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Understanding Story Data: Scores, Timestamps, and Metadata
What the Numbers Mean
When you retrieve stories from Hacker News through the MCP server, each story comes with a score that reflects community engagement. This score represents the number of upvotes the story has received from users. Higher scores typically indicate content that the community finds valuable, interesting, or discussion-worthy. A story with a score of 500+ is considered highly popular, while scores in the thousands represent the most impactful submissions.
Scores are dynamic—they change as more users vote. When monitoring trending topics, you'll notice scores increasing rapidly for breaking news or controversial discussions, while older stories typically see their scores stabilize.
Understanding Timestamps
Every story and comment includes timestamp data that tells you when it was submitted to Hacker News. These timestamps are crucial for understanding content freshness and tracking how quickly stories gain traction.
Timestamps appear in Unix epoch format (seconds since January 1, 1970), but your AI assistant will typically convert these into human-readable dates and times. This temporal data helps you:
- Identify breaking news versus older discussions
- Calculate how quickly stories gained popularity
- Track when specific users were most active
- Filter content by time periods relevant to your research
Metadata Fields Explained
Beyond scores and timestamps, story data includes several metadata fields that provide context:
Author Information: The username of the person who submitted the story. This lets you track contributions from specific community members or identify prolific submitters.
Story URL: The external link to the actual content being discussed. Some submissions are "Ask HN" or "Show HN" posts without external URLs—these are self-contained discussions.
Item ID: Every story, comment, and poll has a unique identifier. You'll use these IDs when requesting specific items or navigating comment threads.
Descendants Count: Shows how many comments exist in the entire discussion tree, giving you a quick sense of how much conversation a story has generated.
Type Field: Indicates whether you're looking at a story, comment, job posting, or poll—each has different associated data.
Practical Applications
Understanding this data structure enables sophisticated queries. You can ask your AI assistant to "find stories from the past week with scores above 300" or "show me all submissions by a specific user in the last month." The metadata provides the filtering criteria, while scores and timestamps help you assess relevance and timeliness.
This structured approach to Hacker News data transforms casual browsing into actionable intelligence about technology trends and community sentiment.
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