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

With no annotations, the description carries full behavioral burden and meets it: it discloses weekly DMR data source, as_of cutoff semantics with PARTIAL months, top-20 brand limitation, no per-model breakdown, and default kind=all. It also surfaces truncation behavior in the sibling tool, preventing misinterpretation. No annotation contradiction.

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?

Dense but well-organized; the core purpose is front-loaded, and each subsequent sentence adds a necessary caveat or usage rule. No filler or repetition.

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 two-parameter stats tool with no annotations or output schema, the description covers data source, parameter semantics, defaults, partial-data behavior, and boundary conditions with sibling tools. An agent has everything needed to select and invoke it correctly.

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?

Although schema coverage is 100%, the description adds real meaning: kind=all vs Personbil is defined, and each dimension enum value receives context (e.g., brands top 20, monthly_totals last 12 months incl. leasing). It goes well beyond the schema's 'Which slice to return.'

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 concrete resource: Danish new-vehicle registration statistics from bildata.io, and lists the available dimensions. It also explicitly distinguishes itself from get_model_stats by noting this is market-level only, so an agent can select it correctly among 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?

Provides explicit routing guidance: 'for a named MODEL use get_model_stats' and warns it is NOT a way to get a brand total outside top 20, explaining the sibling's default and truncation behavior. This gives both when-to-use and when-not-to-use guidance.

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