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

No annotations are provided, so the description must fully disclose behavior. It only states the tool retrieves procurement results, without details on data freshness, pagination, rate limits, or any restrictions. This is insufficient for safe agent invocation.

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 sentences: first defines function, second lists use cases. No redundancy or unnecessary words. Efficient but could be slightly more compact.

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 is adequate for a one-parameter retrieval tool without output schema, but lacks details on the output structure (e.g., what procurement results contain). Useful context is provided for use cases, but gaps remain.

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 a clear description for the single parameter (corporate number with length constraints). The tool description adds no additional meaning beyond what the schema already provides, achieving baseline.

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 specifies the action (取得する/retrieve) and resource (政府調達実績/government procurement results) with the required parameter (corporate number). It distinguishes from siblings by specifying government procurement, distinct from profile, subsidies, or 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?

Description states use cases: discovering companies strong in government procurement and credit checks. It provides clear context for when to use, but does not explicitly state when not to use or mention alternative tools.

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, so the description carries full burden. It explains the data returned (representative, employee count, capital, etc.). Since it's a read operation, no destructive behavior is implied, and the description is transparent about the output fields.

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 sentence that efficiently conveys purpose, data fields, and usage context. No superfluous 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 tool with one parameter, no output schema, and no nested objects, the description is complete. It explains what the tool does, what data it returns, and provides a use case.

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 the parameter 'corporate_number' described as '法人番号（13桁）'. The description does not add additional meaning beyond the schema, 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 the tool fetches a detailed company profile by corporate number, listing specific fields like representative name, employee count, capital, etc. It distinguishes from sibling tools focusing on procurement, subsidies, or 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 includes a usage hint ('営業アプローチの下調べに使う') indicating it's for sales research. It doesn't explicitly exclude other contexts or compare with siblings, but the purpose is clear enough.

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. The description does not disclose behavioral traits such as rate limits, authentication requirements, or what happens on invalid input. For a read operation, safety profile is not mentioned.

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, the second adds a useful use case. No redundant text, front-loaded with purpose.

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?

No output schema exists, but the description does not explain what the response contains or any error handling. For a simple tool, a hint about return format 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?

Schema coverage is 100% with parameter description. The description repeats the parameter usage but adds no new semantic details beyond the schema's 13-digit constraint. 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 retrieves subsidy history for a company by corporate number using the specific verb '取得する' and resource '補助金の履歴'. It distinguishes itself from siblings focused on procurement, profile, and 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 a concrete use case for discovering investment-willing companies, implying when to use the tool. However, it does not explicitly mention when not to use or compare to alternatives, though siblings are 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 are provided, so the description must carry the full burden. It mentions the data source, database size, and filter criteria, but does not describe pagination behavior, rate limits, data freshness, or the exact fields returned in the response. The default sorting order and query limits are not disclosed. While adequate, there are gaps that an agent would benefit from.

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 paragraph that starts with the purpose, lists key filters, and ends with usage guidance for sibling tools. Every sentence adds value, and there is no redundancy or fluff. It is concise yet informative.

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 moderate complexity (10 parameters, no output schema), the description provides a good overview but does not specify the response format or what fields (besides corporate_number) are returned. It does not mention sorting, result ordering, or any limitations on combining filters. The connection to sibling tools is helpful, but completeness is adequate, not excellent.

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 the baseline is 3. The description adds some extra context for with_subsidy_history (signals budget-winning ability) but largely just lists filter options without adding significant meaning beyond the schema. The description does not deeply explain parameter usage or constraints (e.g., how partial matching works for name).

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 for Japanese company data from a specific government database (gBizINFO) for sales leads, listing multiple filter criteria (prefecture, employees, capital, etc.). It also explains how the output (corporate_number) can be used with sibling tools (get_company_profile, get_company_subsidies), effectively distinguishing this search tool from the detail retrieval 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 description implies when to use this tool: for initial discovery of potential leads, and that the resulting corporate_number can be passed to get_company_profile and get_company_subsidies for detailed information. It does not explicitly state when not to use or provide alternatives, but the context with siblings is clear enough for an AI agent to understand the workflow.

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