The server-google-news is a valuable MCP server that empowers AI models with real-time Google News search capabilities. By integrating with SerpAPI, it provides a seamless way to access and categorize news data across multiple languages and regions. This server offers flexible search options, including query-based searches, topic exploration, and publication filtering, enabling AI models to stay informed and provide contextually relevant responses.

The solution addresses the challenge of keeping AI models up-to-date with current events. It automatically categorizes news results into topics like AI & Technology, Business, and Healthcare, allowing for efficient information retrieval. Developers benefit from robust error handling and easy installation via Smithery or mcp-get. By providing a direct connection to Google News, this MCP server enhances the accuracy and relevance of AI-driven applications, making it an essential tool for developers seeking to integrate timely information into their models. It supports standard input/output and HTTP/SSE for versatile integration.

Flexible News Search via SerpAPI

The server-google-news MCP server provides comprehensive search capabilities for Google News through its integration with SerpAPI. This allows AI models to access a wide range of news content based on specific queries, topics, publications, and even story coverage. The server translates the AI model's request into a SerpAPI query, retrieves the results, and structures them for easy consumption by the AI. This includes handling various result types such as headlines, stories, related topics, and menu links, ensuring the AI model receives a complete picture of the news landscape.

For example, an AI model could use this feature to research the public perception of a new product launch. By querying Google News for articles mentioning the product, the AI can analyze sentiment, identify key concerns, and track media coverage over time. The flexibility of the search options allows for fine-grained control over the data retrieved, ensuring the AI model receives the most relevant information. Technically, this is achieved by mapping the MCP request parameters (like q, topic_token, publication_token) to the corresponding SerpAPI parameters.

Global, Configurable News Coverage

This server supports multiple languages and regions through configurable language (hl) and country (gl) codes. This global coverage allows AI models to access news from different parts of the world, providing a more comprehensive and nuanced understanding of events. The server handles the complexities of regional news variations, ensuring that the AI model receives accurate and relevant information regardless of its geographical focus. If an unsupported language code is provided, the server automatically falls back to English, notifying the user of the change.

Imagine an AI model designed to analyze the impact of climate change on different regions. By using the gl and hl parameters, the AI can gather news reports from specific countries and in local languages, gaining insights into regional challenges and perspectives that might be missed by only analyzing English-language sources. This feature leverages SerpAPI's internationalization capabilities, allowing the MCP server to act as a localized news aggregator for AI models.

Automated News Categorization

The server-google-news automatically categorizes news results into predefined topics such as AI & Technology, Business, Science & Research, and Healthcare. This smart categorization feature saves AI models the effort of manually classifying news articles, allowing them to focus on higher-level analysis and decision-making. The categorization is performed on the server side, ensuring that the AI model receives structured and organized data.

Consider an AI-powered investment platform that needs to stay informed about the latest developments in various sectors. By leveraging the automated categorization feature, the platform can quickly identify news articles relevant to specific industries, such as technology or healthcare, and use this information to make informed investment decisions. The categorization logic is implemented within the server, likely using keyword analysis or machine learning techniques to assign articles to the appropriate categories.

Robust Error Handling and Validation

The server implements comprehensive error handling for API failures and invalid inputs, providing helpful error messages to the user. This robustness ensures that the AI model receives reliable data and can gracefully handle unexpected issues. The server validates request parameters, such as language and country codes, and provides informative error messages if these parameters are invalid. It also handles potential API failures from SerpAPI, retrying requests or returning appropriate error codes to the client.

For example, if the SerpAPI key is invalid or the API quota has been exceeded, the server will return a specific error message indicating the problem. This allows the AI model to take appropriate action, such as prompting the user to update their API key or reducing the frequency of requests. This feature is crucial for ensuring the stability and reliability of the MCP server in real-world deployments.

Integration via Smithery and mcp-get

The server-google-news offers multiple installation methods, including Smithery and mcp-get, simplifying the integration process for users. Smithery provides automated installation specifically for Claude Desktop, while mcp-get offers a more general-purpose installation solution. These tools streamline the setup process, reducing the manual configuration required and making it easier for developers to deploy the server.

For developers using Claude Desktop, Smithery provides a one-click installation experience, automatically configuring the necessary settings and dependencies. For other environments, mcp-get offers a command-line interface for installing the server and managing its dependencies. These integration options make the server-google-news accessible to a wider range of users, regardless of their technical expertise.