get_government_recordsFREE during beta — returns live data. Un-aggregated procurement contracts, subsidy grants and certifications, pulled on demand from gBizINFO upstream. Source: gBizINFO (経済産業省), attribution included in every response.
| Name | Required | Description | Default |
|---|---|---|---|
| corporate_id | Yes | 13-digit Japanese corporate number (法人番号), e.g. 7010001012345 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It reveals that data is 'live,' 'pulled on demand,' and includes attribution, but does not disclose potential side effects, rate limits, data volume, or whether the operation is read-only. It is adequate but not comprehensive.
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 two compact sentences, front-loaded with key information ('FREE during beta,' 'returns live data,' 'un-aggregated'). Every element adds value with no wasted words.
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?
The tool has no output schema, so the description should explain return values. It mentions 'procurement contracts, subsidy grants and certifications' but does not describe the structure or format of the data. This is a notable gap for completeness.
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 has 100% coverage, with corporate_id well described. The description adds context about the source (gBizINFO) and the type of data returned, which helps the agent understand how the parameter connects to the output. It goes beyond the schema alone.
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 it returns 'un-aggregated procurement contracts, subsidy grants and certifications' from gBizINFO. The verb 'returns' and the specific data types make the purpose explicit and distinct from sibling tools which focus on other data (e.g., get_gazette_financials).
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 mentions 'FREE during beta — returns live data,' implying it is for on-demand raw logs, but does not specify when not to use it or provide alternatives among sibling tools. No explicit usage guidance is given.
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.