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

The description discloses key behaviors beyond annotations: it supplements outdated public info with user input and returns sources/discrepancies, handles verification steps, instructs not to assert eligibility, and details how to format user-facing responses (e.g., not showing English codes). No contradiction with annotations (readOnlyHint=false, openWorldHint=true).

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 long but densely packed with actionable instructions, structured logically from core function to specific handling and output rules. Every sentence contributes to correct usage, though a slightly tighter wording could improve readability without losing 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 is comprehensive: it covers data retrieval, supplementation, verification steps, output formatting, internal vs. user-facing fields, and response guidance. It also anticipates common pitfalls (e.g., not excluding based on capital relations alone) and directs to sibling tools, leaving no critical gap for an agent to call 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 covers 100% of parameters with descriptions. The description adds extra meaning by clarifying that user-specified values override public data (e.g., '指定時は公開法人情報より優先'), and explains the role of corporate_number. It does not deeply explain business_plans or edge cases, but the added context is valuable, so above 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?

The description states a specific verb (retrieve and match) and resource (gBizINFO corporate profile against J-Grants subsidy conditions), clearly distinguishing it from siblings like get_company_profile or search_companies. It also names the input (corporate number and subsidy ID) and the action of evaluation.

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?

It explicitly states when to use this tool (when you have a corporate number and subsidy ID) and gives conditional guidance: for SME requirements, use verify_corporate_relationship and assess_deemed_large_enterprise_eligibility for specific sub-steps. It also instructs on how to present results and follow responseGuidance, making the selection and invocation context clear.

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