japan-data-mcp

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

With no annotations provided, the description carries the full burden. It discloses that no API key is required, that the return includes 'authentic Japanese legal text with English-normalized metadata and the required source attribution,' and that article extraction is optional. It does not mention rate limits or error behavior, but for a simple read-only fetch, this is meaningful coverage.

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 concise—five short sentences—and front-loaded with the core action. Every sentence adds value: fetch action, optional article, return format, no API key, and typical flow. 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?

There is no output schema, so the description appropriately explains return values: authentic Japanese text, English-normalized metadata, and source attribution. It also provides the typical flow and authentication context. It does not discuss error scenarios or truncation details, but those are partially covered by the input schema. Overall, it is fairly complete for a simple fetch 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?

The schema description coverage is 100%, and the description adds little beyond what is already in the schema. The only slight addition is clarifying that law_id may also be a revision_id, but that is already stated in the schema. Thus the description does not significantly enrich parameter meaning.

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 opens with the specific verb 'Fetch' and clearly names the resource: 'original Japanese text of a law' identified by law_id or revision_id. It distinguishes itself from the sibling search tools by focusing on retrieval rather than searching, and it explicitly references the typical search→fetch flow.

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 gives clear usage context: it states that law_id/revision_id come from search_japanese_law and presents the typical flow 'search_japanese_law -> get_law_text'. It does not, however, explicitly state when not to use this tool or mention alternatives, so it misses the full 'when/when-not/alternatives' criterion.

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 provided, the description carries the full burden. It discloses the auth requirement (free National Tax Agency application ID) and the return format (English-keyed records with corporate number, name, address). This goes beyond a bare 'look up' statement, though it does not describe potential error conditions or parameter exclusivity.

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 short sentences, front-loaded with the core purpose, and every sentence adds value. It avoids redundancy and fluff.

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 there is no output schema, the description explicitly states the output structure. It also covers the prerequisite (application ID). It does not mention edge cases like behavior when no match is found or when both parameters are provided, but for a simple lookup tool the coverage is solid.

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% and both parameters are described in the schema. The description adds the 'or' relationship between the two parameters (partial name OR corporate number) but does not add significant syntax or format details beyond what the schema already provides. Baseline 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 uses a specific verb and resource: 'Look up a Japanese corporation'. It clearly distinguishes this tool from sibling tools (get_law_text, search_japanese_law, search_statistics) by focusing on corporate data rather than legal or statistical data.

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?

It provides clear context that the tool is for looking up Japanese corporations, and mentions the requirement for an application ID. However, it does not explicitly state when to use this tool versus alternatives or any exclusions, so it misses the higher bar of explicit when/when-not guidance.

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 carries the burden of disclosing behavior. It clearly describes the return format (metadata fields and matched Japanese text), states that no API key is needed, and explains language matching behavior. This is more transparent than typical for a read-only search tool, though it does not cover potential limitations like pagination or rate limits.

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 concise and front-loaded with the primary purpose. Each sentence adds value: first states the function and output, second notes the authentication requirement, third clarifies language handling. No unnecessary repetition or verbose language.

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 search tool with 2 parameters and no output schema, the description covers the essential context: what it does, what it returns (including specific metadata fields), and language behavior. It lacks details on result ordering, pagination, or error handling, but these are not critical for basic invocation. The absence of annotations is partially compensated by the clear output description.

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 already fully documents both parameters (keyword and limit) with descriptions. The tool description adds some context about keyword language matching, but this largely echoes the schema's own note that 'Japanese matches the source text directly; English is best-effort.' No additional parameter meaning is provided beyond the schema, so the baseline 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 clearly states the tool's function: searching Japanese laws and regulations by keyword, and specifies the exact output (English-normalized metadata fields plus matched Japanese text). This distinguishes it from siblings like get_law_text (retrieving specific law text) and search_corporation (corporate 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 provides some usage context by noting that Japanese keywords match best and that the source is full-text Japanese, implying English works best-effort. It also mentions 'No API key required'. However, it does not explicitly mention when to use this tool versus alternatives or when not to use it (e.g., if looking for a specific law text, use get_law_text).

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 provided, the description carries the transparency burden. It discloses the key behavioral trait: it requires a free e-Stat application ID, indicating authentication needs. It also specifies the output as metadata with given fields. It does not mention rate limits or error behavior, but for a search tool this covers the most critical behavioral aspects.

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 well-structured sentence that front-loads the core purpose and follows with the key requirement. Every word earns its place, with no filler or repetition.

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 only 2 parameters and no output schema, so the description provides adequate context: search target, output fields, and credential requirement. It is not missing any critical operational details for a straightforward search tool, though it could mention error handling or API specifics. Overall, it is sufficiently complete 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%, so the baseline is 3. The param descriptions already define 'keyword' and 'limit'. The description adds only minimal extra context (that keyword searches e-Stat tables and output is metadata), but does not meaningfully expand on parameter syntax or formats beyond the schema.

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 action ('Search'), a specific resource ('Japanese official statistics tables (e-Stat)'), and the result shape ('English-keyed table metadata'). This clearly distinguishes it from sibling tools like search_corporation and search_japanese_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 description clearly indicates when to use the tool (searching e-Stat statistics by keyword) and mentions the required prerequisite (e-Stat application ID). It does not explicitly name alternatives or exclusions, but the domain-specificity effectively communicates when this tool is appropriate.

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