Trend Radar

Trend Radar is a Model Context Protocol (MCP) server written in Python. It gives any MCP client — Claude Desktop, Claude Code, the MCP Inspector, or your own agent — eight tools that fetch Top 20 trend lists from across the AI ecosystem and render them into a polished, self-contained HTML dashboard:

What is this MCP server?

MCP is an open protocol that lets AI assistants call external tools. This server plugs into your assistant and answers questions like "what's trending in AI right now?" with live, structured data instead of stale training knowledge. It covers three categories:

Category	Tool	Data source
📰 News	get_x_ai_trends	X (Twitter) API v2 — live with a bearer token, curated mock fallback without
📰 News	get_google_news_ai_trends	Official Google News RSS feed, parsed with feedparser
💻 Coding	get_github_trends	GitHub Search API — most-starred repos created in the last 7 days
💻 Coding	get_github_most_starred	GitHub Search API — all-time star leaders
💻 Coding	get_github_most_forked	GitHub Search API — all-time fork leaders
🤗 Models	get_hf_trending_models	Hugging Face Hub API (sort=trendingScore)
🤗 Models	get_hf_most_liked_models	Hugging Face Hub API (sort=likes)
🎨 Web	generate_trends_dashboard	Aggregates all 7 feeds → one index.html

Every data tool returns clean JSON:

{
  "source": "github_trending",
  "count": 20,
  "items": [
    {
      "rank": 1,
      "title": "owner/repo",
      "url": "https://github.com/owner/repo",
      "description": "What the project does",
      "metrics": { "stars": "12.4k", "forks": "980", "language": "Python" }
    }
  ]
}

If a feed fails, the tool returns {"source": ..., "error": ..., "items": []} instead of crashing — and the dashboard shows an inline note for that card while the rest of the page still renders.

Project structure

mcp_Trend_Radar/
├── server.py                 # FastMCP server — registers all 8 tools
├── services/
│   ├── models.py             # TrendItem dataclass + badge formatting
│   ├── http_client.py        # shared requests wrapper + ServiceError
│   ├── x.py                  # X trends (live API / mock fallback)
│   ├── google_news.py        # Google News RSS via feedparser
│   ├── github.py             # GitHub Search API (3 views)
│   ├── huggingface.py        # HF Hub API (trending / most liked)
│   └── dashboard.py          # aggregation + HTML generation + local server
├── docs/images/              # screenshots used in this README
├── requirements.txt
└── .env.example

---

How to use it — step by step

Step 1 · Install

git clone https://github.com/girlmoony/mcp_Trend_Radar.git
cd mcp_Trend_Radar
python -m venv .venv
.venv\Scripts\Activate.ps1
pip install -r requirements.txt
copy .env.example .env    # optional — every tool works without tokens

Step 2 · Try it standalone (no MCP client needed)

The dashboard module doubles as a CLI. This fetches all seven feeds and serves the result:

python -m services.dashboard          # builds output/index.html + serves on :8000
python -m services.dashboard --build  # build only

Open http://127.0.0.1:8000 and you'll see the page from the screenshot above: three color-coded categories, numbered Top-20 lists, and engagement badges.

💻 Coding section — three GitHub views side by side, with stars / forks / language badges:

🤗 Models section — Hugging Face trending and most-liked models, with likes / downloads / task badges:

Step 3 · Register with Claude Desktop

Add the server to claude_desktop_config.json (Windows: %APPDATA%\Claude\claude_desktop_config.json, macOS: ~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "trend-radar": {
      "command": "C:\\path\\to\\python.exe",
      "args": ["C:\\path\\to\\mcp_Trend_Radar\\server.py"]
    }
  }
}

Restart Claude Desktop. The 8 tools appear under the 🔨 tools menu, and you can simply ask:

"What are the trending GitHub repos this week?" "Show me the most liked Hugging Face models." "Generate the trends dashboard and tell me where the file is."

Step 4 · Register with Claude Code (CLI)

claude mcp add trend-radar -- python "C:\path\to\mcp_Trend_Radar\server.py"

Step 5 · Debug with the MCP Inspector

mcp dev server.py

This opens the Inspector web UI where you can list the tools, call each one, and inspect the raw JSON responses interactively.

---

The dashboard tool

Calling generate_trends_dashboard (from any MCP client, or via the CLI in Step 2) writes a single self-contained index.html:

• Semantic HTML5 + custom CSS variables — zero JavaScript, zero external assets
• Distinct colored sections per platform: 🔵 News, 🟣 Coding, 🟡 Models
• Numbered lists with linked titles, snippets, and engagement badges (stars, forks, likes, downloads, reposts)
• Automatic dark mode via prefers-color-scheme and a print-friendly layout
• Per-feed error isolation — one failing API never blanks the page

The tool returns a JSON build summary with the absolute output path and per-section item counts.

Configuration (all optional)

Variable	Effect
GITHUB_TOKEN	Raises the GitHub Search rate limit from 10 to 30 requests/min
X_BEARER_TOKEN	Switches X trends from curated mock data to the live X API v2
HF_TOKEN	Authenticated Hugging Face Hub requests

Copy .env.example to .env and fill in what you have — the server loads it automatically.

Requirements

• Python 3.10+
• mcp · requests · feedparser · python-dotenv (see requirements.txt)

Related projects

This repo previously included AgentLens, a local cost/efficiency scanner for Claude Code sessions. It has been split out into its own repository, since it's a general-purpose tool unrelated to trend research: https://github.com/girlmoony/agentlens

License

MIT