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

No annotations exist, so the description carries the full burden. It does disclose data sources and freshness ('today's cheapest price') and lists returned content, but it does not state read-only behavior, auth needs, error handling, or what happens when no data exists. The description is informative 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?

Two short Japanese sentences cover output content, data sources, parameter provenance, and an example with no fluff. The output description is front-loaded and the slug note is placed exactly where it is needed.

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 read-only lookup with only one required parameter, the essentials are present: what data is returned and how to obtain a valid slug. However, no output schema exists and mount is undocumented, so return format and the optional parameter behavior remain open questions.

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 0% schema coverage, the description must document parameters itself. It explains slug's provenance and gives an example, but the optional mount parameter is completely undocumented. This leaves one of two parameters semantically unexplained.

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 precisely identifies the tool's data product: today's lowest price (Rakuten/Yahoo!), daily history, and specs for a single interchangeable lens. It also distinguishes this from index-level siblings by stating the slug comes from lens_index and giving a concrete example.

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 phrase 'slug は lens_index の slug' implies a workflow of first getting a slug from lens_index, but it never states when to prefer lens_item over lens_index or any other sibling. Usage context is present but left to inference, with no explicit when-not-to-use guidance.

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