wet-mcp and mcp-omnisearch
About wet-mcp
n24q02m/wet-mcp
MCP server for web search, content extraction, and documentation indexing
Provides embedded metasearch (SearXNG) with semantic reranking and query expansion, plus specialized academic research across Google Scholar, arXiv, and PubMed. Features local full-text documentation indexing with HyDE-enhanced retrieval, batch content extraction from up to 50 URLs, and multimodal analysis—all with zero-config local embeddings (Qwen3) or optional cloud providers. Integrates as an MCP server with Claude, Gemini, and Codex via stdio transport, with automatic setup and encrypted credential storage.
About mcp-omnisearch
spences10/mcp-omnisearch
🔍 A Model Context Protocol (MCP) server providing unified access to multiple search engines (Tavily, Brave, Kagi), AI tools (Perplexity, FastGPT), and content processing services (Jina AI, Kagi). Combines search, AI responses, content processing, and enhancement features through a single interface.
# Technical Summary Implements four consolidated MCP tools (web_search, ai_search, github_search, web_extract) with pluggable provider backends, allowing clients to query multiple APIs through a unified interface while gracefully degrading based on available credentials. Supports advanced search operators native to Brave/Kagi, domain filtering via API parameters, and specialized extractors like Firecrawl's interactive scraping and Kagi's multimodal summarization (pages, videos, podcasts). Designed for integration with AI assistants (Claude Desktop, Cline) via environment-variable configuration with zero hard dependencies on any single provider.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work