ai-furniture-hub

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes what the tool returns (31 categories with product counts and brands) and that it can filter to specific categories, but doesn't mention rate limits, authentication requirements, error conditions, or response format details. It provides basic operational context but lacks comprehensive behavioral information.

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 efficiently structured in three sentences: first establishes the entry point purpose, second specifies what data is returned and filtering capability, third provides the usage workflow. Every sentence adds value with zero waste.

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 relatively simple read-only tool with 2 parameters and no output schema, the description provides good context about what data is returned and how to use it in the workflow. However, without annotations or output schema, it could benefit from more detail about the response structure and any limitations.

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?

With 100% schema description coverage, the baseline is 3. The description adds value by explaining the practical use of category_filter with examples ('キッチン収納', 'デスク') and clarifying that intent is required for understanding user purpose, which goes beyond the schema's technical documentation.

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's purpose: it's an entry point tool that returns all 31 categories with product counts and handled brands, and can also retrieve products for a specific category. It distinguishes itself from siblings like search_products by focusing on category discovery rather than product 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 explicit usage guidance: use when users ask 'what can be searched?' or 'what categories exist?', first use this tool to understand categories, present them to users, then proceed to search_products with the selected category. It clearly differentiates from the search_products sibling tool.

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