Dev Radar MCP Server

An MCP server that gives AI agents cross-source developer ecosystem intelligence. Connect once to Claude Desktop, Cursor, or any MCP-compatible client, and your AI agent can analyze trends, discover emerging projects, and compare ecosystems across GitHub, Hacker News, Stack Overflow, Product Hunt, Dev.to, GitLab, and NPM.

What does Dev Radar MCP Server do?

This MCP server exposes 5 intelligence tools that AI agents can discover and call. Each tool synthesizes data from multiple sources into an intelligence report — not raw data, but cross-source correlation, velocity tracking, and topic clustering.

The difference between this and a raw scraper: when you ask "what's trending in Python?", a raw scraper gives you 25 repos. Dev Radar gives you topic clusters, cross-source correlations (which repos also appeared on HN, Dev.to, and Product Hunt), velocity trends (compared to last week's snapshot), and a qualitative assessment.

Why use Dev Radar MCP Server?

• 7 data sources, one connection — GitHub Trending, Hacker News, Stack Overflow, Product Hunt, Dev.to, GitLab, and NPM in a single MCP server
• Cross-source correlation — projects that appear across multiple platforms get higher signal scores
• Historical velocity tracking — weekly snapshots stored in KV stores enable week-over-week comparison
• Topic clustering — repos and discussions grouped by technology theme (AI agents, web frameworks, devtools, etc.)
• Intelligence, not data — synthesized reports with assessments, not raw JSON dumps
• 3 free intelligence reports per call — try each tool without paying

Tools

Tool	What it returns	Price per call
analyze_ecosystem(languages, timeframe)	Full ecosystem health report with topic clusters, cross-source correlations, velocity metrics, and top trending per source	$0.25
discover_emerging_projects(keywords, min_signal_score, max_total_stars)	Projects with high cross-source signal but low total stars — early breakout detection	$0.15
get_intelligence_report(topic)	Comprehensive report on any developer topic: adoption signals, key projects, discussions, velocity, and qualitative assessment	$0.50
detect_topic_shifts(timeframe)	Rising and falling topic trends — which ecosystems are gaining or losing momentum	$0.15
compare_ecosystems(languages)	Side-by-side language comparison: trending repos, topic overlap, HN/Dev.to mentions	$0.20

How to connect

Claude Desktop

Add to your MCP config:

{
  "mcpServers": {
    "dev-radar": {
      "url": "https://actors.apify.com/ef12~dev-radar-mcp/mcp"
    }
  }
}

Cursor

Add to .cursor/mcp.json:

{
  "mcpServers": {
    "dev-radar": {
      "url": "https://actors.apify.com/ef12~dev-radar-mcp/mcp"
    }
  }
}

Example prompts for your AI agent

• "Analyze the Python ecosystem this week — what are the top trends?"
• "Discover emerging AI agent projects that haven't hit 10K stars yet"
• "Give me an intelligence report on the MCP ecosystem"
• "Detect topic shifts — what's rising and falling in the dev world?"
• "Compare Python, JavaScript, and Rust ecosystems side by side"

Use Cases

How to track developer ecosystem trends weekly

Connect the MCP server to your AI agent and schedule a weekly conversation: "Analyze the Python and JavaScript ecosystems this week." Your agent calls analyze_ecosystem, synthesizes the report, and summarizes the key changes.

How to discover breakout projects before they go mainstream

Use discover_emerging_projects with keywords like "ai,agent,llm" and a low max_total_stars threshold. Projects that show cross-source signal but haven't hit mainstream star counts are your early breakout candidates.

How to monitor the MCP ecosystem

Call get_intelligence_report(topic="mcp") to get a full synthesized report: how many repos are trending, how many HN discussions, Product Hunt launches, Dev.to articles, NPM packages, and a qualitative assessment of ecosystem momentum.

Pricing

Pay-Per-Event with a free tier:

• 3 free intelligence reports per tool call
• $0.15-$0.50 per report after the free tier, depending on the tool
• apify-actor-start at $0.00005

Cost example: A weekly ecosystem analysis with 2 follow-up tool calls = 2 paid reports = ~$0.50 per week

How it works under the hood

Each tool call triggers the intelligence engine:

1. Fetches current data from multiple Apify actors in parallel (GitHub, HN, SO, PH, Dev.to, GitLab, NPM)
2. Loads previous snapshots from Apify KV stores for historical comparison
3. Computes cross-source correlations — which projects appear on multiple platforms
4. Clusters items by topic using keyword matching
5. Calculates velocity metrics — week-over-week percentage change
6. Generates a qualitative assessment based on signal volume and cross-source presence
7. Saves current snapshot for next call's velocity comparison

The raw scrapers are invisible infrastructure. The intelligence layer is the product.

FAQ

Do I need an Apify account?

Yes — the server calls Apify actors under the hood. You need an Apify account with API token set in the MCP connection config. Free tier includes $5 in monthly credits.

How is this different from the individual scrapers?

Individual scrapers return raw data (25 repos, 30 HN stories). This server synthesizes across all sources — a project that appears on GitHub, HN, and Dev.to gets a much higher signal score than one that only appears on GitHub. You get a report, not a data dump.

How fast are the tool calls?

Most calls take 10-30 seconds since they fetch from multiple sources in parallel. The intelligence processing (clustering, correlation, velocity) runs in milliseconds once data is fetched.

Can I schedule automated intelligence reports?

Yes — use the Apify API to call individual tools on a schedule, or have your AI agent run a weekly analysis conversation.

Disclaimer

This Actor is designed for ethical web scraping. It only extracts publicly available data and does not access private user information. You should be aware that your results may contain personal data. Personal data is protected by GDPR in the European Union and by other regulations worldwide.