wet-mcp 2.6.1

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)

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.

On first run, the server automatically installs SearXNG, Playwright chromium, and starts the embedded search engine.

Option 1: Minimal uvx (Recommended)

{
  "mcpServers": {
    "wet": {
      "command": "uvx",
      "args": ["--python", "3.13", "wet-mcp@latest"]
      // No API keys needed -- local Qwen3-Embedding-0.6B + Qwen3-Reranker-0.6B (ONNX, CPU)
      // First run downloads ~570MB model, cached for subsequent runs
    }
  }
}

Option 2: Minimal Docker

{
  "mcpServers": {
    "wet": {
      "command": "docker",
      "args": [
        "run", "-i", "--rm",
        "--name", "mcp-wet",
        "-v", "wet-data:/data",
        "n24q02m/wet-mcp:latest"
      ]
      // Volume persists cached web pages, indexed docs, and downloads
      // Same built-in local embedding + reranking as uvx
    }
  }
}

Option 3: Full uvx

{
  "mcpServers": {
    "wet": {
      "command": "uvx",
      "args": ["--python", "3.13", "wet-mcp@latest"],
      "env": {
        "API_KEYS": "GOOGLE_API_KEY:AIza...",     // cloud embedding (Gemini > OpenAI > Mistral > Cohere) + media analysis
        // Reranking: auto local Qwen3-Reranker-0.6B. Or set RERANK_MODEL=cohere/rerank-v3.5 for cloud reranking
        "GITHUB_TOKEN": "ghp_...",                 // higher rate limits for docs discovery
        "SYNC_ENABLED": "true",                    // enable docs sync
        "SYNC_REMOTE": "gdrive",                   // rclone remote name
        "SYNC_INTERVAL": "300",                    // auto-sync every 5min (0 = manual)
        "RCLONE_CONFIG_GDRIVE_TYPE": "drive",
        "RCLONE_CONFIG_GDRIVE_TOKEN": "<base64>"   // from: uvx --python 3.13 wet-mcp setup-sync drive
      }
    }
  }
}

Option 4: Full Docker

{
  "mcpServers": {
    "wet": {
      "command": "docker",
      "args": [
        "run", "-i", "--rm",
        "--name", "mcp-wet",
        "-v", "wet-data:/data",
        "-e", "API_KEYS",
        "-e", "GITHUB_TOKEN",
        "-e", "SYNC_ENABLED",
        "-e", "SYNC_REMOTE",
        "-e", "SYNC_INTERVAL",
        "-e", "RCLONE_CONFIG_GDRIVE_TYPE",
        "-e", "RCLONE_CONFIG_GDRIVE_TOKEN",
        "n24q02m/wet-mcp:latest"
      ],
      "env": {
        "API_KEYS": "GOOGLE_API_KEY:AIza...",
        "GITHUB_TOKEN": "ghp_...",
        "SYNC_ENABLED": "true",
        "SYNC_REMOTE": "gdrive",
        "SYNC_INTERVAL": "300",
        "RCLONE_CONFIG_GDRIVE_TYPE": "drive",
        "RCLONE_CONFIG_GDRIVE_TOKEN": "<base64>"
      }
      // Same auto-detection: cloud embedding from API_KEYS, auto local reranking
    }
  }
}

Sync setup (one-time)

# Google Drive
uvx --python 3.13 wet-mcp setup-sync drive

# Other providers (any rclone remote type)
uvx --python 3.13 wet-mcp setup-sync dropbox
uvx --python 3.13 wet-mcp setup-sync onedrive
uvx --python 3.13 wet-mcp setup-sync s3

Opens a browser for OAuth and outputs env vars (RCLONE_CONFIG_*) to set. Both raw JSON and base64 tokens are supported.

---

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
config	status, set, cache_clear, docs_reindex	Server configuration and cache management
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"}
{"action": "docs", "query": "dependency injection", "library": "spring-boot", "language": "java"}

// 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	41592	SearXNG port (optional)
SEARXNG_URL	http://localhost:41592	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_BACKEND	(auto-detect)	litellm (cloud API) or local (Qwen3). Auto: API_KEYS -> litellm, else local (always available)
EMBEDDING_MODEL	(auto-detect)	LiteLLM embedding model (optional)
EMBEDDING_DIMS	0 (auto=768)	Embedding dimensions (optional)
RERANK_ENABLED	true	Enable reranking after search
RERANK_BACKEND	(auto-detect)	litellm or local. Auto: Cohere key in API_KEYS -> litellm, else local
RERANK_MODEL	(auto-detect)	LiteLLM rerank model (auto: cohere/rerank-v3.5 if Cohere key in API_KEYS)
RERANK_TOP_N	10	Return top N results after reranking
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)
GITHUB_TOKEN	-	GitHub personal access token for library discovery (optional, increases rate limit from 60 to 5000 req/hr)
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)

Embedding & Reranking

Both embedding and reranking are always available — local models are built-in and require no configuration.

• Embedding: Default local Qwen3-Embedding-0.6B. Set API_KEYS to upgrade to cloud (Gemini > OpenAI > Mistral > Cohere), with automatic local fallback if cloud fails.
• Reranking: Default local Qwen3-Reranker-0.6B. If COHERE_API_KEY is present in API_KEYS, auto-upgrades to cloud cohere/rerank-v3.5.
• GPU auto-detection: If GPU is available (CUDA/DirectML) and llama-cpp-python is installed, automatically uses GGUF models (~480MB) instead of ONNX (~570MB) for better performance.
• All embeddings stored at 768 dims (default). Switching providers never breaks the vector table.
• Override with EMBEDDING_BACKEND=local to force local even with API keys.

API_KEYS supports multiple providers in a single string:

API_KEYS=GOOGLE_API_KEY:AIza...,OPENAI_API_KEY:sk-...,COHERE_API_KEY:co-...

LLM Configuration (Optional)

For media analysis, configure API keys:

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

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Architecture

┌─────────────────────────────────────────────────────────┐
│                    MCP Client                           │
│            (Claude, Cursor, Windsurf)                   │
└─────────────────────┬───────────────────────────────────┘
                      │ MCP Protocol
                      v
┌─────────────────────────────────────────────────────────┐
│                   WET MCP Server                        │
│  ┌──────────┐  ┌──────────┐  ┌───────┐  ┌────────┐      │
│  │  search  │  │ extract  │  │ media │  │ config │      │
│  │ (search, │  │(extract, │  │(list, │  │(status,│      │
│  │ research,│  │ crawl,   │  │downld,│  │ set,   │      │
│  │ docs)    │  │ map)     │  │analyz)│  │ cache) │      │
│  └──┬───┬───┘  └────┬─────┘  └──┬────┘  └────────┘      │
│     │   │           │           │        + help tool     │
│     v   v           v           v                       │
│  ┌──────┐ ┌──────┐ ┌──────────┐ ┌──────────┐             │
│  │SearX │ │DocsDB│ │ Crawl4AI │ │ Reranker │             │
│  │NG    │ │FTS5+ │ │(Playwrgt)│ │(LiteLLM/ │             │
│  │      │ │sqlite│ │          │ │ Qwen3    │             │
│  │      │ │-vec  │ │          │ │ local)   │             │
│  └──────┘ └──────┘ └──────────┘ └──────────┘             │
│                                                         │
│  ┌──────────────────────────────────────────────────┐   │
│  │  WebCache (SQLite, TTL)  │  rclone sync (docs)   │   │
│  └──────────────────────────────────────────────────┘   │
└─────────────────────────────────────────────────────────┘

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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