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

Annotations already cover readOnly/openWorld/idempotent semantics, so the description's job is to add domain context — and it does: the ranking comes from monthly Korean travel-movement data and results are Korean-language names. It does not say what happens for a non-Korean or unknown POI (empty result versus error), which is the main remaining behavioral gap.

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?

A single front-loaded sentence with zero filler; the core concept is stated before the data provenance and language caveat.

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?

There is no output schema, and the description does not say what a result item contains beyond 'names in Korean' — no mention of rank, co-visit counts, or whether categories are returned. For a ranking/recommendation tool this leaves the return shape partially unspecified.

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?

With 100% schema description coverage (including the category enum legend and the limit default of 8), the schema already carries the parameter meaning. The description only adds the constraint that poi_name must be a Korean name, which is marginal beyond the schema's own example '경복궁'.

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 resource (related spots) and the mechanic behind it — 'Visitors to X also visited Y' — plus the data source (Korean travel-movement data, monthly). This is clearly distinguishable from find_nearby_places or search_places in concept, though it never names those siblings to make the contrast explicit.

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?

There is no when-to-use or when-not guidance and no alternative is named, even though find_nearby_places is a near-neighbor an agent could easily confuse with this one. The recommendation intent is only implied by the quoted phrase and the 'ranked' wording.

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