Find a Japanese company by name

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

Annotations only cover read-only/open-world/non-destructive, but the description adds substantial behavior beyond that: deterministic rule-based exact-key matching rather than fuzzy search, a metered lookup cost, corporations-only scope, and the warning that same-name companies must be disambiguated by prefecture/city. This is exactly the context annotations cannot carry.

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?

One dense paragraph, and the highest-value facts (what it returns, the verify follow-up, the metered cost) are front-loaded. The middle clause about prefecture/city and the non-identity caveat is slightly run-on, but every sentence 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?

With no output schema, the description still explains the shape of the response (candidates carrying prefecture and city), the cost model, the scope restriction, and the empty-list case. Nothing an agent needs to call and interpret this tool 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?

Schema coverage is 100%, so the baseline is 3, but the description adds real meaning by enumerating accepted input forms (kanji, kana, romaji, registered English name) that the schema's single example does not fully convey. It says nothing extra about the limit parameter, which the schema already documents thoroughly.

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+resource ('Find candidate Corporate Numbers for a Japanese company name') and immediately distinguishes itself from the sibling verify_japanese_company by framing the output as candidates rather than proof. An agent can tell it apart from the verify tools without opening any schema.

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?

Explicitly states the workflow: 'a name match is not proof of identity, so confirm the candidate, then call verify_japanese_company.' It also scopes usage with 'Corporations only' and normalizes the empty result as a valid answer, so the agent knows both when to use it and what a negative result means.

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