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get_hot_rank

获取中文平台实时热榜(今日头条/抖音/微博/百度热搜),返回结构化热点:排名、标题、热度值、标签、链接。数据为各平台公开热榜,10 分钟缓存;仅用于 AI 输入(ai-input),禁止用于模型训练或转售原始数据。

Input Schema

NameRequiredDescriptionDefault
countNo每平台返回条数(1-50,默认 20)
forceNo跳过缓存强制刷新(慎用:遵守 robots 频率纪律)
platformNo目标平台;all = 全部四个平台all

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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.

Conciseness4/5

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.

Completeness4/5

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.

Parameters3/5

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.

Purpose4/5

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.

Usage Guidelines3/5

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.

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