HojinCheck — Japanese corporate verification API (hojin = 法人/corporate entity)

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

With no annotations, the description carries the behavioral burden. It adds value by disclosing check-digit validation and the inclusion of per-item source and retrieval date metadata. However, it does not mention error handling, rate limits, or explicitly confirm read-only status, leaving some behavioral aspects undisclosed.

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 compact sentences. The first sentence delivers the core function and outputs, and the second provides the data source. Every word is informative, with no filler or unnecessary detail.

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 single-parameter, read-only lookup with no output schema, the description covers input requirements, data fields returned, metadata inclusion, and provenance. It lacks explicit failure scenarios, but given the simplicity and schema richness, there is enough context for an agent to use it 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?

The input schema already fully describes the parameter, including the 13-digit format, handling of full-width/hyphens/spaces, and check-digit validation (100% coverage). The tool description repeats these details without adding new semantic meaning, so 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 tool returns a gBizINFO corporate profile (location, representative, capital, employees) based on a 13-digit corporate number with check-digit validation. It uses a specific verb ('返します' - returns) and resource ('法人プロファイル'), which distinguishes it from sibling tools like verify_company or resolve_company.

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 the tool is for retrieving corporate profile data when a corporate number is available, but it does not explicitly state when to use it over alternatives or provide exclusions. Sibling tools such as verify_company and resolve_company are not differentiated, so the agent may not know which to pick.

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 full burden and discloses key behavior: it follows the Cabinet Office CSV and treats weekends/holidays as non-business days. The year_end_as_holiday parameter behavior is also explained. It does not mention return types or edge cases, but for a calculation tool the core logic is transparent.

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, comprising two short sentences that front-load the purpose and immediately state the core rule and the special flag. Every clause contributes useful information without unnecessary verbosity.

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 moderate complexity (6 parameters, 5 ops, no output schema), the description covers the essential behavioral rule and the optional flag. It lacks explicit return type descriptions, but the operation names (is_business_day, next_business_day, etc.) are self-explanatory, so completeness is adequate.

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 description repeats the year_end_as_holiday semantics already present in the schema and adds the general business day rule, but does not significantly enrich parameter meanings beyond the existing schema descriptions.

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: '日本の祝日判定と営業日計算' (Japanese holiday determination and business day calculation), and defines the business day rule ('営業日=土日・祝日以外'). It uses specific verbs and a clear resource, and is distinctly different from sibling tools which focus on company/invoice verification.

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 context on when to use the tool by defining what counts as a business day and the optional year_end_as_holiday behavior. It does not explicitly exclude alternatives, but no sibling tool serves a similar purpose, so the usage is effectively 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?

With no annotations provided, the description carries the full behavioral disclosure burden. It is exceptionally transparent about limitations (parsing only up to town level, not street numbers or Kyoto street names), states that unmatched portions are returned as residual strings, and notes that postal code mismatches are explicitly indicated. It also cites data sources, giving users confidence in its reliability.

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 well-structured and appropriately sized. It opens with the core action, then clearly delineates limitations using a labeled block, and ends with data sources. Every sentence provides valuable information, and the use of headers (【v1の粒度と限界】) makes it easy to scan. No redundancy or 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 the main functionality, limitations, and data sources, and gives hints about output behavior (residual string, mismatch flag). However, without an output schema, it lacks a precise specification of the returned fields (e.g., prefecture/city/town names, town ID, postal code status). This is a minor gap given the tool's complexity, but enough to prevent a perfect score.

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 provides 100% coverage, including descriptions of both parameters (address and optional postal_code). The description adds no new parameter-specific information beyond what the schema offers, so the baseline of 3 is appropriate; it neither enhances nor detracts from semantic clarity.

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 actions: normalize Japanese addresses, decompose into prefecture/municipality/town, resolve town IDs, and match postal codes. It explicitly distinguishes itself from siblings (which handle company/invoice/calendar functions) and specifies the exact scope and limitations, making its purpose unambiguous.

