get_news_by_topic
Search and retrieve Google News articles by specific topics such as WORLD, TECHNOLOGY, or SPORTS. Customize results by period, max results, and generate summaries for concise insights.
Instructions
Find articles by topic using Google News. topic is one of WORLD, NATION, BUSINESS, TECHNOLOGY, ENTERTAINMENT, SPORTS, SCIENCE, HEALTH, POLITICS, CELEBRITIES, TV, MUSIC, MOVIES, THEATER, SOCCER, CYCLING, MOTOR SPORTS, TENNIS, COMBAT SPORTS, BASKETBALL, BASEBALL, FOOTBALL, SPORTS BETTING, WATER SPORTS, HOCKEY, GOLF, CRICKET, RUGBY, ECONOMY, PERSONAL FINANCE, FINANCE, DIGITAL CURRENCIES, MOBILE, ENERGY, GAMING, INTERNET SECURITY, GADGETS, VIRTUAL REALITY, ROBOTICS, NUTRITION, PUBLIC HEALTH, MENTAL HEALTH, MEDICINE, SPACE, WILDLIFE, ENVIRONMENT, NEUROSCIENCE, PHYSICS, GEOLOGY, PALEONTOLOGY, SOCIAL SCIENCES, EDUCATION, JOBS, ONLINE EDUCATION, HIGHER EDUCATION, VEHICLES, ARTS-DESIGN, BEAUTY, FOOD, TRAVEL, SHOPPING, HOME, OUTDOORS, FASHION.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| full_data | No | Return full data for each article. If False a summary should be created by setting the summarize flag | |
| max_results | No | Maximum number of results to return. | |
| period | No | Number of days to look back for articles. | |
| summarize | No | Generate a summary of the article, will first try LLM Sampling but if unavailable will use nlp | |
| topic | Yes | Topic to search for articles. |
Implementation Reference
- The primary MCP handler for the 'get_news_by_topic' tool. It defines input parameters with validation, fetches articles from the news module, optionally summarizes them using LLM or NLP, reports progress, and returns formatted ArticleOut objects.@mcp.tool(description=news.get_news_by_topic.__doc__, tags={"news", "articles", "topic"}) async def get_news_by_topic( ctx: Context, topic: Annotated[str, Field(description="Topic to search for articles.")], period: Annotated[int, Field(description="Number of days to look back for articles.", ge=1)] = 7, max_results: Annotated[int, Field(description="Maximum number of results to return.", ge=1)] = 10, full_data: Annotated[ bool, Field( description="Return full data for each article. If False a summary should be created by setting the summarize flag" ), ] = False, summarize: Annotated[ bool, Field( description="Generate a summary of the article, will first try LLM Sampling but if unavailable will use nlp" ), ] = True, ) -> list[ArticleOut]: set_newspaper_article_fields(full_data) articles = await news.get_news_by_topic( topic=topic, period=period, max_results=max_results, nlp=False, report_progress=ctx.report_progress, ) if summarize: await summarize_articles(articles, ctx) await ctx.report_progress(progress=len(articles), total=len(articles)) return [ArticleOut(**a.to_json(False)) for a in articles]
- Pydantic model defining the output schema for news articles returned by the tool.class ArticleOut(BaseModelClean): title: Annotated[str, Field(description="Title of the article.")] url: Annotated[str, Field(description="Original article URL.")] read_more_link: Annotated[Optional[str], Field(description="Link to read more about the article.")] = None language: Annotated[Optional[str], Field(description="Language code of the article.")] = None meta_img: Annotated[Optional[str], Field(description="Meta image URL.")] = None movies: Annotated[Optional[list[str]], Field(description="List of movie URLs or IDs.")] = None meta_favicon: Annotated[Optional[str], Field(description="Favicon URL from meta data.")] = None meta_site_name: Annotated[Optional[str], Field(description="Site name from meta data.")] = None authors: Annotated[Optional[list[str]], Field(description="list of authors.")] = None