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rui497

TrendRadar MCP Server

by rui497

read_articles_batch

Reads up to 5 article URLs in one batch, spacing requests to respect rate limits, and returns full content for comparative analysis and report generation.

Instructions

批量读取多篇文章内容(最多 5 篇,间隔 5 秒)

逐篇请求文章内容,每篇之间自动间隔 5 秒以遵守速率限制。

典型使用流程:

  1. 先用 search_news(include_url=True) 搜索新闻获取多个链接

  2. 再用 read_articles_batch(urls=[...]) 批量读取正文

  3. AI 对多篇文章进行对比分析、综合报告

Args: urls: 文章链接列表(必需),最多处理 5 篇 timeout: 每篇的请求超时时间(秒),默认 30

Returns: JSON格式的批量读取结果,包含每篇的完整内容和状态

Examples: - read_articles_batch(urls=["https://a.com/1", "https://b.com/2"])

Note: - 单次最多读取 5 篇,超出部分会被跳过 - 5 篇约需 25-30 秒(每篇间隔 5 秒) - 单篇失败不影响其他篇的读取

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlsYes
timeoutNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

With no annotations provided, the description fully carries the transparency burden. It discloses key behaviors: automatic 5-second delay for rate limiting, max 5 articles, failure isolation (single failure doesn't affect others), and expected time duration. It also mentions the return format, making the tool's behavior predictable.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with sections (overview, typical flow, args, returns, example, notes), front-loads the critical constraints (max 5, interval), and every sentence provides useful information without redundancy. It is appropriately sized for the tool's complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has only 2 parameters and no annotations, the description is complete: it covers the usage workflow, parameter meanings, return format, a concrete example, and operational notes. The presence of an output schema reduces the need for detailed return explanation, and the description still covers it sufficiently.

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 no descriptions for parameters, but the description explains urls (required, max 5) and timeout (default 30, per-request timeout). This adds crucial semantics beyond the schema, enabling correct parameter usage.

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 it reads multiple articles in batch, with a max of 5 and a 5-second interval. It uses a specific verb and resource ('batch read articles'), distinguishing it from the sibling tool read_article which handles single articles.

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?

It provides a typical usage flow (search_news first, then batch read, then analysis) which gives clear context for when to use this tool. However, it doesn't explicitly state when not to use it (e.g., for a single article, read_article might be preferable), so it lacks explicit exclusions.

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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