WET - Web Extended Toolkit MCP Server

mcp-name: io.github.n24q02m/wet-mcp

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.

The recommended way to run this server is via uvx:

uvx --python 3.13 wet-mcp@latest

Alternatively, you can use pipx run --python python3.13 wet-mcp.

Option 1: uvx (Recommended)

{
  "mcpServers": {
    "wet": {
      "command": "uvx",
      "args": ["--python", "3.13", "wet-mcp@latest"],
      "env": {
        // -- optional: LiteLLM Proxy (production, selfhosted gateway)
        // "LITELLM_PROXY_URL": "http://10.0.0.20:4000",
        // "LITELLM_PROXY_KEY": "sk-your-virtual-key",
        // -- optional: cloud embedding (Gemini > OpenAI > Cohere) + media analysis
        // -- without this, uses built-in local Qwen3-Embedding-0.6B + Qwen3-Reranker-0.6B (ONNX, CPU)
        // -- first run downloads ~570MB model, cached for subsequent runs
        "API_KEYS": "GOOGLE_API_KEY:AIza...",
        // -- optional: custom endpoints (e.g. modalcom-ai-workers on Modal.com)
        // "EMBEDDING_API_BASE": "https://your-worker.modal.run",
        // "EMBEDDING_API_KEY": "your-key",
        // "RERANK_API_BASE": "https://your-worker.modal.run",
        // "RERANK_API_KEY": "your-key",
        // -- optional: higher rate limits for docs discovery (60 -> 5000 req/hr)
        "GITHUB_TOKEN": "ghp_...",
        // -- optional: sync indexed docs across machines via rclone
        "SYNC_ENABLED": "true",                    // optional, default: false
        "SYNC_REMOTE": "gdrive",                   // required when SYNC_ENABLED=true
        "SYNC_INTERVAL": "300",                    // optional, auto-sync every 5min (0 = manual only)
        "RCLONE_CONFIG_GDRIVE_TYPE": "drive",      // required when SYNC_ENABLED=true
        "RCLONE_CONFIG_GDRIVE_TOKEN": "<base64>"   // required when SYNC_ENABLED=true, from: uvx --python 3.13 wet-mcp setup-sync drive
      }
    }
  }
}

Option 2: Docker

{
  "mcpServers": {
    "wet": {
      "command": "docker",
      "args": [
        "run", "-i", "--rm",
        "--name", "mcp-wet",
        "-v", "wet-data:/data",                    // persists cached web pages, indexed docs, and downloads
        "-e", "LITELLM_PROXY_URL",                 // optional: pass-through from env below
        "-e", "LITELLM_PROXY_KEY",                 // optional: pass-through from env below
        "-e", "API_KEYS",                          // optional: pass-through from env below
        "-e", "EMBEDDING_API_BASE",                // optional: pass-through from env below
        "-e", "EMBEDDING_API_KEY",                 // optional: pass-through from env below
        "-e", "RERANK_API_BASE",                   // optional: pass-through from env below
        "-e", "RERANK_API_KEY",                    // optional: pass-through from env below
        "-e", "GITHUB_TOKEN",                      // optional: pass-through from env below
        "-e", "SYNC_ENABLED",                      // optional: pass-through from env below
        "-e", "SYNC_REMOTE",                       // required when SYNC_ENABLED=true: pass-through
        "-e", "SYNC_INTERVAL",                     // optional: pass-through from env below
        "-e", "RCLONE_CONFIG_GDRIVE_TYPE",         // required when SYNC_ENABLED=true: pass-through
        "-e", "RCLONE_CONFIG_GDRIVE_TOKEN",        // required when SYNC_ENABLED=true: pass-through
        "n24q02m/wet-mcp:latest"
      ],
      "env": {
        // -- optional: LiteLLM Proxy (production, selfhosted gateway)
        // "LITELLM_PROXY_URL": "http://10.0.0.20:4000",
        // "LITELLM_PROXY_KEY": "sk-your-virtual-key",
        // -- optional: cloud embedding (Gemini > OpenAI > Cohere) + media analysis
        // -- without this, uses built-in local Qwen3-Embedding-0.6B + Qwen3-Reranker-0.6B (ONNX, CPU)
        "API_KEYS": "GOOGLE_API_KEY:AIza...",
        // -- optional: custom endpoints (e.g. modalcom-ai-workers on Modal.com)
        // "EMBEDDING_API_BASE": "https://your-worker.modal.run",
        // "EMBEDDING_API_KEY": "your-key",
        // "RERANK_API_BASE": "https://your-worker.modal.run",
        // "RERANK_API_KEY": "your-key",
        // -- optional: higher rate limits for docs discovery (60 -> 5000 req/hr)
        "GITHUB_TOKEN": "ghp_...",
        // -- optional: sync indexed docs across machines via rclone
        "SYNC_ENABLED": "true",                    // optional, default: false
        "SYNC_REMOTE": "gdrive",                   // required when SYNC_ENABLED=true
        "SYNC_INTERVAL": "300",                    // optional, auto-sync every 5min (0 = manual only)
        "RCLONE_CONFIG_GDRIVE_TYPE": "drive",      // required when SYNC_ENABLED=true
        "RCLONE_CONFIG_GDRIVE_TOKEN": "<base64>"   // required when SYNC_ENABLED=true, from: uvx --python 3.13 wet-mcp setup-sync drive
      }
    }
  }
}

Pre-install (optional)

Pre-download all dependencies before adding to your MCP client config. This avoids slow first-run startup:

# Pre-download SearXNG, Playwright, embedding model (~570MB), and reranker model (~570MB)
uvx --python 3.13 wet-mcp warmup

# With cloud embedding (validates API key, skips local download if cloud works)
API_KEYS="GOOGLE_API_KEY:AIza..." uvx --python 3.13 wet-mcp warmup

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

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

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 > 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-multilingual-v3.0.
• 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 (3-Mode Architecture)

LLM access (for media analysis) supports 3 modes, resolved by priority:

No cross-mode fallback — if proxy is configured but unreachable, calls fail (no silent fallback to direct API).

SearXNG Configuration (2-Mode)

Web search is powered by SearXNG, a privacy-respecting metasearch engine.

Embedded mode is best for local development and single-user deployments. On first run, wet-mcp automatically downloads and configures SearXNG.

External mode is recommended when:

• Running in Docker (use a separate SearXNG container)
• Sharing a SearXNG instance across multiple services
• SearXNG is already deployed on your infrastructure

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)   │   │
│  └──────────────────────────────────────────────────┘   │
└─────────────────────────────────────────────────────────┘

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+)

Compatible With

Also by n24q02m

Related Projects

• modalcom-ai-workers — GPU-accelerated AI workers on Modal.com (embedding, reranking)
• qwen3-embed — Local embedding/reranking library used by wet-mcp

Contributing

See CONTRIBUTING.md

License

MIT - See LICENSE