Japan Law Search MCP

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

No annotations are provided, so the description carries the full burden. It describes the operation as a simple retrieval but does not mention potential errors, authentication, or return format. Sufficient for a straightforward read operation, but could be more explicit.

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 a single sentence that states the main action and then provides important format specifics. No extraneous content, front-loaded with key information.

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?

For a simple tool with two required parameters and no output schema, the description covers the input semantics well. It does not describe the output, but the purpose implies the tool returns article text. Adequate for the tool's complexity.

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% with parameter descriptions. The description adds value by clarifying the flexible formats for the article parameter (e.g., '3', '第3条'), which go beyond the schema's example.

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 explicitly states the tool's purpose: get article text given law_id and article number. It specifies the resource (law article) and action (get text), and differentiates from siblings that handle structure or search.

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 clearly indicates required inputs and acceptable formats for the article number. While it doesn't explicitly state when to use versus siblings, the sibling names imply different purposes, making the usage context clear.

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

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

No annotations are provided, so the description carries full burden. It states the tool returns structure (headings) but does not detail output format, depth, or any side effects. For a read-only tool, this is adequate but could be more explicit.

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 sentences long, front-loads the purpose, and provides actionable guidance without extraneous information.

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?

For a simple tool with one parameter, no output schema, and no annotations, the description sufficiently explains the tool's purpose and usage. Missing details like return format are minor given the tool's straightforward nature.

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 schema provides 100% coverage for the single parameter law_id, including a description and example. The description adds useful context by noting that law_id is found in search_laws results, which helps the agent understand where to obtain it.

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 retrieves the structure of a law (list of headings for chapters, sections, articles) by specifying a law_id, and distinguishes from siblings (get_law_article and search_laws) by focusing on the table of contents.

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 using it to decide which articles to read, implying it precedes get_law_article, and notes that law_id comes from search_laws, providing context for when to use it.

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

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

No annotations are provided, so the description must fully disclose behavior. It mentions searching e-Gov and the search modes, but does not describe the return format (fields beyond law_id), pagination, rate limits, or whether the database is always up-to-date. Since there is no output schema, the lack of return structure information is a notable gap.

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 sentences long and front-loads the core purpose. It avoids verbosity but could be slightly more structured (e.g., bullet points for search options). Still, it is efficiently written.

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 3 parameters, no output schema, and no annotations, the description covers the search purpose, options, and integration with siblings. However, it lacks details on the return structure and pagination behavior, which are important for an agent to consume the output correctly.

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 baseline is 3. The description adds context beyond the schema by elaborating on the enum values (e.g., 'full_text=条文本文を全文検索（既定）') and implying the use of limit. This extra detail justifies a score above baseline.

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?

Description clearly states the tool searches Japanese current laws from e-Gov, listing types (法律・政令・省令) and scope (~10,000 items). It distinguishes from sibling tools by explaining how law_id from results connects to get_law_structure and get_law_article.

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 explains the two search modes (full_text vs title) and their defaults, and explicitly says to use the returned law_id with sibling tools. However, it does not give explicit when-not-to-use guidance or comparisons with alternatives beyond siblings.

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