name: hn-search description: Search and monitor Hacker News stories, comments, and users via the free Algolia API. Use when the user asks about HN discussions, wants to find posts about a topic, or monitor HN for mentions. No API key required.
Hacker News Search
Search and monitor Hacker News stories, comments, and users via the Algolia HN Search API. No API key required.
When to Use
- User asks about Hacker News discussions on a topic
- User wants to find HN posts about a company, product, or technology
- User wants to monitor HN for mentions of something
- User asks "what's trending on HN" or "what did HN think about X"
- User wants to find Show HN / Ask HN / Launch HN posts
API Overview
Base URL: https://hn.algolia.com/api/v1
Two search endpoints:
/search— Relevance-sorted (best for finding specific topics)/search_by_date— Date-sorted (best for monitoring / recent activity)
Rate Limits: 10,000 requests/hour (generous, no auth needed)
How to Search
Step 1: Build the URL
https://hn.algolia.com/api/v1/search?query=QUERY&tags=TAG&hitsPerPage=N&numericFilters=FILTERS
Step 2: Fetch with web_fetch
Use web_fetch to call the API. Response is JSON.
Parameters
| Parameter | Description | Example |
|---|---|---|
query |
Search terms (URL-encoded) | query=openai+gpt |
tags |
Filter by type (see below) | tags=story |
hitsPerPage |
Results per page (max 1000) | hitsPerPage=20 |
page |
Page number (0-indexed) | page=0 |
numericFilters |
Numeric filters (see below) | numericFilters=points>100 |
Tag Filters
Use tags to filter by content type:
| Tag | Description |
|---|---|
story |
Stories only |
comment |
Comments only |
show_hn |
Show HN posts |
ask_hn |
Ask HN posts |
front_page |
Currently on front page |
author_USERNAME |
Posts by a specific user |
story_ID |
Comments on a specific story |
Combine tags with commas (AND) or parentheses with commas (OR):
tags=story,show_hn→ Show HN stories (AND)tags=(story,comment)→ Stories OR comments
Numeric Filters
| Filter | Description |
|---|---|
points>N |
Minimum points/upvotes |
num_comments>N |
Minimum comments |
created_at_i>TIMESTAMP |
After Unix timestamp |
created_at_i<TIMESTAMP |
Before Unix timestamp |
Combine with commas: numericFilters=points>100,num_comments>50
Date Ranges
To search within a time window, use Unix timestamps with created_at_i:
Calculate the current Unix timestamp first (e.g., via exec: date +%s), then subtract:
| Window | Subtract from now |
|---|---|
| Last 24 hours | - 86400 |
| Last 7 days | - 604800 |
| Last 30 days | - 2592000 |
Example: if now is 1705312200, last 7 days = numericFilters=created_at_i>1704707400
Response Format
Each hit contains:
{
"objectID": "12345",
"title": "Story Title",
"url": "https://example.com/article",
"author": "username",
"points": 150,
"num_comments": 42,
"created_at": "2024-01-15T10:30:00Z",
"created_at_i": 1705312200,
"story_text": "Text for Ask HN / Show HN (HTML)",
"_tags": ["story", "author_username", "story_12345"]
}
For comments, hits also include:
{
"comment_text": "The comment body (HTML)",
"story_id": 12345,
"story_title": "Parent Story Title",
"story_url": "https://example.com",
"parent_id": 12344
}
The response wrapper includes:
{
"hits": [...],
"nbHits": 1000,
"page": 0,
"nbPages": 50,
"hitsPerPage": 20
}
Constructing HN Links
- Story:
https://news.ycombinator.com/item?id={objectID} - Comment:
https://news.ycombinator.com/item?id={objectID} - User:
https://news.ycombinator.com/user?id={author}
Step-by-Step Instructions
Searching for Stories on a Topic
- URL-encode the query
- Fetch:
https://hn.algolia.com/api/v1/search?query=YOUR_QUERY&tags=story&hitsPerPage=10 - Parse the JSON response
- For each hit, present: title, points, num_comments, author, date, HN link, and original URL
Finding Recent/Trending Discussions
- Calculate Unix timestamp for your time window (e.g., 7 days ago)
- Fetch:
https://hn.algolia.com/api/v1/search?query=YOUR_QUERY&tags=story&numericFilters=points>50,created_at_i>TIMESTAMP&hitsPerPage=10 - Sort results by points or comments for "trending"
Getting Comments on a Story
- Get the story's objectID from a search
- Fetch:
https://hn.algolia.com/api/v1/search?tags=comment,story_STORYID&hitsPerPage=20 - Present comment_text, author, points for each
Monitoring a Topic (Show Recent Mentions)
- Use
/search_by_dateinstead of/search - Fetch:
https://hn.algolia.com/api/v1/search_by_date?query=YOUR_QUERY&tags=(story,comment)&hitsPerPage=20 - Results are newest-first — useful for "what's new about X on HN"
Finding a User's Posts
- Fetch:
https://hn.algolia.com/api/v1/search?tags=author_USERNAME,story&hitsPerPage=20 - For their comments:
tags=author_USERNAME,comment
Output Format
Present results as a clean list:
### HN Results for "query" (N total)
1. **Story Title** (150 pts, 42 comments)
By username · Jan 15, 2024
🔗 https://example.com/article
💬 https://news.ycombinator.com/item?id=12345
2. ...
For comments:
### HN Comments on "Story Title"
1. **username** (12 pts) · Jan 15, 2024
> First ~200 chars of the comment text...
💬 https://news.ycombinator.com/item?id=12345
Error Handling
- Empty results: Tell the user no results were found. Suggest broadening the query or removing filters.
- API error / timeout: Retry once. If still failing, inform the user the HN search API may be temporarily down.
- Rate limited (429): Unlikely at 10k/hr, but if hit, wait 60 seconds and retry.
- Malformed response: Check the URL construction — common issues are unencoded special characters in the query.
Examples
Example 1: "What's HN saying about Rust?"
Fetch: https://hn.algolia.com/api/v1/search?query=rust+programming&tags=story&hitsPerPage=5&numericFilters=points>50
Example 2: "Find Show HN posts about AI agents from the last month"
# Calculate timestamp for 30 days ago, then:
Fetch: https://hn.algolia.com/api/v1/search_by_date?query=ai+agents&tags=show_hn&numericFilters=created_at_i>TIMESTAMP&hitsPerPage=10
Example 3: "What has pg posted recently?"
Fetch: https://hn.algolia.com/api/v1/search_by_date?tags=author_pg&hitsPerPage=10
Data Source
Algolia HN Search API — Free, no authentication required. Indexes all public Hacker News content in near real-time.