Japan Company Leads Finder MCP

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

No annotations are provided, so the description must cover behavioral traits. It does not mention data freshness, rate limits, authentication requirements, or what happens if the corporate number is invalid. For a retrieval tool, such details are important for agent decision-making.

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 consists of two clear sentences covering purpose and use case. It is concise and front-loaded, with no extraneous information. Minor improvement could be to structure as bullet points for clarity.

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 (single parameter, no output schema, no annotations), the description covers basic purpose and use cases. However, it lacks usage guidelines and behavioral details, making it only moderately complete for agent 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?

The input schema fully describes the single parameter 'corporate_number' with length constraints and description. The tool description does not add any additional meaning or context beyond what the schema already provides. With 100% schema coverage, 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 title and description clearly state the tool retrieves government procurement records for a company given a corporate number. The verb '取得する' (acquire) and resource '企業の政府調達実績' are specific. It distinguishes itself from sibling tools like get_company_profile and get_company_subsidies by focusing on procurement 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?

The description mentions use cases: finding companies strong in official procurement and credit checks. However, it does not provide explicit when-to-use or when-not-to-use guidance, nor does it compare to sibling tools like get_company_subsidies or search_companies. Usage context is implied but not fully developed.

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 full burden. It discloses the returned fields and use case, but it does not mention error behavior (e.g., invalid corporate number), authentication requirements, or rate limits. This is adequate but not fully 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 a single concise sentence front-loaded with the main action, followed by a colon and a list of example fields. No wasted words or 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?

The tool has one parameter and no output schema. The description lists many output fields, providing sufficient context for what the tool returns. However, it does not specify the output format or error handling, so it is not fully 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 description coverage is 100% with 'corporate_number' described as '法人番号（13桁）'. The description adds context that the number is specified to get the profile, but no additional parameter semantics beyond the schema. Baseline 3 applies.

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 title and description clearly indicate that the tool retrieves a detailed company profile given a corporate number. The description lists specific fields (representative, employees, capital, etc.) and states its use case for sales research. It is easily distinguishable from sibling tools (procurement, subsidies, 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 states the tool is used for 'sales approach research,' providing a clear context. However, it does not explicitly exclude other cases or mention alternatives among siblings, though the sibling names and purpose differences imply proper usage.

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 description carries full burden. It states the tool retrieves subsidy history (read operation) but does not disclose authentication, rate limits, or response format. Adequate but not detailed.

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: first clearly states function, second provides practical usage example. No wasted words, front-loaded.

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 and no output schema, description covers purpose and usage adequately. Missing return format but not critical for selection.

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 one parameter described. The description adds no new meaning beyond specifying the corporate number, matching the schema. 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?

Description uses specific verb '取得する' and resource '補助金の履歴', clearly distinguishing from siblings (get_company_procurement, get_company_profile, search_companies). It also provides concrete business use cases.

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 use case for discovering companies with budgets and investment willingness, providing clear context. However, it does not explicitly state when not to use or contrast with alternatives.

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 discloses the data source (gBizINFO, over 1 million companies) and available filters. It does not mention exact query latency or update frequency, but it is sufficiently transparent for an agent to understand 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 a single, well-structured paragraph. It front-loads the purpose, lists key filters, and ends with downstream usage instructions. 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?

With 10 optional parameters and no output schema, the description effectively covers the tool's capabilities. It explains the output contains corporate_number and how to use it. While return fields are not detailed, the description is complete enough for an agent to use the tool 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% (all parameters have descriptions). The description adds extra meaning by grouping filters and explaining that 'with_subsidy_history' signals budget acquisition ability and investment willingness, which goes 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 tool searches for sales leads from the Japanese corporate database gBizINFO. It specifies the verb '検索する' (search) and the resource (法人データベース). It distinguishes from siblings by mentioning downstream tools get_company_profile and get_company_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 indicates when to use the tool (for searching business leads) and how to proceed with results (use corporate_number with other tools). However, it does not explicitly state when not to use it or list alternatives for different scenarios.

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