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

Beyond the readOnly/openWorld=false annotations, the description discloses several traits the agent could not otherwise know: values are US dollars as declared (CIF), the rows are derived aggregates because UN Comtrade rows cannot be republished, and only the five largest partners are returned with `WLD` as the total. It also flags HS-code quality caveats (850440/854330 broad, 854140 pre-2022 includes LEDs). It does not say anything about ordering, rate limits, or what happens when `limit` is omitted.

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 core scope is front-loaded in the first sentence and the caveats follow logically. The single very long opening sentence packs a lot in and is hard to parse, but no sentence is filler — each adds scope, units, or a data-quality warning.

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 five optional parameters, 0% schema coverage, and no output schema, the description has to do heavy lifting. It covers the domain, units, aggregation policy, and four of the five parameters, but leaves `limit` undefined and says nothing about the returned shape (columns, ordering, default page size), which matters for a data-extraction tool.

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 0%, so the description carries the full parameter burden. It does compensate substantially: the six countries map to the `country` enum, the six product groups map to the `product` enum, 'from 2015' bounds `year`, and `partner` is explained via the `WLD` total and the top-five limitation. But `limit` is never mentioned, and no code format is given for `partner` beyond the `WLD` example, so the coverage gap is only partly closed.

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 names a concrete resource (clean-energy equipment imports), an explicit geography (Saudi Arabia, UAE, Oman, Qatar, Kuwait, Bahrain), a time span (from 2015), and the dimensions of the cut (product group, year, five largest partners). An agent can tell exactly what data comes back. It stops short of naming a sibling such as gcc_series or get_series, which would have made the differentiation unambiguous.

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?

Usage is implied rather than stated: the enumeration of countries, years and product groups tells the agent what queries are answerable, and 'the five largest partners' signals a result-shape constraint. However, there is no explicit when-to-use or when-not guidance, and no alternative tool is named for related needs (e.g., generic series retrieval).

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