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

Annotations already declare this as a read-only, idempotent, non-destructive, closed-world operation. The description adds useful behavioral context beyond annotations by stating the exact count (78), that the names are accepted for filtering, and that official accents are preserved.

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 short sentences front-load the resource and scope, with no redundant or filler content. Every sentence adds useful information about the list and its accent handling.

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 static lookup with no output schema, the description supplies enough context: what is returned (78 municipality names), why (city filter), and how they are represented (official accents). Nothing needed to call it correctly is missing.

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 tool takes zero parameters, so there are no parameter semantics to document. The empty schema is fully consistent with the description, and the baseline for zero-parameter tools applies.

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 verb and resource: list the 78 accepted Puerto Rico municipality names. It also clarifies scope by tying the list to the city filter and preserves official accents, making the tool's purpose unambiguous among siblings such as list_categories or discover_listings.

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 phrase 'for the city filter' clearly signals the intended use case: retrieving valid city values for filtering. It does not explicitly name when not to use the tool or point to an alternative, but the context is sufficiently clear for a simple lookup tool.

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