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

Annotations already establish readOnlyHint, idempotentHint, and destructiveHint: false. The description adds meaningful behavioral context beyond these: the data is aggregated from real reviews, returns a short sentence plus reference date and source URL, and is not a unit price or quote amount. This gives agents a realistic expectation of the output type without contradicting the annotations.

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 description is moderately compact: one scoping sentence with concrete topic examples, one sentence describing return composition, one caveat disclaiming exact pricing, and one sentence on the topic param. It front-loads the subject scope and keeps sentences short; the 'not a quote' caveat could appear slightly earlier, but the structure is mostly effective.

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?

Given there is no output schema, the description compensates by explicitly stating what the tool returns (a short sentence, reference date, source URL) and clarifying the data is a review-aggregated range, not a quote. For one optional parameter and read-only semantics, this is sufficient context for an agent to invoke the tool 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?

The schema documents the single optional `topic` parameter at 100% coverage with examples and empty-topic behavior. The description enriches that by enumerating acceptable topic categories (평, 방, 장르, etc.) and specifying that there are about 30 topics, which goes beyond the schema. This adds real value for the agent when deciding what to pass.

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 states a specific deliverable: aggregated interior cost ranges for 30 topics across floor type, room type, and construction process, returning a short sentence with reference date and source URL. It also explicitly disambiguates itself as a review-aggregated reference rather than a quote tool, which clearly separates it from sibling tools like nmjib_checklist and nmjib_faq_search.

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 gives parameter-level guidance ('topic을 주면 그 주제만', empty means the full table) and implies the tool is for cost references. However, it never explicitly says when to use this tool instead of its siblings or when not to use it, leaving alternatives differentiation to inference.

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