address_postal_code

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

Annotations already declare readOnlyHint=true, so the read-only nature is covered. The description adds context about the data source (Japan Post KEN_ALL data) and implies multiple results via 'candidates', but it does not describe edge-case behavior (e.g., missing postal code) or response structure.

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 a single, front-loaded sentence that conveys the tool's purpose, scope, and data source without any fluff or repetition. Every word earns its place.

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 simple one-parameter lookup with annotations and full schema coverage, the description is nearly complete. The only minor gap is that 'address candidates' could be more explicit about the return type (likely a list), but the term is reasonably clear.

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 input schema provides a complete description of the zip parameter, including format and example. The description adds no additional parameter detail, so with 100% schema coverage the baseline score of 3 is appropriate.

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 clearly states a specific verb ('Look up') and resource ('Japanese address candidates for a 7-digit postal code'). It explicitly names the postal code format and data source, distinguishing it from sibling tools like address_normalize or address_geocode.

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 purpose implicitly signals when to use the tool (when you have a 7-digit Japanese postal code and need address candidates). However, it does not explicitly contrast with alternatives or state when not to use it, so usage guidance is only implied.

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