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

Annotations already declare readOnlyHint and openWorldHint, so safety is covered. The description still adds meaningful semantics beyond the annotations: the price is a store-verified RMB landed price with in-stock prioritization, which tells the agent how to interpret the returned figure rather than just that it exists.

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?

One sentence, no filler, and the core action (lowest purchasable price) is front-loaded before the secondary return facets. Everything stated 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?

With an output schema present, return values need not be spelled out, and the annotations carry the safety profile. The description is complete for calling the tool correctly; only routing guidance against siblings is absent.

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 100% for the single sku parameter, including examples and the suggestion to use search_products first. The description adds no additional parameter meaning, so the baseline of 3 applies – the schema carries this dimension entirely.

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 and resource (查询某个 AI 产品的可买最低价) and enumerates the returned facets: price range, shop count, and the source shop. It is clearly distinguishable from search_products or list_all_products by its price-centric scope, though it never names a 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?

Usage is implied: you call it when you need the lowest purchasable price for a known SKU. There is no explicit when-to-use, when-not-to-use, or alternative (e.g., get_offers for deeper offer lists, get_price_trend for history). The note about using search_products first lives in the schema, not the description.

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?

Annotations already indicate readOnlyHint and openWorldHint. The description adds sorting behavior ('in-stock first, then final price'), notes that RMB price includes payment fees, and lists returned fields (store name, price, stock, link). This provides useful behavioral context beyond annotations, though it does not cover rate limits or auth needs.

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, dense sentence that front-loads the verb and resource, then packs sorting order, return fields, and use cases. Every clause contributes information with no wasted words, making it highly concise and well-structured.

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 read-only nature, full schema coverage, annotations, and an output schema, the description is largely complete. It covers purpose, usage, sorting, and return fields. Minor gaps (e.g., not mentioning the limit parameter's effect) are covered by the schema, so the description needn't repeat them.

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 parameters (sku, limit, include_out_of_stock) are documented in the schema. The description mentions '某个 AI 产品' (a certain AI product) which implicitly maps to sku, but it adds no additional semantics for limit or include_out_of_stock beyond what the schema provides. Baseline 3 is appropriate.

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 uses a specific verb (列出/list) and resource (各店报价/quotes across stores) for an AI product. It states the sorting order and returned fields, making the tool's purpose clear. However, it does not explicitly distinguish from siblings like get_lowest_price, which could cause confusion for an agent deciding between listing all offers versus just the lowest price.

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?

It provides clear usage context: '用于回答「哪家店最便宜」「去哪买」' (used to answer which store is cheapest and where to buy). This tells the agent when to use it, but it does not mention when not to use it or name alternative tools like get_lowest_price or search_products, leaving some ambiguity about sibling selection.

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?

Annotations already declare readOnlyHint and openWorldHint, so safety is covered; the description adds a fixed 30-day window, daily granularity, and consistency with the site's chart — context the annotations don't provide. It omits edge behavior (e.g., sparse data, timezone/window definition), keeping it from a 5.

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 sentences, both earning their place: the first defines scope and returned fields, the second defines the questions it answers. No filler and the core resource is front-loaded.

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 single-parameter read tool with an output schema and readOnly annotations, almost everything needed is present. Minor gap: no statement about behavior when no data exists for the SKU or whether the 30-day window is calendar-fixed.

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 100% and the sku parameter already carries its own description with examples ('claude_pro', 'gpt_plus_topup'). The description adds no further meaning about the parameter, so the baseline 3 applies.

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 and resource (query the last 30 days of daily lowest purchasable price for a given AI product) and even pins the semantics to the detail-page chart ('同详情页走势图同口径'). The listed outputs (start/end price, high/low points, change percent) make it clearly distinct from siblings like get_lowest_price, which is a point-in-time lookup.

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?

Gives concrete use-case triggers — answering 'is it rising or falling lately' and 'is now a low point' — which maps directly to agent intent. It does not name the alternative tool (get_lowest_price) or state when not to use this, so it stops short of full routing guidance.

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?

readOnlyHint=true and openWorldHint=true already establish this as a safe global read, so the description's burden is low. It adds that the payload is an aggregate snapshot including a data-update time, which is mild but useful context; it does not mention caching or freshness behavior.

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?

One sentence, front-loaded with the verb and scope, then the returned metrics. No filler or repetition; every clause 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?

