mcp-omnisearch and web-research-assistant
These are **competitors** — both provide unified web search capabilities through MCP, but A offers broader search engine and AI tool integration (Tavily, Brave, Kagi, Perplexity) while B specializes in SearXNG with additional domain-specific tools (GitHub, package info, API docs).
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.
About web-research-assistant
elad12390/web-research-assistant
MCP server for SearXNG with 13 production-ready tools for web search, package info, GitHub integration, error translation, API docs, and more
Implements the Model Context Protocol over stdio for seamless Claude Desktop and OpenCode integration, with configurable backends including local SearXNG, Exa AI neural search, crawl4ai for content extraction, and Pixabay for images. Exposes 4 MCP resources for direct data lookups (packages, repos, service status, changelogs) and 5 reusable prompt templates alongside the 13 tools, enabling AI agents to conduct structured research workflows with automatic response size limits and usage tracking.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work