the safety profile is covered. The description adds real behavioral context beyond them: the fixed 90-day lookback window, the 'no source answers yet' filter, and the aggregation by distinct askers and countries.

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 sentences, front-loaded with the core scope before the purpose statement, and no redundant filler. Marginally long-winded in the opening clause but every clause contributes information.

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, the description takes on the return-shape burden and does so: it names the returned content (topics, count of distinct askers, countries). Optional filtering is covered by the schema, so an agent has enough to call it correctly.

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 q (word filter) and country (ISO code) are already fully documented in the schema. The description adds no extra meaning about how those filters interact with the topic list, so the baseline 3 applies.

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 and resource ('Lists topics that DatosIA users asked for'), plus a precise scope: requests in the last 90 days that no source here answers yet. This distinguishes it clearly from answer-providing siblings like search_data or browse_marketplace.

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?

'Useful to see what data is missing, or what a dataset in the marketplace could supply' gives a clear use context and hints at the marketplace relationship. It stops short of explicitly naming an alternative tool or a when-not-to-use condition (e.g., use search_data for data that is already answered).

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