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

No annotations are provided, so the description carries the full behavioral disclosure burden. It usefully discloses machine-readability, the 2026–2029 range, and that 2027–2029 rates are provisional. However, it does not describe the response format, update cadence, or any access considerations, though the operation is clearly a simple read.

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 dense sentences with no filler. The core content is front-loaded, the API equivalence is a useful single clause, and the provisional-data caveat is included without extra wording.

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?

For a zero-parameter, read-only rates endpoint, the description is nearly complete: it identifies the data domain, years, categories, and provisional status. The absence of an output schema means the description could have specified the exact return shape, but 'machine-readable' plus the field list is adequate for correct invocation.

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 are zero parameters and the input schema is empty, so the baseline is 4. The description adds meaningful context by describing the payload of returned data (brackets, tiers, percentages) even though no parameters need explanation.

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 specific resource ('Danish vehicle registration tax rates'), defines the scope ('2026–2029'), and enumerates the contained data categories (brackets, CO2 tiers, bundfradrag, EV phase-in). This clearly differentiates it from calculation, forecasting, and market/model stats siblings.

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?

The intended use is implied through the resource type: it is the raw rates dataset, as opposed to calculate_registreringsafgift or forecast_registreringsafgift. However, the description never explicitly says 'use this when you need raw rates' or names alternatives, so the agent must infer usage from context.

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