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JoJoStar56

TrendRadar MCP Server

by JoJoStar56

search_news

Search hot news and RSS feeds across platforms using keyword, fuzzy, or entity modes. Filter by date range, platform, and sort by relevance or weight.

Instructions

统一搜索接口,支持多种搜索模式,可同时搜索热榜和RSS

建议:使用自然语言日期时,先调用 resolve_date_range 获取精确日期范围。

Args: query: 搜索关键词或内容片段 search_mode: 搜索模式 - "keyword": 精确关键词匹配(默认) - "fuzzy": 模糊内容匹配 - "entity": 实体名称搜索(人物/地点/机构) date_range: 日期范围,格式 {"start": "YYYY-MM-DD", "end": "YYYY-MM-DD"},默认今天 platforms: 平台ID列表,如 ['zhihu', 'weibo'],不指定则使用所有平台 limit: 热榜返回条数限制,默认50 sort_by: 排序方式 - "relevance"(相关度)/ "weight"(权重)/ "date"(日期) threshold: 相似度阈值(仅fuzzy模式),0-1,默认0.6 include_url: 是否包含URL链接,默认False include_rss: 是否同时搜索RSS数据,默认False rss_limit: RSS返回条数限制,默认20

Returns: JSON格式的搜索结果,包含热榜新闻列表和可选的RSS结果

Examples: - search_news(query="AI") - search_news(query="AI", include_rss=True) - search_news(query="特斯拉", date_range={"start": "2025-01-01", "end": "2025-01-07"})

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
queryYes
sort_byNorelevance
platformsNo
rss_limitNo
thresholdNo
date_rangeNo
include_rssNo
include_urlNo
search_modeNokeyword

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It explains the return format (JSON with hotlist and optional RSS) and details parameter effects like threshold for fuzzy mode. However, it does not mention read-only nature, rate limits, or response behavior on empty queries. A more explicit statement about side-effects would improve transparency.

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 sections (overview, suggestion, Args, Returns, Examples) and front-loads the purpose. Every sentence adds value, but the Args section could be slightly more concise as it partially repeats schema defaults. Overall, it is effective without being verbose.

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 complexity (10 parameters, 1 required) and the presence of an output schema, the description is complete. It explains all parameters, provides examples, and clarifies the return structure. No critical gaps are present, making it easy for an agent to invoke the tool correctly.

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 schema description coverage is 0%, so the description must compensate. It does so by providing detailed explanations for all 10 parameters, including defaults, allowed search_mode values, and the date_range format. This adds significant meaning beyond the bare schema, fully compensating for the lack of schema descriptions.

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 is a unified search interface for news, supporting multiple search modes (keyword, fuzzy, entity) and can search both hotlists and RSS simultaneously. This effectively distinguishes it from specific sibling tools like 'search_rss' or 'get_latest_news'.

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 suggestion to call 'resolve_date_range' for natural language dates and mentions default behavior for platforms. Examples show typical usage. However, it lacks explicit guidance on when NOT to use this tool versus alternatives like 'aggregate_news' or 'get_news_by_date'.

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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