hn-search

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

1. URL-encode the query
2. Fetch: https://hn.algolia.com/api/v1/search?query=YOUR_QUERY&tags=story&hitsPerPage=10
3. Parse the JSON response
4. For each hit, present: title, points, num_comments, author, date, HN link, and original URL

Finding Recent/Trending Discussions

1. Calculate Unix timestamp for your time window (e.g., 7 days ago)
2. Fetch: https://hn.algolia.com/api/v1/search?query=YOUR_QUERY&tags=story&numericFilters=points>50,created_at_i>TIMESTAMP&hitsPerPage=10
3. Sort results by points or comments for "trending"

Getting Comments on a Story

1. Get the story's objectID from a search
2. Fetch: https://hn.algolia.com/api/v1/search?tags=comment,story_STORYID&hitsPerPage=20
3. Present comment_text, author, points for each

Monitoring a Topic (Show Recent Mentions)

1. Use /search_by_date instead of /search
2. Fetch: https://hn.algolia.com/api/v1/search_by_date?query=YOUR_QUERY&tags=(story,comment)&hitsPerPage=20
3. Results are newest-first — useful for "what's new about X on HN"

Finding a User's Posts

1. Fetch: https://hn.algolia.com/api/v1/search?tags=author_USERNAME,story&hitsPerPage=20
2. 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.