pc-okaidoku-store

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

No annotations are provided, so the description must carry the full behavioral burden. It does disclose the payload (price, specs, spec PDF, product page URL), which is useful, but it says nothing about not-found behavior, whether id takes precedence over name, or what happens when neither is supplied.

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?

It is a single, front-loaded sentence with no wasted clauses. The lookup key comes first and the returned fields follow.

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?

There is no output schema, and the description usefully lists the return contents, so the retrieval contract is reasonably covered. However, the ambiguity between id and name with 0 required parameters means an agent can still invoke the tool incorrectly.

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 0%, so the description must compensate. It does by mapping the two parameters to their meaning (product ID or product name), but it leaves the id/name precedence and the all-optional case undocumented.

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 names a specific verb (取得) and a precise resource (商品1件の詳細), and enumerates the returned fields (価格・スペック・仕様PDF・商品ページURL). The phrase 商品1件 makes clear this is a single-item lookup, which distinguishes it from search_products, but the description never names that sibling explicitly.

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?

There is no statement of when to use this tool versus search_products or list_categories. The description does not say whether id and name are alternatives or what to do when both are provided, despite both being optional (0 required).

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?

With no annotations, the description carries the full behavioral burden. It discloses the return content (categories with counts), which implies a read-only aggregation, but says nothing about ordering, pagination, or permission requirements. That is adequate for a trivial zero-parameter lookup but not rich.

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?

A single front-loaded sentence with no filler. It is arguably too terse to be maximally useful, but nothing in it is wasted.

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 no-argument list tool with no output schema, the description covers the essentials: the enumeration and the accompanying counts. It still leaves open the ordering of results and the shape of the count field, which the agent would need in order to use the output confidently.

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 tool takes zero parameters and the schema is an empty object, so there is nothing for the description to disambiguate. Baseline 4 applies; no misleading parameter hints are present.

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 gives a specific verb ('返します' / returns) and resource ('取扱いカテゴリ' / handling categories) plus the payload shape ('一覧と件数' / list and counts). It is clearly distinct from the sibling tools get_product and search_products, although it never says so explicitly.

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?

There is no guidance on when to call this instead of get_product or search_products, and no prerequisites or exclusions. An agent must infer usage purely from the resource name.

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?

With no annotations, the description carries the full behavioral burden. It usefully states that all prices are tax-included JPY, but it does not disclose result ordering, pagination behavior, whether the search is read-only, or how limit affects results beyond the schema.

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 compact sentences with no filler: the first establishes the store and search purpose, the second summarizes filters and pricing. It is front-loaded and easy for an agent to parse.

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 five-parameter search tool with no output schema, the description identifies the store, available filters, and currency/tax basis. It is adequate but incomplete on result behavior such as ordering, empty results, and how pagination or limit is applied.

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%, so all five parameters are already documented in the schema. The description adds the useful currency/tax context for min_price and max_price, but otherwise does not add parameter meaning 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 states a specific verb and resource: searching products in the lanitech PC bargain store, with keyword/category/price filtering. It is clear enough to distinguish from get_product and list_categories by its search-and-filter nature, although it does not explicitly name those siblings.

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 filtering dimensions imply when to use this tool for product discovery, but there is no explicit guidance on when to prefer get_product for details or list_categories for browsing categories. Usage is inferable rather than stated.

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