MCP Trend Detector -- Scan Emerging Tech Trends from 5 Signals: HN, arXiv, npm, Stack Overflow & Google News

Is the technology you are betting on actually growing -- or are you chasing yesterday's hype? Trend reports are published quarterly, but markets move daily. MCP Trend Detector scans 5 independent data sources in real time and gives you a 0-100 trend score with a clear level: HOT, GROWING, EMERGING, or NICHE. Know where the market is heading before your competitors do.

What It Does

Enter any technology topic, framework, or market category. The detector queries Hacker News discussions, arXiv research papers, npm package ecosystem, Stack Overflow questions, and Google News articles. It aggregates the signals into a composite trend score and returns detailed per-source metrics so you can see exactly where the signal is strongest.

Features

• :white_check_mark: 5-Signal Trend Analysis -- HN discussions, arXiv papers, npm packages, Stack Overflow questions, Google News articles
• :white_check_mark: Composite Trend Score 0-100 -- weighted across all 5 sources with clear level classification
• :white_check_mark: 4 Trend Levels -- HOT (80+), GROWING (50-80), EMERGING (25-50), NICHE (0-25)
• :white_check_mark: Per-Source Metrics -- see exact counts for each signal: total HN stories, arXiv paper count, npm package count, news article count
• :white_check_mark: Top Results -- surfaces the top HN story (with points) and top npm package for context
• :white_check_mark: Latest News Date -- shows when the most recent Google News article was published to assess recency
• :white_check_mark: MCP-Native -- designed for AI agents tracking market shifts and tech adoption autonomously
• :white_check_mark: No API Keys Required -- all 5 sources use public endpoints
• :white_check_mark: Instant Results -- all signals checked in seconds, not hours

Input Parameters

Parameter	Type	Required	Description	Example
topic	string	Yes	Technology, framework, or market category to analyze	"MCP Model Context Protocol"

Output Data Example

{
  "topic": "MCP Model Context Protocol",
  "hackerNews": {
    "totalStories": 342,
    "topStory": "Anthropic's MCP Protocol is Changing How AI Agents Access Data",
    "topPoints": 891
  },
  "arxiv": {
    "totalPapers": 156
  },
  "npm": {
    "totalPackages": 87,
    "topPackage": "@modelcontextprotocol/sdk"
  },
  "stackoverflow": {
    "hasMore": true,
    "quotaRemaining": 285
  },
  "googleNews": {
    "articleCount": 45,
    "latestDate": "Mon, 18 Mar 2026 09:30:00 GMT"
  },
  "trendScore": 68,
  "trendLevel": "GROWING",
  "scrapedAt": "2026-03-19T12:00:00.000Z"
}

How It Works

1. You provide a technology topic or market keyword
2. Signal 1 -- Hacker News: Queries HN Algolia API for story count and surfaces the top story by points. 1,000+ stories = massive developer interest
3. Signal 2 -- arXiv Papers: Searches the arXiv API for academic research volume. 10,000+ papers = well-established research field
4. Signal 3 -- npm Ecosystem: Searches the npm registry for related packages. 1,000+ packages = mature developer ecosystem
5. Signal 4 -- Stack Overflow: Checks the Stack Exchange API for question volume. Active Q&A = real adoption and usage
6. Signal 5 -- Google News: Parses Google News RSS for article count and latest publication date. 50+ articles = strong media coverage
7. Scoring Engine -- each signal contributes weighted points to the composite score (max 100). The score maps to a trend level

Scoring Breakdown

Signal	HIGH Threshold	Points (HIGH)	MEDIUM Threshold	Points (MED)	LOW	Points (LOW)
Hacker News	1,000+ stories	25	100+ stories	15	<100	5
arXiv	10,000+ papers	25	1,000+ papers	15	<1,000	5
npm	1,000+ packages	20	100+ packages	10	<100	3
Google News	50+ articles	20	10+ articles	10	<10	3
Stack Overflow	hasMore = true	10	--	--	hasMore = false	3

Total Score	Level	Interpretation
80-100	HOT	Mainstream technology with massive adoption. Market is validated.
50-79	GROWING	Significant traction across multiple signals. Strong investment opportunity.
25-49	EMERGING	Early signs of growth. Worth watching, too early for mass market.
0-24	NICHE	Very limited signals. Either too new or too specialized.

Use Cases

1. Technology scouting -- evaluate emerging technologies before committing engineering resources. Compare "Rust vs Go vs Zig" trend scores side by side
2. Investment research -- VCs can scan potential investment themes across 5 data sources in seconds instead of commissioning analyst reports
3. Product roadmap decisions -- should you add WebAssembly support? Check if the trend is HOT or still NICHE before allocating a sprint
4. Content strategy -- identify GROWING topics to write about while they are still rising, not after they peak. Publish when the wave is building
5. Competitive positioning -- track which technologies your competitors are adopting by monitoring related trend signals
6. AI agent pipelines -- build autonomous research workflows that scan 50+ topics daily and surface only the ones trending upward
7. Academic research planning -- gauge how crowded a research area is (arXiv count) before choosing your PhD topic or grant application

Example Queries to Try

• "MCP Model Context Protocol" -- how fast is the AI agent ecosystem growing?
• "WebAssembly" -- is WASM still niche or going mainstream?
• "Rust programming" -- developer adoption trajectory
• "quantum computing" -- academic vs commercial maturity
• "Bluesky AT Protocol" -- is the decentralized social movement gaining traction?

Related Tools

• Browse all our MCP tools on Apify Store
• MCP Servers Collection on GitHub
• Full Market Research Reports (50+ pages, verified data)

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Trends do not wait for quarterly reports. Detect them in real time. Built for AI agents. Works for humans too.