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JoJoStar56

TrendRadar MCP Server

by JoJoStar56

read_article

Read article content from a URL and convert it to clean Markdown by removing ads and navigation. Ideal for analyzing news articles and extracting key details.

Instructions

读取指定 URL 的文章内容,返回 LLM 友好的 Markdown 格式

通过 Jina AI Reader 将网页转换为干净的 Markdown,自动去除广告、导航栏等噪音内容。 适合用于:阅读新闻正文、获取文章详情、分析文章内容。

典型使用流程:

  1. 先用 search_news(include_url=True) 搜索新闻获取链接

  2. 再用 read_article(url=链接) 读取正文内容

  3. AI 对 Markdown 正文进行分析、摘要、翻译等

Args: url: 文章链接(必需),以 http:// 或 https:// 开头 timeout: 请求超时时间(秒),默认 30,最大 60

Returns: JSON格式的文章内容,包含完整 Markdown 正文

Examples: - read_article(url="https://example.com/news/123")

Note: - 使用 Jina AI Reader 免费服务(100 RPM 限制) - 每次请求间隔 5 秒(内置速率控制) - 部分付费墙/登录墙页面可能无法完整获取

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes
timeoutNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description carries full burden; it discloses use of Jina AI Reader, rate limits (100 RPM), built-in rate control, and potential issues with paywalled pages, making behavior transparent.

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?

The description is well-structured with clear sections (purpose, usage flow, args, returns, examples, notes) and is front-loaded, though slightly verbose with redundant bullet points.

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?

Given the presence of an output schema and only 2 parameters, the description covers usage flow, limitations, and output format sufficiently for an agent to use the tool effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 2 parameters with 0% description coverage, but the description adds crucial meaning: url must start with http/https, timeout defaults to 30 and max 60, thus fully compensating for the schema gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool reads article content from a specified URL and returns LLM-friendly Markdown, distinguishing it from siblings like search_news (for finding URLs) and read_articles_batch (for batch reading).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides a typical usage flow (search_news then read_article), lists suitable use cases, and notes limitations (paywalls), but does not explicitly state when not to use or list all alternatives.

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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