n24q02m/wet-mcp

Overview

An open-source MCP server for web search, content extraction, library documentation lookup, and multimodal analysis. It uses Python and implements the Model Context Protocol to integrate these capabilities into AI applications.

Best for

Best for
Developers integrating web search and content extraction into MCP-based AI workflows

Use cases

• Search the web and extract content from pages
• Retrieve documentation for Python libraries
• Perform multimodal analysis on input data

How to use

Install

docker run -d --name wet-mcp-http -p 8084:8080 -v wet-data:/data -e MCP_TRANSPORT=http -e PUBLIC_URL=https://wet.example.com n24q02m/wet-mcp:latest

Tools exposed

• SearXNG
• Qwen3
• ONNX Runtime
• GGUF
• Google Scholar
• Semantic Scholar
• arXiv
• PubMed
• CrossRef
• BASE
• FTS5
• HyDE
• markitdown
• Cloudflare
• Medium
• LinkedIn
• Twitter
• Google Drive
• OAuth Device Code
• Tavily

Tested with

Claude Code, Claude Desktop, Cursor, Windsurf, ChatGPT

Example client config

EMBEDDING_MODELS=provider/model\nRERANK_MODELS=provider/model\nLLM_MODELS=provider/model\nJINA_AI_API_KEY=your_key

Notes

An open-source MCP server for web search, content extraction, library documentation lookup, and multimodal analysis. It uses Python and implements the Model Context Protocol to integrate these capabilities into AI applications.

9 stars on GitHub. Last updated 2026-06-01. Licensed MIT.

Use cases

• Search the web and extract content from pages
• Retrieve documentation for Python libraries
• Perform multimodal analysis on input data

Pros

• Free and open-source with no licensing costs
• Combines multiple capabilities in a single server
• Lightweight Python implementation for easy customization

Cons

• Small community with only 9 stars
• Limited documentation and examples available
• Relies on external APIs for search and extraction

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.