The Hacker News MCP Server is a valuable tool within the MCP ecosystem, functioning as an MCP Server that empowers AI models with real-time access to Hacker News data. It offers a suite of tools, including get_stories for fetching top, new, or specific types of stories, get_story_info to retrieve comments, search_stories for query-based searches, and get_user_info for user profile details.

This server seamlessly integrates with AI models, allowing them to understand current tech trends, gather public sentiment on specific topics, and research user activity. By providing structured access to Hacker News, it eliminates the need for complex web scraping and data parsing, saving developers time and effort. The core value lies in enabling AI to engage in informed conversations, generate insightful content, and conduct timely research, all driven by the pulse of the Hacker News community. Implemented in Python, it can be easily integrated into existing MCP setups via standard configuration updates.

Fetch Top Hacker News Stories

The get_stories tool is a core feature of mcp-hn, enabling AI models to retrieve the latest and most discussed content from Hacker News. It allows specifying categories like "top," "new," "ask_hn," and "show_hn," providing a filtered view of the Hacker News front page. This functionality is crucial for AI models that need to stay updated on current trends, tech news, and community discussions. The tool returns a list of stories, each containing relevant information such as title, URL, and score, which can then be processed by the AI model for further analysis or summarization.

For example, an AI-powered news aggregator could use get_stories to fetch the "top" stories and then summarize them for users, providing a concise overview of the day's most important tech news. This allows the AI to provide timely and relevant information, enhancing its utility and user engagement. The underlying implementation likely involves making HTTP requests to the Hacker News API and parsing the JSON response to extract the story data.

Retrieve Story-Specific Comments

The get_story_info tool allows AI models to delve deeper into specific Hacker News stories by fetching the associated comments. This is invaluable for understanding community sentiment, identifying key arguments, and extracting valuable insights from user discussions. The tool takes a story ID as input and returns a structured representation of the comments, including the comment text, author, and timestamps. This enables AI models to perform sentiment analysis, topic extraction, and summarization of the comments, providing a comprehensive understanding of the community's perspective on a particular topic.

Imagine an AI model designed to assist researchers in understanding public opinion on emerging technologies. By using get_story_info to retrieve comments on Hacker News stories about AI, the model can identify common concerns, arguments for and against the technology, and potential ethical implications. This information can then be used to inform policy decisions and guide future research. The implementation likely involves using the Hacker News API to retrieve the comment data and parsing the HTML or JSON response.

Search Hacker News Archives

The search_stories tool empowers AI models to explore the vast archive of Hacker News stories by performing keyword-based searches. This is particularly useful for researching specific topics, tracking trends over time, and identifying relevant discussions within the Hacker News community. The tool takes a search query as input and returns a list of stories that match the query, along with relevant metadata such as title, URL, and score. This allows AI models to quickly identify relevant information and extract valuable insights from the Hacker News archives.

Consider an AI model designed to assist investors in identifying promising startups. By using search_stories to search for discussions about specific companies or technologies, the model can gauge public sentiment, identify potential risks and opportunities, and gain a deeper understanding of the competitive landscape. This information can then be used to inform investment decisions and improve the accuracy of financial forecasts. The implementation likely involves using the Hacker News API or an alternative search API to perform the search and retrieve the results.

Fetch User Information

The get_user_info tool allows AI models to retrieve information about specific Hacker News users. This can be useful for understanding user activity, identifying influential members of the community, and personalizing content recommendations. The tool takes a username as input and returns a profile containing information such as the user's karma, creation date, and recent activity. This enables AI models to gain a deeper understanding of user behavior and tailor their responses accordingly.

For example, an AI-powered community moderator could use get_user_info to identify users with a history of posting inappropriate content and take appropriate action. Alternatively, an AI-powered recommendation engine could use this tool to identify users with similar interests and recommend relevant content based on their past activity. The implementation likely involves using the Hacker News API to retrieve the user profile data.