📡 Trend Radar — an MCP Server for AI, Coding & Model Trends

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:

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.

Related MCP server: Daily Hots MCP

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)

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