Japan Laws Search MCP

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

No annotations exist, so the description carries the full disclosure burden, yet it only adds the vague 'PRO.' marker hinting at a subscription tier. It says nothing about what metadata is returned, whether auth is mandatory, or what happens with an invalid/expired law_id.

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?

A single compact sentence with the resource and key front-loaded after a terse tier marker. The cryptic 'PRO.' prefix is the only low-value element.

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?

Two parameters, no annotations, no output schema, and no sibling differentiation; the description never explains what 'detail/metadata' actually contains or whether access_token is required, leaving real gaps for a data-retrieval tool.

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 50%: access_token is documented in the schema, while law_id has no description. The phrase 'by law_id from search results' partially compensates by telling the agent where law_id comes from, but says nothing about format or validity.

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 (Fetch) and resource (law detail/metadata), plus the keying parameter (law_id). 'from search results' implicitly separates it from the sibling search tools, though it never names them.

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?

'from search results' implies the tool is a follow-up step that requires a law_id obtained from law_search/law_search_pro, but this is left to inference. No explicit when-not-to-use or named alternative is given.

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?

With no annotations, the description does real work: it discloses cost (FREE) and a hard result cap (top 3), both material for planning. It omits matching behavior (substring vs. full-text) and any error or rate-limit behavior.

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 short sentence with the most decision-relevant fact (FREE) front-loaded and the result cap last. The phrase 'as supported' is soft filler, but nothing else is wasted.

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 one-parameter search tool with no output schema, the description covers cost, result volume, and input language, which is most of what an agent needs. Return shape and matching semantics are the remaining gaps, and both are modest at this 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?

The single keyword parameter has 0% schema description coverage, so the description must compensate. It does add language semantics (Japanese or romanji), but the hedge 'as supported' is vague and it says nothing about whether keyword matching is exact, substring, or multi-term.

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+scope: search Japanese laws by keyword, capped at top 3. It hints at a tier distinction (FREE, top 3) that implicitly separates it from law_search_pro, but never names the sibling tools.

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 FREE label and 'top 3' cap implicitly signal this is the lightweight/low-cost option versus law_search_pro, but the description never states when to choose this over law_detail or law_search_pro. Usage must be inferred.

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?

Without annotations, the description provides the critical behavior of a limited output size (20 hits). This is useful, but it does not elaborate on expected error conditions, throttling, or authentication requirements beyond naming the token parameter.

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, capturing the tool's identity (PRO), action (search), and defining constraint (20 hits) in a single, efficient statement.

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 search tool lacking annotations, it is incomplete. It provides no guidance on authentication necessity or the expected format of the results, forcing the agent to rely solely on schema interpretation.

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?

Given the low schema description coverage (33%), the description fails to provide meaningful context for parameters like 'access_token' or how the 'limit' parameter interacts with the stated 'PRO' cap. It relies too heavily on the implicit understanding of the keyword.

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?

It clearly defines the resource (law search hits) and provides a scope constraint (max 20 hits). This specific limit helps distinguish it from the generic 'law_search' sibling, even without explicit comparison.

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 a restriction on volume but offers no explicit guidance on when to use this tool versus the alternative 'law_search'. It only communicates a ceiling, not a rule.

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