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

Beyond the readOnlyHint and openWorldHint annotations, the description discloses substantial behavioral detail: the fuzzy matching with confidence-based responses (high confidence returns specs plus recommendations 추천.자동선택/추천.대안, low confidence returns candidates only), the standardized value notation rules, and the 20-point per-call cost. It also notes that no suitable spec means no charge, which annotation coverage alone would not convey.

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 purpose is front-loaded and every sentence carries information, but the bilingual (English + Korean) duplication and the dense enumeration of notation rules make it longer than necessary. Structure is logical and no sentence is truly wasted.

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?

With no output schema, the description carries the burden of explaining return values, and it does so well by describing specs, options, trim candidates, and recommendation fields. Combined with the cost note and fuzzy-matching behavior, it covers everything an agent needs for this moderately complex, zero-required-param tool.

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 the baseline is 3, but the description adds meaning beyond the schema by specifying the standardized output notation (fuel, body type, drivetrain, transmission, efficiency grades, '+' joining). It reinforces the fallback-to-list semantics of an empty model, though parameter-level detail largely duplicates the schema.

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 opening sentence gives a specific verb and resource ('Look up specifications of a Korean-market vehicle model') plus the exact inputs (brand, model name, model year, trim) and scope (passenger cars and motorcycles/scooters). No sibling tool covers this, so the agent can distinguish it easily among the car-related siblings like get_car_flooding and get_car_scrap.

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 description explains call behavior clearly: calling without a model name returns a brand/model list, and mismatched trims still resolve to the closest match. It provides solid context for how to invoke the tool with different parameter combinations, though it does not name explicit alternatives or exclusion conditions (there are effectively none among siblings).

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