Search Japanese corporate-led childcare laws by keyword when the law name is unknown. Retrieve relevant acts, cabinet orders, and ministerial ordinances from official e-Gov data.
企業主導型保育に関連する法令をキーワードで検索する。法令名が分からない場合に使用。e-Gov法令API v2を使用。
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | 取得件数（デフォルト10、最大20） | |
| keyword | Yes | 検索キーワード。例: "児童福祉", "保育", "認可外", "子育て支援" | |
| law_type | No | 法令種別で絞り込み。Act=法律, CabinetOrder=政令（施行令）, MinisterialOrdinance=省令（施行規則/基準） |
Changes observed during successful MCP inspections.
v0.3.1Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the behavioral burden itself. It does disclose that this tool uses the e-Gov law API v2, which is useful context about an external dependency. However, it does not mention read-only behavior, response format, rate limits, or potential errors, leaving some behavioral ambiguity.
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 three short sentences, each earning its place: what the tool does, when to use it, and what external API it relies on. It is front-loaded with the core purpose and contains no filler.
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 description covers purpose, usage condition, and external API dependency, which is solid for a simple search tool. However, there is no output schema and no mention of what the returned results look like, error behavior, or network-side expectations, so the agent still has some unknowns.
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 parameters are already well-documented with examples and enum meanings. The description adds no additional parameter-level semantics beyond what the schema provides, so 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 states a specific verb and resource: searching laws related to employer-led childcare by keyword. It also explicitly notes this is for when the law name is unknown, which distinguishes it from lookup tools like get_law.
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 phrase '法令名が分からない場合に使用' (use when the law name is unknown) gives a clear context for when this tool should be selected. It does not explicitly name alternatives or exclusion conditions, but the usage condition is enough to guide selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.