With an output schema present, no parameters, and annotations covering the read-only/open-world profile, the description supplies everything an agent needs to select and invoke this simple aggregate tool.

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, so the baseline is 4. There is nothing for the description to disambiguate beyond confirming the call is argument-free, which the empty schema already conveys.

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 and resource (获取...整体统计) and enumerates exactly what is counted: shops, Telegram bots, product categories, total products, and update time. This clearly separates it from the search/list/price siblings, though it does not name which sibling is the alternative.

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 or when-not-to-use guidance is given. The description implies 'call for an overview', but an agent gets no explicit condition for choosing this over list_shops or list_all_products, nor any note that it takes no arguments.

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?

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is covered by structured data. The description usefully adds that results come back as in-store price ranges, but for a tool that returns ALL products it says nothing about result size, truncation, or pagination limits — a meaningful omission for a full-catalog listing.

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 well-formed sentence with the resource up front and the benefit clause last; no filler or repetition. It could have spent that second clause on a routing hint, but nothing 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?

With an output schema present, return values need not be described, and annotations cover safety. What is missing for a catalog-wide enumeration tool is any caveat about volume, pagination, or cost of an unfiltered call — the one thing an agent must know before invoking it.

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%, and the schema already documents both sort (with per-value semantics) and has_price_only. The description adds nothing about either parameter, so the baseline 3 applies — the schema does all the work here.

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+resource (列出所有可比价的 AI 产品/SKU) and even states the payload (店内到手价区间), which is more concrete than the title. It distinguishes itself from search_products mainly through '全部' and '门店覆盖范围', but never names the sibling explicitly, so the contrast is implied rather than stated.

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 trailing clause 方便了解全站覆盖范围 hints at the intended use case (a coverage/site-wide overview rather than a targeted lookup), which is more than nothing. However it never says when NOT to use this tool or points to search_products / get_lowest_price for filtered or single-item queries, so an agent must infer the routing.

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?

Annotations already declare readOnlyHint=true and openWorldHint=true, so safety and open-world behavior are covered. The description adds the sort order (cumulative sales) and the filterable dimension, but the enumerated return fields duplicate what the output schema already provides, so it adds only modest context.

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 ordered as purpose then scope then filter. It is efficient, though the trailing list of return fields is redundant given the output schema and slightly dilutes the signal.

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?

Both parameters are fully documented in the schema, the output shape is described by an output schema, and the annotations cover the safety profile. The only real gap is the absence of routing guidance relative to sibling list/search tools, which is minor for a simple read-only listing.

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%: the schema documents limit (default 10, max 30) and platform with concrete example values. The description only restates the platform filter and adds nothing on syntax or format, so baseline 3 applies.

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) and resource (水獭收录的 AI 订阅店铺), plus the ordering rule (按累计销量) and the shop categories covered (卡网、独立站). An agent can instantly tell this is about shops rather than products, distinguishing it from siblings like search_products and list_all_products.

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 optional platform filter is mentioned ('可按平台筛选'), which implies when to use it, but there is no guidance on when to choose this tool over alternatives such as list_all_products, nor any exclusion or prerequisite. Usage must be inferred from the domain.

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?

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety and open-domain profile is covered. The description usefully adds that queries accept Chinese, English, and common colloquialisms, but says nothing about ordering, empty-result behavior, or how price ranges are computed — context beyond annotations is thin.

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 sentence, front-loaded with verb, scope, and return summary, then a second short clause on query flexibility. No filler and nothing repeated from the schema.

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 an output schema present, return values need not be explained, and annotations carry the safety profile; the description nonetheless summarizes key returns. The remaining gap is routing relative to the six sibling tools, which leaves the agent to guess between search and listing tools.

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 100%, so the baseline is 3, but the description genuinely extends query semantics by naming supported product families and colloquial aliases (「克劳德」「GPT 会员」), telling the agent what strings are likely to match. It still adds nothing about the limit parameter, which the schema already documents.

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 (搜索) and resource (AI 订阅产品), and enumerates the return shape (SKU、价格区间、店铺数), which is more than the name alone conveys. It does not explicitly contrast with siblings like list_all_products or get_offers, so an agent must infer that keyword search vs. full listing is the dividing line.

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 ('按关键词搜索') but never states when to reach for this tool instead of list_all_products or get_offers, nor does it set exclusions. The mention of supported aliases hints at the intended caller behavior but is not framed as routing guidance.

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