Skip to main content
Glama
funinii

TrendRadar

by funinii

get_latest_news

Retrieve recent news data from multiple platforms to identify current trends and topics.

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', 'douyin'] - 不指定时:使用 config.yaml 中配置的所有平台 - 支持的平台来自 config/config.yaml 的 platforms 配置 - 每个平台都有对应的name字段(如"知乎"、"微博"),方便AI识别 limit: 返回条数限制,默认50,最大1000 注意:实际返回数量可能少于请求值,取决于当前可用的新闻总数 include_url: 是否包含URL链接,默认False(节省token)

Returns: JSON格式的新闻列表

重要:数据展示建议 本工具会返回完整的新闻列表(通常50条)给你。但请注意:

  • 工具返回:完整的50条数据 ✅

  • 建议展示:向用户展示全部数据,除非用户明确要求总结

  • 用户期望:用户可能需要完整数据,请谨慎总结

何时可以总结

  • 用户明确说"给我总结一下"或"挑重点说"

  • 数据量超过100条时,可先展示部分并询问是否查看全部

注意:如果用户询问"为什么只显示了部分",说明他们需要完整数据

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
platformsNo
limitNo
include_urlNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The implementation of the get_latest_news tool handler in DataQueryTools class.
    def get_latest_news(
        self,
        platforms: Optional[List[str]] = None,
        limit: Optional[int] = None,
        include_url: bool = False
    ) -> Dict:
        """
        获取最新一批爬取的新闻数据
    
        Args:
            platforms: 平台ID列表,如 ['zhihu', 'weibo']
            limit: 返回条数限制,默认20
            include_url: 是否包含URL链接,默认False(节省token)
    
        Returns:
            新闻列表字典
    
        Example:
            >>> tools = DataQueryTools()
            >>> result = tools.get_latest_news(platforms=['zhihu'], limit=10)
            >>> print(result['total'])
            10
        """
        try:
            # 参数验证
            platforms = validate_platforms(platforms)
            limit = validate_limit(limit, default=50)
    
            # 获取数据
            news_list = self.data_service.get_latest_news(
                platforms=platforms,
                limit=limit,
                include_url=include_url
            )
    
            return {
                "news": news_list,
                "total": len(news_list),
                "platforms": platforms,
                "success": True
            }
    
        except MCPError as e:
            return {
                "success": False,
                "error": e.to_dict()
            }
        except Exception as e:
            return {
                "success": False,
                "error": {
                    "code": "INTERNAL_ERROR",
                    "message": str(e)
                }
            }

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.3

TDQS

A4.7/5.0
Behavior4/5

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

With no annotations provided, the description carries full burden. It discloses key behaviors such as the limit cap (1000), the optional URL inclusion to save tokens, and the fact that actual returned count may be lower. It does not cover rate limits or authentication, but these are less critical for a read-only news retrieval tool.

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 long but well-structured, with a clear purpose statement followed by parameter details and usage recommendations. The additional guidance on data display is valuable for the agent, though some redundancy (e.g., repeating limit info) could be trimmed.

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 an output schema (mentioned in context), the description appropriately omits detailed return value documentation. It instead focuses on usage context, including when to summarize data, and addresses potential user expectations. This completeness helps the agent 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 0% description coverage, so the description fully compensates by explaining each parameter in detail: platforms (list of IDs, default from config), limit (default 50, max 1000), and include_url (default false, token-saving purpose). It also provides context like platform names for AI recognition.

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 retrieves the latest batch of crawled news data for quick understanding of current hotspots. It explicitly lists the parameters (platforms, limit, include_url) and describes the return format, distinguishing itself from sibling tools like search_news and get_news_by_date by focusing on the most recent data.

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

Usage Guidelines5/5

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

The description provides explicit guidance on when to show full data vs. summarize, including a dedicated section on when summarization is appropriate. It also explains the behavior when platforms are omitted (uses config defaults) and notes the actual return count may be less than requested.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.