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Querying Specific Items by ID
Understanding Item IDs
Every piece of content on Hacker News—whether it's a story, comment, poll, or job posting—has a unique numeric identifier called an item ID. These IDs are assigned sequentially as content is created and serve as permanent references to specific items. You'll often encounter these IDs in URLs (like news.ycombinator.com/item?id=12345678) or when browsing discussions and want to retrieve specific content programmatically.
When to Query by ID
Querying by ID is useful when you already know which specific item you want to retrieve. Common scenarios include:
- Following up on a previously discovered story or comment
- Retrieving the full details of an item you've seen referenced elsewhere
- Accessing a specific discussion thread you want to analyze
- Programmatically tracking particular submissions over time
This approach differs from browsing story feeds (like top or new stories) because you're directly accessing a known item rather than discovering what's currently trending.
How to Request Specific Items
To retrieve a specific item by its ID, simply ask your AI assistant with a natural language request that includes the ID number. For example:
- "Get me Hacker News item 38745632"
- "Show me the details for HN item 38745632"
- "Retrieve the story with ID 38745632"
The server will fetch the item and return all available information, including the title, URL, author, score, timestamp, and text content (if applicable).
Understanding Item Types
Items returned by ID can be of different types, and the data you receive will vary accordingly:
Stories include the submission title, external URL (if it's a link post), author username, points, and submission time. You'll also receive the item's direct Hacker News URL where comments can be found.
Comments contain the comment text, author, timestamp, and the ID of the parent item (which could be the story or another comment). This allows you to understand the conversation hierarchy.
Polls include the poll question and references to the available answer options, each of which has its own item ID.
Exploring Related Content
Once you've retrieved an item, you can explore related content by using the IDs referenced within it. Stories include an array of comment IDs that you can fetch individually to read the discussion. Comments reference their parent items, allowing you to traverse conversation threads upward or downward. This makes it possible to reconstruct entire discussion trees or focus on specific conversation branches that interest you.
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Metorial provides developers with instant access to 600+ MCP servers for building AI agents that can interact with real-world tools and services. Built on MCP, Metorial simplifies agent tool integration by offering pre-configured connections to popular platforms like Google Drive, Slack, GitHub, Notion, and hundreds of other APIs. Our platform supports all major AI agent frameworks—including LangChain, AutoGen, CrewAI, and LangGraph—enabling developers to add tool calling capabilities to their agents in just a few lines of code. By eliminating the need for custom integration code, Metorial helps AI developers move from prototype to production faster while maintaining security and reliability. Whether you're building autonomous research agents, customer service bots, or workflow automation tools, Metorial's MCP server library provides the integrations you need to connect your agents to the real world.