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

With annotations already covering readOnly/idempotent/closed-world, the description adds real behavioral context: the data source (NTA monthly bulk data), check-digit pre-validation that reports a wrong first digit as an input error, and decoded flag semantics (`closed`, `excluded_from_search`, `not_found`) including the instruction to tell the user about closure. This is well beyond what the annotations provide.

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 English portion is front-loaded and dense with no filler, leading with the input, then the returned fields, then validation behavior, then flags. The full Japanese mirror doubles the length, which is defensible for a bilingual audience but is redundant text for any single reader.

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 return-value burden and does so by enumerating every returned field plus the flag vocabulary. Combined with the validation and exclusion notes, an agent has everything needed to call it and interpret the result.

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 coverage is 100% for the single parameter, so the baseline is 3. The description adds meaning the schema does not: the first digit is a check digit that is verified and surfaces as an input error rather than a not-found result, which materially affects how the agent interprets a failure.

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?

States a specific verb and resource (look up a 13-digit Japanese corporate number) and immediately enumerates the returned fields. It also distinguishes itself from two adjacent concepts: the sibling search_corporation_by_name (name-based) and the invoice/qualified-invoice-issuer registry, which it explicitly rules out.

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 negative exclusion ('this is not the invoice registry') and the input type ('13-digit corporate number') make the use case clear: call this when you already hold a corporate number. It stops short of explicitly naming search_corporation_by_name as the alternative for name-based lookups, so the routing guidance is implied rather than spelled out.

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