Shirabe Japan Data Hub
Server Details
Japan data tools for AI agents: calendar (rokuyo), address, name splitting, corporate number lookup
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.4/5 across 5 of 5 tools scored.
Each tool addresses a unique domain task: corporate number lookup, corporate name search, calendar, address normalization, and name splitting. There is no functional overlap; agents can clearly distinguish which tool to use based on the input and desired output.
Two tools use a noun_verb pattern (corporation_lookup, corporation_search) while the other three use verb_noun (lookup_calendar, normalize_japanese_address, split_japanese_name). This inconsistency could cause minor confusion, though all names are still descriptive and readable.
Five tools is an appropriate count for a focused Japan data hub covering corporate registry, calendar, address, and name processing. Each tool serves a distinct purpose without unnecessary redundancy.
The tool surface covers the core functionalities: corporate lookup/search, calendar, address normalization, and name splitting. A minor gap is the lack of a dedicated postal code lookup, though it is partially addressed in address normalization. Overall, the set is well-rounded for common tasks.
Available Tools
5 toolscorporation_lookupLook up Japanese corporation by numberARead-onlyIdempotentInspect
Look up a Japanese company by its 13-digit corporate number (法人番号) against the National Tax Agency (NTA) corporate-number registry. Returns the registered trade name, head-office address, change history and other public fields as JSON, plus mandatory NTA attribution. Useful when an AI agent already has a corporate number and needs canonical company facts (e.g. validating or enriching a B2B record). To go the other way (company name → number), use corporation_search. Source: Shirabe Corporation API.
| Name | Required | Description | Default |
|---|---|---|---|
| law_id | Yes | A 13-digit Japanese corporate number (法人番号), e.g. 1234567890123. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint. The description adds that the tool queries the NTA registry, returns JSON with mandatory attribution, and sources from the Shirabe Corporation API. This provides behavioral context beyond annotations, such as data source and attribution requirement.
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?
Three sentences, each purposeful: purpose and data source, usage context, alternative tool and source citation. No fluff, front-loaded, and every sentence earns its place.
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 lookup tool with one parameter and no output schema, the description covers purpose, usage context, alternative, data source, attribution, and return format (JSON with field types). It is complete for effective selection and invocation.
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% for the single parameter, and the schema already includes a description with example. The description reinforces the format but does not add new semantics beyond what the schema 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 clearly states the tool looks up a Japanese company by its 13-digit corporate number against the NTA registry, and specifies what it returns (trade name, address, change history, etc.). It explicitly distinguishes from sibling tool corporation_search by noting the direction (number to facts vs. name to number).
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?
Describes when useful ('already has a corporate number and needs canonical company facts') and provides an explicit alternative ('To go the other way, use corporation_search'). This gives clear decision context for an AI agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
corporation_searchSearch Japanese corporations by nameARead-onlyIdempotentInspect
Search the Japanese corporate-number registry (National Tax Agency) by company name and return matching companies, each with its 13-digit corporate number (法人番号), registered name and address, plus mandatory NTA attribution. Handles trade-name variants (㈱ / (株) / 株式会社) via normalization. Useful when an AI agent has a company name and needs to resolve its corporate number / canonical record. To go the other way (number → company), use corporation_lookup. Source: Shirabe Corporation API.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | A Japanese company name, e.g. 株式会社テックウェル (variants like ㈱テックウェル are accepted). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, indicating safe, idempotent behavior. The description adds that it handles trade-name variants and returns mandatory NTA attribution, but does not disclose potential limitations like partial matching or pagination.
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 front-loaded with the core action and uses four sentences efficiently. It is concise without sacrificing clarity, though it could be slightly more streamlined.
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 the tool's simplicity (one parameter, read-only), the description fully explains the return values (corporate number, name, address, attribution) and input handling. No output schema exists, so the description provides sufficient context.
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 schema already describes the parameter. The description adds value by noting that trade-name variants are accepted via normalization, which enhances understanding 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 clearly states the action (Search), the resource (Japanese corporate-number registry), and the output (corporate number, name, address). It distinguishes from the sibling tool corporation_lookup by specifying the direction of lookup.
