亞洲公仔扭蛋情報站

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

No annotations are provided, so the description carries full burden. It discloses that results include user reports and discussions, which adds behavioral context beyond a simple data retrieval. However, it does not mention permissions, rate limits, or output format. For a tool with no annotations, more transparency (e.g., what constitutes 'reports', any privacy considerations) would be beneficial.

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 a single, well-structured sentence that front-loads the primary function and then lists additional data. It is concise and every element 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 the tool's complexity (returns multiple data types) and no output schema, the description should ideally explain the return structure more or note that it may include nested data. It covers the main content but lacks details on the identifier parameter and doesn't fully compensate for missing annotations. Adequate but with clear gaps.

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 0% (only 'id' with no description). The description does not mention the 'id' parameter at all, leaving it undocumented. Since there is only 1 parameter and the tool clearly requires an identifier, the gap is less severe, but the description fails to clarify what 'id' refers to (e.g., product ID, internal ID) or its format.

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 function: fetch detailed information about a single toy, including user availability reports (in-stock/sold-out, city & store name) and discussions. This is a specific verb+resource (get single toy details + reporting). However, it could be more distinguishing from sibling tools like 'latest_toy_news' or 'search_toys' by explicitly noting it's for a single item by ID rather than a broader 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 implies usage (to get details for a specific toy) but does not provide explicit when-to-use guidance or alternatives. No mention of when to use this versus 'search_toys' or 'latest_toy_news'. Without explicit routing, the agent must infer.

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?

No annotations are provided, so the description carries the full behavioral burden. It hints the content is community-sourced ('網友情報') but says nothing about ordering, freshness limits, item counts, pagination, or what a result looks like for a read tool with a 0%-documented parameter.

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 short, front-loaded fragment with no wasted words, which is good. However, it is a bare noun phrase with no verb or sentence structure, so the reader must infer that this is a fetch operation.

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?

With no output schema and no annotations, the description must explain return behavior, freshness semantics, and source reliability on its own. Instead it only lists categories, leaving what 'latest' means and how results are delivered undefined.

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%, but there is only one optional parameter and its enum values are self-describing; the description enumerates matching categories (official/news/rumor/video/unbox/discuss), which at least maps meaning to the filter. This is largely a restatement of the enum, so it adds only marginal value.

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 phrase '最新網友情報' (latest netizen info) conveys a retrieval intent, and the enumeration of categories clarifies breadth, but there is no clear verb+resource framing and nothing distinguishes it from siblings like list_meets or release_calendar. The listed categories largely restate the enum rather than define a distinct purpose.

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?

No when-to-use guidance, no prerequisites, and no mention of alternatives such as get_toy or search_toys. The agent is given no signal about when this tool is preferable to its siblings.

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?

No annotations are provided, so the description carries the full burden. It discloses the conceptual content of each listing (have/want/method/region), which is modest value, but says nothing about pagination, sorting, filtering behavior, volume, or authentication — all relevant for a list endpoint with zero annotation coverage.

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 beginning with the verb 列出, with the parenthetical enumerating the fields. No wasted preamble, though the packed parenthetical makes it slightly dense.

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?

With no output schema and no annotations, the description does describe what a listing contains, which covers the essentials for a read-only list tool. However, it omits return volume/pagination and the exact role of the city filter, leaving gaps for an agent to call it accurately.

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?

Only one parameter (city), and schema description coverage is 0%, so the schema contributes nothing beyond the enum. The description's mention of 地區 (region) loosely signals that a region/city dimension exists, but it never explains how the city parameter filters results or how '其他'/'東京' style values behave. Marginal compensation for the coverage gap.

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?

States a specific verb (列出/list) and resource (扭蛋公仔交換/gashapon figure trades) with the scope qualifier 徵求中 (currently seeking/open). It is clearly distinguishable from all siblings (get_toy, search_toys, toy_stats, etc.), none of which deal with trades, though it never names or contrasts them.

