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

Annotations already declare readOnly, idempotent, non-destructive, closed-world behavior, so the bar is lower, and the description still adds real value: a 95% confidence interval, a wage-controlled variant of the correlation, the caveat that correlation is not causation, and the conditional availability of the county series. These are behavioral/interpretive traits not present in any structured field. It stops short of describing result volume or pagination for the ~380-row county case.

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 topic is front-loaded, which is good, but the opening is a long comma-spliced enumeration of datasets that is hard to parse on first read. The trailing short sentences ('Correlation is not causation', the index name, the sibling pointer) are efficient and earn their place. Net: informative but denser than it needs to be.

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?

An output schema exists, so return structure need not be explained, and annotations cover the safety profile. The description covers data scope, statistical caveats, the controlling variant, and the index name, which is sufficient for a read-only statistics fetch. The main residual gap is not saying which datasets come from Eurostat versus GUS/UDT or how the two correlation variants are labeled in the response.

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?

There is a single boolean parameter with 100% schema description coverage that already explains the ~380-row full-county behavior versus the top/bottom 10 default. The description corroborates this ('top and bottom counties', 'when the county series is available') but adds no syntax or semantics beyond the schema. Baseline 3 applies when the schema does the work.

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 subject (BEV and PHEV passenger cars in Poland) and enumerates the exact series returned: year-end fleet, new-car shares, fuel mix, provinces/counties per 1000 cars, and the charging-site/BEV correlation. It also names the backing index (finwire_ev_share_new_cars), so an agent can tell it apart from get_ev_charging or get_vehicle_registrations. The only weakness is that the verb is implicit — it never says plainly 'returns data on'.

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?

One alternative is named explicitly ('For charging points use get_ev_charging'), which is genuine routing help. However, nothing says when this tool is the right choice versus get_regional_data, get_vehicle_registrations, or get_europe_data, and there are no stated prerequisites. Usage is implied by the data enumeration rather than stated.

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