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 explicitly states when to use (to resolve a company name to corporate number) and provides a clear alternative (corporation_lookup for reverse lookup). It does not include exclusions or when not to use, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
lookup_calendarLook up Japanese calendarARead-onlyIdempotentInspect
Look up Japanese calendar information for a given date: Rokuyo (six-day cycle such as Taian/Butsumetsu), Rekichu (almanac notes), Eto (sexagenary cycle), 24 solar terms, and purpose-based auspiciousness scores. Useful when an AI agent needs canonical Japanese calendar facts (e.g. choosing an auspicious date for a wedding or ceremony). Source: Shirabe Calendar API (shirabe.dev).
| Name | Required | Description | Default |
|---|---|---|---|
| date | Yes | Target date in YYYY-MM-DD (between 1873-01-01 and 2100-12-31). | |
| categories | No | Optional purpose categories to score (e.g. marriage, funeral, groundbreaking). Omit to return all available categories. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds value by specifying the data source (Shirabe Calendar API) and listing the types of information returned, which goes beyond the annotations.
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-loading the core purpose and following with use case and source. Every sentence adds value; 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?
For a simple read-only tool with two parameters and no output schema, the description covers input format, optional parameter semantics, use case, and data source. It is complete for an AI agent to select and invoke 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 description coverage is 100%, so baseline is 3. The description adds extra meaning for the 'categories' parameter explaining it as 'optional purpose categories to score' and provides example categories, improving understanding 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 clearly states 'Look up Japanese calendar information for a given date' and enumerates specific data types (Rokuyo, Rekichu, Eto, etc.). It distinguishes tool from sibling tools focused on corporations and addresses.
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 explicitly suggests a use case ('choosing an auspicious date for a wedding or ceremony'), providing clear context. However, it does not mention when not to use or alternatives among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
normalize_japanese_addressNormalize Japanese addressARead-onlyIdempotentInspect
Normalize a free-form Japanese address against the official Address Base Registry (ABR, all 47 prefectures). Returns the canonical form, structured components (prefecture/city/town/block/building), postal code, WGS84 coordinates, match level (0-4) and confidence, plus mandatory CC BY 4.0 attribution. Useful when an AI agent must clean or de-duplicate Japanese B2B/customer records. Source: Shirabe Address API.
| Name | Required | Description | Default |
|---|---|---|---|
| address | Yes | A raw Japanese address string (may include postal code, building, floor). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, establishing safety. Description adds rich behavioral details: return fields, match level, confidence, attribution. No contradiction.
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?
Three sentences efficiently pack purpose, behavior, and usage context. Front-loaded with main verb and target. 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?
Given one parameter and no output schema, the description fully covers behavior, return values, and usage intent. Annotations handle safety. Complete.
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 covers 100% of parameter with description; description adds value by clarifying the input may include postal code, building, floor, beyond schema's 'raw string'.
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 normalizes Japanese addresses against the ABR, returns canonical form and structured components. It distinguishes itself from sibling tools (unrelated) by focusing on normalization.
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?
Explicitly mentions usefulness for cleaning/deduplicating Japanese records, providing clear context. No explicit exclusions or alternatives, but siblings are unrelated, reducing need.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
split_japanese_nameSplit Japanese personal nameARead-onlyIdempotentInspect
Split a Japanese personal name into family and given parts using IPAdic person-name POS tags (with whitespace/length heuristics as fallback). Returns family, given, and a confidence score (0-1; warning when low). Useful for normalizing name fields in HR, CRM, or form data. Source: Shirabe Text API.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | A Japanese personal name, e.g. 山田良介. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds behavioral details: the algorithm (IPAdic with fallback), output confidence score, and warning when low. This goes beyond the annotations and provides useful context about the tool's 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?
The description is two sentences, front-loaded with the core functionality, and every sentence adds value. No redundant or unnecessary content.
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 explains the output structure and confidence score, and provides usage context. It lacks discussion of limitations or edge cases (e.g., non-Japanese names), but for a simple tool with strong annotations and comprehensive parameter documentation, it is nearly complete.
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 covers the parameter fully with an example. The tool description does not add new constraints or format details for the parameter itself; it explains how the parameter is processed, which is more about tool behavior. Since schema coverage is 100%, a 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 verb 'Split', the resource 'Japanese personal name', the method 'using IPAdic person-name POS tags (with whitespace/length heuristics as fallback)', and the output structure (family, given, confidence score). It is distinct from sibling tools like corporation_lookup or normalize_japanese_address.
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 explicitly states when to use: 'useful for normalizing name fields in HR, CRM, or form data.' It implies it is for Japanese names only, but does not explicitly state when not to use or list alternatives. However, no direct sibling tool competes, so the guidance is adequate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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