wet-mcp 2.5.0b0

Open-source MCP Server for web search, extract, crawl, academic research, and library docs with embedded SearXNG

WET - Web Extended Toolkit MCP Server

Open-source MCP Server for web search, content extraction, library docs & multimodal analysis.

Features

• Web Search - Search via embedded SearXNG (metasearch: Google, Bing, DuckDuckGo, Brave)
• Academic Research - Search Google Scholar, Semantic Scholar, arXiv, PubMed, CrossRef, BASE
• Library Docs - Auto-discover and index documentation with FTS5 hybrid search
• Content Extract - Extract clean content (Markdown/Text)
• Deep Crawl - Crawl multiple pages from a root URL with depth control
• Site Map - Discover website URL structure
• Media - List and download images, videos, audio files
• Anti-bot - Stealth mode bypasses Cloudflare, Medium, LinkedIn, Twitter
• Local Cache - TTL-based caching for all web operations
• Docs Sync - Sync indexed docs across machines via rclone

---

Quick Start

Prerequisites

• Python 3.13 (required -- Python 3.14+ is not supported due to SearXNG incompatibility)

Add to mcp.json

uvx (Recommended)

{
  "mcpServers": {
    "wet": {
      "command": "uvx",
      "args": ["--python", "3.13", "wet-mcp@latest"],
      "env": {
        // Optional: API keys for embedding and media analysis
        "API_KEYS": "GOOGLE_API_KEY:AIza..."
      }
    }
  }
}

Warning: You must specify --python 3.13 when using uvx. Without it, uvx may pick Python 3.14+ which causes SearXNG search to fail silently (RuntimeError: can't register atexit after shutdown in DNS resolution).

That's it! On first run:

1. Automatically installs SearXNG from GitHub
2. Automatically installs Playwright chromium + system dependencies
3. Starts embedded SearXNG subprocess
4. Runs the MCP server

Docker

{
  "mcpServers": {
    "wet": {
      "command": "docker",
      "args": [
        "run", "-i", "--rm",
        "-e", "API_KEYS",
        "n24q02m/wet-mcp:latest"
      ],
      "env": {
        "API_KEYS": "GOOGLE_API_KEY:AIza..."
      }
    }
  }
}

With docs sync (Google Drive)

Step 1: Get a drive token (one-time, requires browser):

uvx --python 3.13 wet-mcp setup-sync drive

This downloads rclone, opens a browser for Google Drive auth, and outputs a base64-encoded token for RCLONE_CONFIG_GDRIVE_TOKEN.

Step 2: Copy the token and add it to your MCP config:

{
  "mcpServers": {
    "wet": {
      "command": "uvx",
      "args": ["--python", "3.13", "wet-mcp@latest"],
      "env": {
        "API_KEYS": "GOOGLE_API_KEY:AIza...",
        "SYNC_ENABLED": "true",
        "SYNC_REMOTE": "gdrive",
        "SYNC_INTERVAL": "300",
        "RCLONE_CONFIG_GDRIVE_TYPE": "drive",
        "RCLONE_CONFIG_GDRIVE_TOKEN": "<paste base64 token>"
      }
    }
  }
}

Both raw JSON and base64-encoded tokens are supported. Base64 is recommended — it avoids nested JSON escaping issues.

Without uvx

pip install wet-mcp
wet-mcp

---

Tools

Tool	Actions	Description
search	search, research, docs	Web search, academic research, library documentation
extract	extract, crawl, map	Content extraction, deep crawling, site mapping
media	list, download, analyze	Media discovery & download
help	-	Full documentation for any tool

Usage Examples

// search tool
{"action": "search", "query": "python web scraping", "max_results": 10}
{"action": "research", "query": "transformer attention mechanism"}
{"action": "docs", "query": "how to create routes", "library": "fastapi"}

// extract tool
{"action": "extract", "urls": ["https://example.com"]}
{"action": "crawl", "urls": ["https://docs.python.org"], "depth": 2}
{"action": "map", "urls": ["https://example.com"]}

// media tool
{"action": "list", "url": "https://github.com/python/cpython"}
{"action": "download", "media_urls": ["https://example.com/image.png"]}

---

Configuration

Variable	Default	Description
WET_AUTO_SEARXNG	true	Auto-start embedded SearXNG subprocess
WET_SEARXNG_PORT	8080	SearXNG port
SEARXNG_URL	http://localhost:8080	External SearXNG URL (when auto disabled)
API_KEYS	-	LLM API keys (format: ENV_VAR:key,...)
EMBEDDING_MODEL	(auto-detect)	LiteLLM embedding model for docs vector search
EMBEDDING_DIMS	0 (auto=768)	Embedding dimensions
WET_CACHE	true	Enable/disable web cache
SYNC_ENABLED	false	Enable rclone sync for docs DB
SYNC_REMOTE	-	rclone remote name (e.g., "gdrive")
SYNC_FOLDER	wet-mcp	Remote folder name
SYNC_INTERVAL	0	Auto-sync interval in seconds (0 = manual)
LOG_LEVEL	INFO	Logging level

LLM Configuration (Optional)

For media analysis and docs embedding, configure API keys:

API_KEYS=GOOGLE_API_KEY:AIza...
LLM_MODELS=gemini/gemini-3-flash-preview

The server auto-detects embedding models from configured API keys (Gemini > OpenAI > Mistral > Cohere).

---

Architecture

┌─────────────────────────────────────────────────────────┐
│                    MCP Client                           │
│            (Claude, Cursor, Windsurf)                   │
└─────────────────────┬───────────────────────────────────┘
                      │ MCP Protocol
                      v
┌─────────────────────────────────────────────────────────┐
│                   WET MCP Server                        │
│  ┌──────────┐  ┌──────────┐  ┌───────┐  ┌──────────┐   │
│  │  search  │  │ extract  │  │ media │  │   help   │   │
│  │ (search, │  │(extract, │  │(list, │  │          │   │
│  │ research,│  │ crawl,   │  │downld,│  │          │   │
│  │ docs)    │  │ map)     │  │analyz)│  │          │   │
│  └──┬───┬───┘  └────┬─────┘  └──┬────┘  └──────────┘   │
│     │   │           │           │                       │
│     v   v           v           v                       │
│  ┌──────┐ ┌──────┐ ┌──────────┐                         │
│  │SearX │ │DocsDB│ │ Crawl4AI │                         │
│  │NG    │ │FTS5+ │ │(Playwrgt)│                         │
│  │      │ │sqlite│ │          │                         │
│  │      │ │-vec  │ │          │                         │
│  └──────┘ └──────┘ └──────────┘                         │
│                                                         │
│  ┌──────────────────────────────────────────────────┐   │
│  │  WebCache (SQLite, TTL)  │  rclone sync (docs)   │   │
│  └──────────────────────────────────────────────────┘   │
└─────────────────────────────────────────────────────────┘

---

Build from Source

git clone https://github.com/n24q02m/wet-mcp
cd wet-mcp

# Setup (requires mise: https://mise.jdx.dev/)
mise run setup

# Run
uv run wet-mcp

Docker Build

docker build -t n24q02m/wet-mcp:latest .

Requirements: Python 3.13 (not 3.14+)

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Contributing

See CONTRIBUTING.md

License

MIT - See LICENSE