What the Numbers Tell You

Every story on Hacker News comes with metadata that reveals its journey through the community. When you retrieve story data through the MCP server, you'll encounter several key pieces of information that help you understand a story's reception and relevance.

Scores represent the net votes a story has received from the community. A higher score indicates stronger community interest and approval. Stories with scores in the hundreds have resonated significantly, while those in the thousands represent major discussions that have captured widespread attention. The score helps you quickly identify which submissions the community values most.

Timestamps appear in Unix epoch format, indicating when a story was submitted. This timing information is crucial for understanding context—a story with 200 points posted an hour ago is trending rapidly, while the same score on a week-old story suggests modest interest. By combining timestamp and score data, you can gauge velocity and identify breaking discussions versus evergreen content.

Understanding Item IDs

Each story, comment, and user interaction on Hacker News has a unique numerical identifier. When you request story data, these IDs serve as references for drilling deeper. A story's ID allows you to retrieve its comment thread, while comment IDs enable you to navigate discussion trees. These identifiers are permanent and unchanging, making them reliable references for tracking specific content over time.

Author and Source Information

Story metadata includes the submitting user's username and, for link posts, the destination URL. The username connects you to the submitter's profile, where you can explore their history and reputation through karma scores. The URL reveals the source publication or domain, helping you assess content origin and credibility at a glance.

Story Types and Categorization

Stories on Hacker News come in different types: standard submissions with URLs, "Ask HN" posts that pose questions to the community, "Show HN" posts sharing projects or creations, and job postings. The metadata identifies these types, allowing you to filter and focus on content categories relevant to your needs. Understanding these distinctions helps you interpret scores and engagement appropriately—an "Ask HN" post naturally generates more comments than a typical article submission.

Practical Application

When analyzing story data, combine these metadata elements for deeper insight. A recent story with a rapidly climbing score and active comment thread indicates an emerging hot topic. An older story with sustained high scores represents validated, quality content. By understanding what each data point represents, you can effectively gauge community sentiment, track trends, and identify valuable discussions worth your attention.