io.github.n24q02m/wet-mcp

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 -- Embedded SearXNG metasearch (Google, Bing, DuckDuckGo, Brave) with filters, semantic reranking, query expansion, and snippet enrichment
• Academic Research -- Search Google Scholar, Semantic Scholar, arXiv, PubMed, CrossRef, BASE
• Library Docs -- Auto-discover and index documentation with FTS5 hybrid search, HyDE-enhanced retrieval, and version-specific docs
• Content Extract -- Clean content extraction (Markdown/Text), structured data extraction (LLM + JSON Schema), batch processing (up to 50 URLs), deep crawling, site mapping
• Local File Conversion -- Convert PDF, DOCX, XLSX, CSV, HTML, EPUB, PPTX to Markdown
• Media -- List, download, and analyze images, videos, audio files
• Anti-bot -- Stealth mode bypasses Cloudflare, Medium, LinkedIn, Twitter
• Zero Config -- Built-in local Qwen3 embedding + reranking, no API keys needed. Optional cloud providers (Jina AI, Gemini, OpenAI, Cohere)
• Sync -- Cross-machine sync of indexed docs via rclone (Google Drive, S3, Dropbox)

Quick Start

Claude Code Plugin (Recommended)

Via marketplace (includes skills: /fact-check, /compare):

/plugin marketplace add n24q02m/claude-plugins
/plugin install wet-mcp@claude-plugins

Or install this plugin only:

/plugin marketplace add n24q02m/wet-mcp
/plugin install wet-mcp

Configure env vars in ~/.claude/settings.local.json or shell profile. See Environment Variables.

MCP Server

Python 3.13 required -- Python 3.14+ is not supported due to SearXNG incompatibility. You must specify --python 3.13 when using uvx.

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

Option 1: uvx

{
  "mcpServers": {
    "wet": {
      "command": "uvx",
      "args": ["--python", "3.13", "wet-mcp@latest"]
    }
  }
}

// Cursor (~/.cursor/mcp.json), Windsurf, Cline, Amp, OpenCode
{
  "mcpServers": {
    "wet": {
      "command": "uvx",
      "args": ["--python", "3.13", "wet-mcp@latest"]
    }
  }
}

# Codex (~/.codex/config.toml)
[mcp_servers.wet]
command = "uvx"
args = ["--python", "3.13", "wet-mcp@latest"]

Option 2: 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",
        "n24q02m/wet-mcp:latest"
      ]
    }
  }
}

Configure env vars in ~/.claude/settings.local.json or your shell profile. See Environment Variables below.

Pre-install (optional)

Use the setup MCP tool to warmup models and install dependencies:

# Via MCP tool call (recommended):
setup(action="warmup")

# With cloud embedding configured, warmup validates API keys
# and skips local model download if cloud models are available.

The warmup action pre-downloads SearXNG, Playwright, and embedding/reranker models (~1.1GB total) so the first real connection does not timeout.

Sync setup

Sync is fully automatic. Just set SYNC_ENABLED=true and the server handles everything:

1. First sync: rclone is auto-downloaded, a browser opens for OAuth authentication
2. Token saved: OAuth token is stored locally at ~/.wet-mcp/tokens/ (600 permissions)
3. Subsequent runs: Token is loaded automatically -- no manual steps needed

For non-Google Drive providers, set SYNC_PROVIDER and SYNC_REMOTE:

{
  "SYNC_ENABLED": "true",
  "SYNC_PROVIDER": "dropbox",
  "SYNC_REMOTE": "dropbox"
}

Tools

MCP Prompts

Configuration

Embedding & Reranking

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

• Jina AI (recommended): A single JINA_AI_API_KEY enables both embedding and reranking
• Embedding priority: Jina AI > Gemini > OpenAI > Cohere. Local Qwen3 fallback always available
• Reranking priority: Jina AI > Cohere. Local Qwen3 fallback always available
• GPU auto-detection: CUDA/DirectML auto-detected, uses GGUF models for better performance
• All embeddings stored at 768 dims. Switching providers never breaks the vector table

LLM Configuration (3-Mode Architecture)

SearXNG Configuration (2-Mode)

Security

• SSRF prevention -- URL validation on crawl targets
• Graceful fallbacks -- Cloud → Local embedding, multi-tier crawling
• Error sanitization -- No credentials in error messages
• File conversion sandboxing -- Optional CONVERT_ALLOWED_DIRS restriction

Build from Source

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

Compatible With

Also by n24q02m

Contributing

See CONTRIBUTING.md.

License

MIT -- See LICENSE.