predict_corporate_demand

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

Annotations already indicate readOnlyHint=true and destructiveHint=false, so the description carries a lower burden. It adds that the tool covers 10 prefectures, but does not disclose data freshness, limitations, or other behavioral traits. No contradictions.

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 short sentences in two languages, front-loaded with key information, and contains no fluff. Every part 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 5 parameters and no output schema, the description is too brief. It does not explain the output format, score interpretation, or which 10 prefectures are covered. The mismatch between described sectors and schema enums further reduces 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 description coverage is 80%, with most parameters having descriptive names or explanations. The tool description adds high-level context about sectors (manufacturing, office, retail) but does not align perfectly with the enum values (office, logistics, commercial, mixed). It does not compensate for the missing propertyType description or add syntax details.

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 predicts corporate demand scores for manufacturing, office, and retail across 10 prefectures. It uses a specific verb 'predict' and identifies the resource 'corporate demand'. However, it does not explicitly differentiate from sibling tools like forecast_demographic_shift, though the focus on corporate demand is implicit.

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 corporate demand prediction but provides no explicit guidance on when to use this tool versus alternatives, such as when to query demographic forecasts or land prices. No exclusions or prerequisites are mentioned.

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