CN HotRank

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

With no annotations, the description carries the full burden and does disclose real behavioral traits: a 10-minute cache and the licensing restriction. It omits rate-limit/robots behavior (only hinted at in the schema's force param) and error/failure behavior, so it is strong but not exhaustive.

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?

Purpose and returned fields are front-loaded in one dense sentence, with the legal/caching caveats following. It is efficient, though listing both platforms and output fields within an already long sentence is mildly redundant.

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, the description helpfully enumerates the return shape (排名、标题、热度值、标签、链接), and covers caching and licensing. Remaining gaps (pagination across platforms when all is selected, error behavior) are minor for a read-only aggregator.

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 the schema already documents count, force, and platform. The description names the platforms but adds no extra semantics for count/force (e.g. cache-bypass tradeoffs), 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 (获取中文平台实时热榜) and enumerates the four sources (今日头条/抖音/微博/百度热搜) plus the returned fields. It is highly specific, but never names or contrasts with the sibling list_platforms, so sibling differentiation is left implicit.

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 rather than stated: the description makes clear this fetches public hot lists and even imposes license terms (AI 输入 only, no training/resale), but it gives no when-to-use vs when-to-prefer-list_platforms guidance and names no prerequisites for calling.

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 disclosure burden, and it does disclose a genuinely non-obvious trait: that the list includes platforms temporarily suspended because robots.txt fully bans crawling, along with the path to reinstatement. This tells the agent that presence in the list does not imply availability. It does not state whether the call requires auth or whether results are static.

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 the primary payload (platforms) first and qualifications in parentheses. Dense but every clause adds information; no padding.

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 zero-parameter read tool with no output schema, the description covers what the result contains (platforms, data caliber, compliance status and suspension reasons), which is what an agent needs to decide to call it. It stops short of describing result shape or ordering, but that is a minor gap at this complexity.

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 there is nothing for the description to disambiguate; the baseline is 4. No parameter claims are made that could mislead.

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 (热榜平台/hot-rank platforms), plus the extra payload the response carries (数据口径, 合规状态). It is clearly distinguishable from the sibling get_hot_rank (retrieval of ranking data vs. enumeration of supported platforms), though it 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?

Usage is implied by the verb: call this to discover which platforms exist before calling get_hot_rank. However, there is no explicit statement of when to use it versus the sibling, no prerequisites, and no note that it is a discovery/static-info call rather than a data fetch. Adequate but leaves routing to inference.

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