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

Annotations already declare read-only, idempotent, non-destructive and open-world behavior, so the safety burden is lifted. The description adds genuine value beyond them: the interpretation rule that values above 100% indicate stock build-up, and the Eurostat source, which matters for citation and trust.

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 metric definition, followed by the one non-obvious interpretation caveat and the source. Nothing is repeated from the schema and every clause carries 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?

A simple read-only lookup with fully documented parameters and rich annotations needs only the metric definition plus interpretation context, both of which are present. Absent an output schema, a brief note on return shape (time series rows) would have closed the remaining gap.

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 all four parameters (“geo”, “energy” enum, “to_year”, “from_year”) are already documented with defaults. The description restates the energy-type options and the year dimension but adds no syntax or format detail beyond the schema, 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?

The description precisely defines the retrieved metric (“percentage of a country's energy needs met by net imports”) along with the energy breakdowns and time dimension, so an agent knows exactly what data this returns. It does not explicitly contrast itself with near-siblings like energy_mix, import_origins, or russia_import_trend, leaving differentiation to inference from the metric definition.

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?

There is no statement of when to call this versus alternatives, no prerequisites, and no exclusions. The definition implies the general use case (energy import dependency statistics) but the agent gets no routing guidance among the many energy/dependency siblings.

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