wet-mcp 2.20.0b1

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

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 Google Drive (OAuth Device Code, no browser redirect)

Quick Start

Claude Code Plugin (Recommended)

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

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

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

Gemini CLI Extension

gemini extensions install https://github.com/n24q02m/wet-mcp

Codex CLI

Add to ~/.codex/config.toml:

[mcp_servers.wet]
command = "uvx"
args = ["--python", "3.13", "wet-mcp"]

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"]
    }
  }
}

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.

Tools

Tool	Actions	Description
search	search, research, docs, similar	Web search (with filters, reranking, expand/enrich), academic research, library docs (HyDE), find similar
extract	extract, batch, crawl, map, convert, extract_structured	Content extraction, batch processing (up to 50 URLs), deep crawling, site mapping, local file conversion, structured data extraction (JSON Schema)
media	list, download, analyze	Media discovery, download, and analysis
config	status, set, cache_clear, docs_reindex	Server configuration and cache management
setup	warmup, setup_sync	Pre-download models, configure cloud sync
help	--	Full documentation for any tool

MCP Prompts

Prompt	Parameters	Description
research_topic	topic	Research a topic using academic search
library_docs	library, question	Find library documentation

Zero-Config Setup

No environment variables needed. On first start, the server opens a setup page in your browser:

1. Start the server (via plugin, uvx, or Docker)
2. A setup URL appears -- open it in any browser
3. Fill in your credentials on the guided form
4. Credentials are encrypted and stored locally

Your credentials never leave your machine. The relay server only sees encrypted data.

For CI/automation, you can still use environment variables (see below).

Configuration

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 uses Google Drive with OAuth Device Code flow (no browser redirect needed):

1. Configure: Set SYNC_ENABLED=true, GOOGLE_DRIVE_CLIENT_ID, and GOOGLE_DRIVE_CLIENT_SECRET
2. First sync: Run setup(action="setup_sync") -- visit URL and enter code
3. Token saved: OAuth token is stored locally at ~/.wet-mcp/tokens/ (600 permissions)
4. Subsequent runs: Token is loaded automatically, auto-refreshed when expired

{
  "SYNC_ENABLED": "true",
  "GOOGLE_DRIVE_CLIENT_ID": "your-client-id.apps.googleusercontent.com",
  "GOOGLE_DRIVE_CLIENT_SECRET": "your-client-secret"
}

Environment Variables

Variable	Required	Default	Description
API_KEYS	No	--	API keys for cloud providers (format: ENV_VAR:key,...). Enables cloud embedding + reranking
COHERE_API_KEY	No	--	Cohere API key (embedding + reranking)
JINA_AI_API_KEY	No	--	Jina AI API key (embedding + reranking)
GEMINI_API_KEY	No	--	Google Gemini API key (LLM + embedding)
OPENAI_API_KEY	No	--	OpenAI API key (LLM + embedding)
GITHUB_TOKEN	No	auto-detect	GitHub token for docs discovery (60 -> 5000 req/hr). Auto-detected from gh auth token
EMBEDDING_BACKEND	No	auto-detect	cloud or local (Qwen3). Auto: API_KEYS -> cloud, else local
EMBEDDING_MODEL	No	auto-detect	Cloud embedding model name
EMBEDDING_DIMS	No	0 (auto=768)	Embedding dimensions
RERANK_ENABLED	No	true	Enable reranking after search
RERANK_BACKEND	No	auto-detect	cloud or local. Auto: Cohere/Jina key -> cloud, else local
RERANK_MODEL	No	auto-detect	Cloud rerank model name
RERANK_TOP_N	No	10	Return top N results after reranking
LLM_MODELS	No	gemini-3-flash-preview	LLM model for media analysis (google-genai or openai)
WET_AUTO_SEARXNG	No	true	Auto-start embedded SearXNG subprocess
WET_SEARXNG_PORT	No	41592	SearXNG port
SEARXNG_URL	No	http://localhost:41592	External SearXNG URL (when auto disabled)
SEARXNG_TIMEOUT	No	30	SearXNG request timeout in seconds
CONVERT_MAX_FILE_SIZE	No	104857600	Max file size for local conversion in bytes (100MB)
CONVERT_ALLOWED_DIRS	No	--	Comma-separated paths to restrict local file conversion
CACHE_DIR	No	~/.wet-mcp	Data directory for cache, docs, downloads
DOCS_DB_PATH	No	~/.wet-mcp/docs.db	Docs database location
DOWNLOAD_DIR	No	~/.wet-mcp/downloads	Media download directory
TOOL_TIMEOUT	No	120	Tool execution timeout in seconds (0=no timeout)
WET_CACHE	No	true	Enable/disable web cache
SYNC_ENABLED	No	false	Enable Google Drive sync
GOOGLE_DRIVE_CLIENT_ID	No	--	OAuth client ID (required for sync)
GOOGLE_DRIVE_CLIENT_SECRET	No	--	OAuth client secret (required for sync)
SYNC_FOLDER	No	wet-mcp	Google Drive folder name
SYNC_INTERVAL	No	300	Auto-sync interval in seconds (0=manual)
LOG_LEVEL	No	INFO	Logging level

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 (2-Mode Architecture)

Priority	Mode	Config	Use case
1	SDK	GEMINI_API_KEY or OPENAI_API_KEY	Direct API access (google-genai, openai)
2	Disabled	Nothing needed	Offline, embedding/rerank only (no LLM)

SearXNG Configuration (2-Mode)

Mode	Config	Description
Embedded (default)	WET_AUTO_SEARXNG=true	Auto-installs and manages SearXNG as subprocess
External	WET_AUTO_SEARXNG=false + SEARXNG_URL=http://host:port	Connects to pre-existing SearXNG instance

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

License

MIT -- See LICENSE.