publish_date: Annotated[Optional[str], Field(description="Publish date in ISO format.")] = None top_image: Annotated[Optional[str], Field(description="URL of the top image.")] = None images: Annotated[Optional[list[str]], Field(description="list of image URLs.")] = None text: Annotated[Optional[str], Field(description="Full text of the article.")] = None summary: Annotated[Optional[str], Field(description="Summary of the article.")] = None keywords: Annotated[Optional[list[str]], Field(description="Extracted keywords.")] = None tags: Annotated[Optional[list[str]], Field(description="Tags for the article.")] = None meta_keywords: Annotated[Optional[list[str]], Field(description="Meta keywords from the article.")] = None meta_description: Annotated[Optional[str], Field(description="Meta description from the article.")] = None canonical_link: Annotated[Optional[str], Field(description="Canonical link for the article.")] = None meta_data: Annotated[Optional[dict[str, str | int]], Field(description="Meta data dictionary.")] = None meta_lang: Annotated[Optional[str], Field(description="Language of the article.")] = None source_url: Annotated[Optional[str], Field(description="Source URL if different from original.")] = None
- Core helper function that performs the actual news search by topic using the GNews library, fetches articles, and processes them via process_gnews_articles.async def get_news_by_topic( topic: str, period=7, max_results: int = 10, nlp: bool = True, report_progress: Optional[ProgressCallback] = None, ) -> list[newspaper.Article]: """Find articles by topic using Google News. topic is one of WORLD, NATION, BUSINESS, TECHNOLOGY, ENTERTAINMENT, SPORTS, SCIENCE, HEALTH, POLITICS, CELEBRITIES, TV, MUSIC, MOVIES, THEATER, SOCCER, CYCLING, MOTOR SPORTS, TENNIS, COMBAT SPORTS, BASKETBALL, BASEBALL, FOOTBALL, SPORTS BETTING, WATER SPORTS, HOCKEY, GOLF, CRICKET, RUGBY, ECONOMY, PERSONAL FINANCE, FINANCE, DIGITAL CURRENCIES, MOBILE, ENERGY, GAMING, INTERNET SECURITY, GADGETS, VIRTUAL REALITY, ROBOTICS, NUTRITION, PUBLIC HEALTH, MENTAL HEALTH, MEDICINE, SPACE, WILDLIFE, ENVIRONMENT, NEUROSCIENCE, PHYSICS, GEOLOGY, PALEONTOLOGY, SOCIAL SCIENCES, EDUCATION, JOBS, ONLINE EDUCATION, HIGHER EDUCATION, VEHICLES, ARTS-DESIGN, BEAUTY, FOOD, TRAVEL, SHOPPING, HOME, OUTDOORS, FASHION. """ google_news.period = f"{period}d" google_news.max_results = max_results gnews_articles = google_news.get_news_by_topic(topic) if not gnews_articles: logger.debug(f"No articles found for topic '{topic}' in the last {period} days.") return [] return await process_gnews_articles(gnews_articles, nlp=nlp, report_progress=report_progress)
- Supporting helper that downloads, parses, and optionally applies NLP to articles from GNews results.async def process_gnews_articles( gnews_articles: list[dict], nlp: bool = True, report_progress: Optional[ProgressCallback] = None, ) -> list[newspaper.Article]: """ Process a list of Google News articles and download them (async). Optionally report progress via report_progress callback. """ articles = [] total = len(gnews_articles) for idx, gnews_article in enumerate(gnews_articles): article = await download_article(gnews_article["url"]) if article is None or not article.text: logger.debug(f"Failed to download article from {gnews_article['url']}:\n{article}") continue article.parse() if nlp: article.nlp() articles.append(article) if report_progress: await report_progress(idx, total) return articles
- src/google_news_trends_mcp/server.py:233-233 (registration)MCP tool registration decorator linking the handler to the tool name 'get_news_by_topic'.@mcp.tool(description=news.get_news_by_topic.__doc__, tags={"news", "articles", "topic"})