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 lists what the listing contains (what they have, what they want, exchange methods, region) but never says when to use this tool versus alternatives, nor any precondition for calling it. No when/when-not guidance at all.

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 provided, the description carries the full behavioral burden. It identifies the operation as a retrieval (取得) and the grouping basis, but says nothing about permissions, pagination, whether an empty month returns an empty list, or how results are ordered beyond the vague 依官方發售時期.

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, appropriately sized for a simple two-parameter read tool. It is efficient, though the brevity borders on under-specification given the gaps elsewhere.

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?

With no output schema, no annotations, and half the parameters undocumented, the description should explain the return shape and the brand filter's role. It does neither, leaving an agent unable to predict what a call yields or how to use the optional parameter.

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 only 50%: month has a format description (YYYY-MM) but brand is an enum with no explanation. The description implies a month scope but never mentions the optional brand filter, its default, or whether omitting it returns all brands.

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?

States a concrete verb (取得) and resource (扭蛋與公仔發售表), plus the scope (某個月, 依官方發售時期), so an agent knows this returns a monthly gashapon/figure release schedule. It does not, however, distinguish itself from siblings like search_toys or latest_toy_news that could also surface release information.

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 explicit statement of when to use this tool versus search_toys, latest_toy_news, or toy_stats. The phrase 依官方發售時期 hints at the ordering basis but gives no conditions for selection or exclusion relative to alternatives.

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 does disclose meaningful return content (release period, price, status, limited-edition flag, source) and enumerates the status vocabulary, which substitutes for a missing output schema. However, it says nothing about permissions, rate limits, ordering, or what the limit parameter controls.

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 pairs the action and scope with the expected return fields, with no filler. It is dense but every clause carries information; the only cost is that the parameter and return details are crammed together rather than separated.

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 read-only search with no output schema and no annotations, the description does describe the returned fields and status vocabulary, which is the most important gap to fill. It remains incomplete because four of five parameters (brand, category, limit, and the enum semantics) get no explanation and there is no note on result limits.

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 0% across 5 parameters, so the description must compensate and largely does not. It hints at query language and enumerates the status values that map to one enum, but the brand enum values, category enum values, and especially the limit parameter's effect/pagination semantics are left entirely 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 opens with a specific verb (搜尋/search) and a concrete resource scope (Asian gashapon, blind boxes, figures, models), and even characterizes the searchable text as Chinese or Japanese. It does not explicitly distinguish itself from siblings like get_toy or latest_toy_news, so it stops short of a 5.

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 explicit when-to-use or when-not-to-use guidance, and no sibling is named as an alternative. The only steering is the note that the query may be Chinese or Japanese with example titles (寶可夢, ちいかわ, 鋼彈), which implies content but not selection criteria.

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?

No annotations are provided, so the description must carry the full behavioral burden. It does not state whether the tool is read-only, whether it requires authentication, or whether it has side effects, rate limits, or caching behavior. It only lists the returned fields, which is return-value information rather than behavioral trait disclosure.

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 a single, front-loaded sentence with no wasted words. It immediately conveys the tool's output without preamble or redundancy.

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 the simple, parameterless nature of the tool and the absence of an output schema, the description adequately enumerates the returned statistics (product count, member count, official data update time). It could be slightly more complete by mentioning the return format or that it is a read-only operation, but for a zero-parameter stats tool it is largely sufficient.

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 has zero parameters, so there are no parameter semantics to describe. The baseline for a parameterless tool is 4, and the description adds no param-related meaning because none is needed.

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 resource it provides: site-wide counts of products, members, and the last official data update time. It distinguishes itself from sibling tools like get_toy and search_toys, which are about individual items rather than aggregate statistics. However, it uses a noun phrase rather than a verb, so the action is implied rather than explicit.

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 use this tool versus alternatives, nor any mention of prerequisites or exclusions. The description only says what data is returned, leaving the agent to infer that it should be called when aggregate site statistics are needed.

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