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iPythoning
by iPythoning

get_latest_news

Fetch recent news headlines from selected platforms with configurable limits and optional URLs. Use to monitor trending topics and stay informed.

Instructions

获取最新一批爬取的新闻数据,快速了解当前热点

Args: platforms: 平台ID列表,如 ['zhihu', 'weibo'],不指定则使用所有平台 limit: 返回条数限制,默认50,最大1000 include_url: 是否包含URL链接,默认False(节省token)

Returns: JSON格式的新闻列表

数据展示建议

  • 默认展示全部返回数据,除非用户明确要求总结

  • 用户说"总结"或"挑重点"时才进行筛选

  • 用户问"为什么只显示部分"说明需要完整数据

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
platformsNo
include_urlNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv6.10.0

TDQS

A3.8/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral burden. It discloses return format, default limit, platform handling, include_url token-saving behavior, and explicit display expectations for the agent. It does not cover error behavior or data freshness guarantees, but the read-only nature is clear from the get verb.

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 Args, Returns, and display guidance sections. The main purpose is front-loaded, and each section earns its place, though the display-suggestion block is slightly beyond tool invocation semantics.

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?

For a simple read tool with three optional parameters and an output schema, the description is largely complete: it covers parameter behavior, defaults, return format, and agent-facing display policy. It could still mention ordering of results or what news fields are returned, but the output schema likely covers those.

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?

Schema description coverage is 0%, and the description fully compensates by explaining all three parameters: platforms, limit with default and max, and include_url with its token-saving rationale. This is exactly the semantic content the schema lacks.

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?

The description states a clear verb and resource: it fetches the latest batch of crawled news data to quickly understand current hotspots. It is distinguishable from siblings like get_latest_rss and get_news_by_date, though it does not explicitly name them.

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

Usage Guidelines2/5

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

No guidance is given about when to use this tool versus alternatives such as search_news, get_news_by_date, or get_trending_topics. The only usage-related context is the display suggestion block, which addresses how to present results rather than when to select this tool.

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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