Open-source MCP Server for web search, extract, crawl, academic research, and library docs with embedded SearXNG
Project description
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 3.13when usinguvx. Without it,uvxmay pick Python 3.14+ which causes SearXNG search to fail silently (RuntimeError: can't register atexit after shutdownin DNS resolution).
That's it! On first run:
- Automatically installs SearXNG from GitHub
- Automatically installs Playwright chromium + system dependencies
- Starts embedded SearXNG subprocess
- Runs the MCP server
Docker
{
"mcpServers": {
"wet": {
"command": "docker",
"args": [
"run", "-i", "--rm",
"-v", "wet-data:/data",
"-e", "API_KEYS",
"n24q02m/wet-mcp:latest"
],
"env": {
"API_KEYS": "GOOGLE_API_KEY:AIza..."
}
}
}
}
The
-v wet-data:/datavolume mount persists cached web pages, indexed library docs, and downloaded media across container restarts.
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...", // optional: enables media analysis & docs embedding
"SYNC_ENABLED": "true", // required for sync
"SYNC_REMOTE": "gdrive", // required: rclone remote name
"SYNC_INTERVAL": "300", // optional: auto-sync seconds (default: 0 = manual)
// "SYNC_FOLDER": "wet-mcp", // optional: remote folder (default: wet-mcp)
"RCLONE_CONFIG_GDRIVE_TYPE": "drive", // required: rclone backend type
"RCLONE_CONFIG_GDRIVE_TOKEN": "<paste base64 token>" // required: from setup-sync
}
}
}
}
Both raw JSON and base64-encoded tokens are supported. Base64 is recommended — it avoids nested JSON escaping issues.
Remote is configured via env vars — works in any environment (local, Docker, CI).
With sync in Docker
{
"mcpServers": {
"wet": {
"command": "docker",
"args": [
"run", "-i", "--rm",
"-v", "wet-data:/data",
"-e", "API_KEYS",
"-e", "SYNC_ENABLED",
"-e", "SYNC_REMOTE",
"-e", "SYNC_INTERVAL", // optional: remove if manual sync only
"-e", "RCLONE_CONFIG_GDRIVE_TYPE",
"-e", "RCLONE_CONFIG_GDRIVE_TOKEN",
"n24q02m/wet-mcp:latest"
],
"env": {
"API_KEYS": "GOOGLE_API_KEY:AIza...", // optional: enables media analysis & docs embedding
"SYNC_ENABLED": "true", // required for sync
"SYNC_REMOTE": "gdrive", // required: rclone remote name
"SYNC_INTERVAL": "300", // optional: auto-sync seconds (default: 0 = manual)
// "SYNC_FOLDER": "wet-mcp", // optional: remote folder (default: wet-mcp)
"RCLONE_CONFIG_GDRIVE_TYPE": "drive", // required: rclone backend type
"RCLONE_CONFIG_GDRIVE_TOKEN": "<paste base64 token>" // required: from setup-sync
}
}
}
}
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 (optional) |
SEARXNG_URL |
http://localhost:8080 |
External SearXNG URL (optional, when auto disabled) |
SEARXNG_TIMEOUT |
30 |
SearXNG request timeout in seconds (optional) |
API_KEYS |
- | LLM API keys (optional, format: ENV_VAR:key,...) |
LLM_MODELS |
gemini/gemini-3-flash-preview |
LiteLLM model for media analysis (optional) |
EMBEDDING_MODEL |
(auto-detect) | LiteLLM embedding model for docs vector search (optional) |
EMBEDDING_DIMS |
0 (auto=768) |
Embedding dimensions (optional) |
CACHE_DIR |
~/.wet-mcp |
Data directory for cache DB, docs DB, downloads (optional) |
DOCS_DB_PATH |
~/.wet-mcp/docs.db |
Docs database location (optional) |
DOWNLOAD_DIR |
~/.wet-mcp/downloads |
Media download directory (optional) |
TOOL_TIMEOUT |
120 |
Tool execution timeout in seconds, 0=no timeout (optional) |
WET_CACHE |
true |
Enable/disable web cache (optional) |
SYNC_ENABLED |
false |
Enable rclone sync |
SYNC_REMOTE |
- | rclone remote name (required when sync enabled) |
SYNC_FOLDER |
wet-mcp |
Remote folder name (optional) |
SYNC_INTERVAL |
0 |
Auto-sync interval in seconds, 0=manual (optional) |
LOG_LEVEL |
INFO |
Logging level (optional) |
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+)
Contributing
See CONTRIBUTING.md
License
MIT - See LICENSE
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