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 the tool is for Japanese address processing and clearly states what it does NOT do (no block numbers, etc.), which serves as an exclusion for cases needing finer granularity. However, it does not explicitly mention alternatives or provide a direct 'when to use vs. other tools' comparison, though none of the sibling tools are address-related.

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 full burden. It adds valuable behavioral context by disclosing the data source (National Tax Agency Web-API) and the output structure (candidates with confidence and reason label). This goes beyond the schema, though it does not mention network side effects or error handling.

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?

Two concise sentences: the first states the core function and output, the second gives the data source. It is front-loaded and contains no filler, earning the maximum score.

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 no output schema and no annotations, so the description should compensate. It mentions candidates and confidence but does not detail the return format (e.g., JSON structure, array of objects) or edge cases like empty results or error behavior. It is adequate but incomplete for a fuzzy-matching 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 100% with each parameter having a description. The description itself adds no parameter-specific detail beyond the schema—e.g., the mention of name variations duplicates the schema's name description. 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 resolves corporate numbers from corporate names, returning candidates with confidence scores and reason labels. It specifies the input variations (kana/kanji, orthographic variants) and names the data source, making the purpose distinct from siblings like verify_company.

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 usage for fuzzy name-to-number resolution (due to mention of variants and confidence), but it does not explicitly state when to use this tool versus alternatives like verify_company or get_company_profile. There is no when-not/exclusion 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 provided, the description carries the transparency burden. It discloses the check digit validation, data source (National Tax Agency Web-API), and the optional history inclusion, which implies a read-only external lookup. However, it does not specify error handling, authentication requirements, or the exact return structure.

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 exactly two sentences, with the primary purpose front-loaded and the optional behavior and data source in the second sentence. Every word is informative, with no redundancy or 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 tool has a small parameter set and no output schema, yet the description manages to enumerate the returned fields and mention the key option and data source. It lacks error behavior and response format details, which would be useful but not critical for this simple verification 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?

Both parameters are already described in the schema with full coverage, so the baseline is 3. The description adds minor context by mentioning check digit validation for corporate_number and clarifying the effect of include_history, but these largely overlap with the schema descriptions.

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: it takes a 13-digit corporate number (with check digit verification) and returns existence, trade name, location, corporate type, and closure status. This specific verb+resource combination distinguishes it from siblings like resolve_company.

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 usage for verifying a corporate number's existence but does not explicitly state when to prefer this tool over alternatives like resolve_company or get_company_profile. It lacks exclusions or conditional guidance, so the agent must infer the appropriate context.

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 the full burden. It discloses the data source (国税庁適格請求書発行事業者公表システムWeb-API), the return content (registration status, date, cancellation/expiration), and the effect of optional parameters. It does not mention error handling or rate limits, but the core behavior is transparent and the official source adds trust.

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 sentences long, front-loaded with the core function, and every sentence adds value: what it does, how options work, what is not supported, and the data source. No fluff or redundant restatement.

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 tool with three parameters and no output schema, the description covers the essential aspects: purpose, input constraint, behavior of options, and data source. It does not specify the exact response field names or structure, but it names the kinds of data returned. This is sufficiently complete for an agent to select and invoke the tool effectively.

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 all three parameters are already documented in detail. The description adds a high-level constraint (input must be a registration number only) but does not materially extend the parameter syntax or format beyond what the schema already provides. The baseline of 3 is appropriate since the schema does the heavy lifting.

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 a specific verb and resource: it verifies a qualified invoice issuer registration number and returns registration status, registration date, and cancellation/invalidation. It also distinguishes from siblings by explicitly noting that search by name is not provided, making it clear this tool is for number-based verification only.

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 clear context for when to use this tool—when you have a registration number—and explicitly states an exclusion: search by name is not supported. It does not name alternative sibling tools, but the when-not guidance is strong and the parameter behaviors for on_date and include_history are explained.

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