jp-biz-opportunity

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

With no annotations, the description carries full burden. It discloses that the tool is paid, returns an HTTP 402 payment challenge, and requires settlement for access. This adds value beyond basic read expectations, though it does not explicit state it is non-destructive.

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, with three sentences each serving a distinct purpose: what the tool does, the payment mechanism, and the beta note. 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 no output schema, the description covers the return value fields (sponsor, max grant, etc.) and the payment flow sufficiently. It lacks error handling or authorization details but is adequate for a single-param 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 single parameter 'subsidy_id' is described both in the schema (as a live jGrants id) and in the description (which provides example format). The description also explains what fields the record contains, adding meaning beyond the schema alone.

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 that the tool retrieves the full raw record of a specific Japanese subsidy/grant program, listing key fields (sponsor, max grant amount, etc.). This distinguishes it from sibling tools like search_subsidies which would return a list.

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 payment model (x402, cost, settlement) and the beta status, providing important context for usage. However, it does not explicitly state when to use this tool over alternatives, though the purpose implies it is for fetching a single detailed record.

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 fully discloses the payment requirement, HTTP 402 response, beta limitations, and settlement process. This is critical behavioral information beyond a simple retrieval tool.

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 four sentences, front-loading purpose then adding payment and beta details. Each sentence is relevant, though the beta note could be shortened slightly.

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?

Covers purpose, parameters, and payment, but lacks response format details (e.g., exact fields returned, error cases). With no output schema, describing the response would improve completeness.

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 has 100% coverage with a description for tender_id. The tool description adds no extra parameter meaning, so 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 it retrieves the full raw record of a Japanese public-sector open tender notice, listing key fields. It distinguishes itself from sibling search tools by focusing on a single record detail.

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 paid access model (0.05 USDC per call, HTTP 402 challenge) and notes the private beta, giving clear context for when to use. However, it does not explicitly compare to alternatives or state when not to use.

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?

Discloses pricing (0.05 USDC per call) and clarifies it is a free catalog lookup (read-only, no data retrieval). No annotations provided, so description carries full burden and does so effectively.

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 essential: states it's a free catalog, lists dataset types with pricing, then provides next-step guidance. 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?

Covers purpose, pricing, and follow-up actions. Lacks explicit description of the output structure (e.g., array of objects with fields), but given the simplicity and mention of api_url, it is mostly sufficient.

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?

No parameters exist, so baseline is 4. Description adds value by explaining the tool's purpose, but no parameter details are needed.

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?

Clearly states the tool lists Japanese public-sector business-opportunity datasets, specifying types (open tender notices, subsidy programs, procurement sanction lists). Differentiates from siblings by focusing on the catalog listing vs. specific get/search operations.

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 advises using search/get tools or the returned api_url for data retrieval, and mentions pricing. Could be more direct about when not to use this tool, but context is 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 provided; description adds minimal behavioral context ('FREE — returns live data', 'Attribution included') but does not disclose side effects, data freshness, or whether it is a destructive/mutating operation.

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?

Description is concise (two sentences), front-loads key information (FREE, live data), and avoids unnecessary words. 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?

Given 3 parameters and no output schema, description covers search functionality well, mentioning keyword, region, and deadline filter, and states return includes notice text excerpt and URL. Could mention pagination or result count for completeness.

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%, but description adds meaningful context beyond schema: keyword is required (min 2 chars) and region is folded into the query, clarifying parameter usage.

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 public-sector tender notices by keyword, specifying the resource (tender notices) and verb (search). It distinguishes from siblings by focusing on open tenders.

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?

Description mentions 'FREE — returns live data' and provides usage hints like keyword minimum 2 chars and region folded into query, but does not explicitly state when to use vs alternatives or when not to use.

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?

Despite no annotations, the description discloses critical behavioral traits: paid per call (0.05 USDC), returns an HTTP 402 payment challenge, and describes the private beta context. However, it does not detail what data is returned after successful payment or any other side effects.

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 that are front-loaded with the core purpose, then efficient addition of payment and beta details. No superfluous 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?

While the description covers the payment mechanism and beta status, it does not describe the output format of the sanctions data itself, which is a significant gap given the absence of an output schema. The tool's main value (the records) is not explained.

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 the description reinforces the parameter by specifying '13-digit Japanese corporate number (法人番号)' with an example. This adds practical usage context beyond the schema's generic description.

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 'Look up suspension-of-nomination and administrative action records for one Japanese corporate number', providing a specific verb and resource. The title further clarifies the Japanese context. This clearly distinguishes it from sibling tools like search_open_tenders or search_subsidies.

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 it is for one corporate number, but does not offer any guidance on when to use this tool versus alternatives, nor does it provide any exclusions or prerequisites. Lack of comparative usage 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?

With no annotations, the description carries the full burden. It states the tool is free, returns live data, and includes attribution, which adds transparency. However, it omits details on rate limits, pagination, or what happens if no results are found.

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: two sentences with a front-loaded hook ('FREE — returns live data'). Every sentence adds value without redundancy.

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 no output schema, the description could explain the return structure or result format. It is adequate for a simple search tool but lacks completeness around output expectations and edge cases.

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 useful constraints: keyword is required (min 2 chars), which is not present in the schema, thereby enhancing parameter understanding despite a slight mismatch.

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 it searches currently-open Japanese subsidy and grant programs, listing specific searchable fields (titles, deadlines, max amounts, target areas). It distinguishes from siblings like search_open_tenders and search_sanctions by its specific domain.

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 notes that keyword (min 2 chars) is required and mentions free, live data, setting clear context. However, it does not explicitly state when to use this tool over siblings or provide exclusion criteria.

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