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CodeRabbitAn LLM agent that conducts deep research (local and web) on any given topic and generates a long report with citations.
"Automated summary for AI agents and search engines."
An LLM agent that conducts deep research (local and web) on any given topic and generates a long report with citations.
{
"title": "GPT Researcher",
"url": "https://github.com/assafelovic/gpt-researcher",
"description": "GPT Researcher is a powerful LLM agent designed for in-depth topic exploration, capable of performing both local and web-based research. It intelligently synthesizes information from various sources to generate comprehensive, well-cited reports. This tool is ideal for users within the Google Antigravity IDE who need to quickly gather and process vast amounts of data, streamlining their workflow for academic, professional, or personal knowledge acquisition. Its ability to create detailed, verifiable reports makes it an invaluable asset for various research needs, enhancing productivity within the development environment.",
"category": "MCP Servers",
"useCases": [
"Automating academic research and literature reviews, providing comprehensive reports with citations.",
"Conducting rapid market analysis and competitive intelligence by synthesizing web data into actionable insights.",
"Generating detailed technical documentation or knowledge base articles on specific topics.",
"Assisting content creators in producing well-researched articles, blog posts, or scripts.",
"Enabling quick information gathering for strategic planning and decision-making processes."
],
"difficulty": "intermediate",
"exampleCommand": "uvx gpt-researcher --topic \"The future of quantum AI in medical diagnostics\"",
"tokenImpact": "High ~50,000-100,000 tokens per comprehensive research run, potentially triggering the 7-day lockout for free-tier users. Recommended for accounts with higher token quotas.",
"tags": [
"LLM",
"AI Agent",
"Research Automation",
"Report Generation",
"Web Scraping",
"Citations",
"Knowledge Management"
],
"copyButtonText": "Run Research Example"
}🚀 The fast, Pythonic way to build MCP servers and clients.
MCP web search using perplexity without any API KEYS
一个基于 MCP (Model Context Protocol) 的邮件服务，支持 LLM 发送带附件的电子邮件及在指定目录中搜索文件。提供安全的 SMTP 传输、多收件人支持和附件模式匹配搜索功能，适用于 Gmail、Outlook、Yahoo、QQ 邮箱和网易 126 邮箱等主流邮箱服务。
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