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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds useful behavior beyond that: chronological ordering, default-country fallback, and the default point cap with 'most recent kept' retention policy. It stops short of return-format or truncation signalling, but adds real value.

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 tight sentences; the core purpose and ordering constraint are front-loaded, and the optional-selection guidance follows without padding.

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?

No output schema exists, so the description carries the burden of return semantics; it discloses ordering and the max_points retention rule, which is the key consumer-facing detail. A note on what happens when no exact series matches would make it fully self-contained.

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 100%, so baseline is 3. The description goes beyond the schema by giving concrete period syntax examples (2015, Q1/2015, 01/2015) where the schema only shows '2015', and by explaining that dimension values come from inspect_dataset.

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 ('Time series values for one country from a dataset') plus scope (single country) and ordering (oldest first). This is clearly distinguishable from the sibling tools get_dataset, inspect_dataset, and search_catalog.

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 routes the agent to inspect_dataset for obtaining dimension values and explains the optional series-selection path and start-period usage. It doesn't explicitly state when to prefer this over get_dataset, but the workflow context is clear.

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