search_corporationsFREE — returns live data. Resolve Japanese company names to 13-digit corporate numbers (法人番号) via gBizINFO. Use the corporate number with the dataset tools.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Company name (Japanese), partial match supported | |
| page | No | Result page, default 1 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions 'FREE — returns live data,' which is a useful behavioral trait. However, it does not disclose any limitations, auth needs, or error behavior, leaving some gaps.
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 extremely concise with two sentences: one stating the key benefit and another explaining the output usage. Every sentence is purposeful and front-loaded.
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?
Given the low complexity (2 parameters, no output schema), the description is complete. It explains what the tool does and how to use the result with dataset tools. The schema covers parameter details.
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 description coverage is 100%, so the baseline is 3. The description adds no additional information beyond what the schema provides for the parameters.
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 the tool's purpose: searching Japanese corporations by name and resolving them to 13-digit corporate numbers. It distinguishes itself from sibling tools which focus on other specific data types like financials or government records.
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 description implies usage context by stating 'Use the corporate number with the dataset tools.' It does not explicitly list when not to use or alternatives, but the sibling tool names and distinct purpose provide sufficient guidance for an AI agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct purpose: searching corporations, listing datasets, retrieving gazette financials, government records, or pension headcount. No overlap in functionality.
All tool names follow a consistent verb_noun pattern in snake_case, using 'get_', 'list_', or 'search_' prefixes. No mixing of conventions.
5 tools is well-scoped for a gateway server providing access to Japanese corporate datasets. Each tool serves a necessary role without redundancy.
The tool surface covers the full workflow: search, catalog, and retrieve three key datasets. Minor gap: no tool for payment status or dataset metadata beyond what list_company_datasets provides.