Research
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Enables large language models to fetch, analyze, and summarize academic research papers from multiple trusted sources in real-time.
About
Research is a comprehensive Model Context Protocol (MCP)-based assistant designed to empower large language models (LLMs) with real-time access to academic research. It achieves this by providing standardized access to prominent research databases like arXiv, Semantic Scholar, and PubMed through dedicated MCP servers. This system allows AI assistants to efficiently query live research data, process the results, and generate structured insights such as summaries, comparisons, and properly formatted citations.
Key Features
- Automatic Deduplication: Smart paper matching across different sources
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- Citation Analysis: Track citation counts and influential papers
- Advanced Filtering: Filter by year, author, venue, and more
- Multi-Source Search: Query arXiv, Semantic Scholar, and PubMed simultaneously
- BibTeX Generation: Automatic citation formatting for all sources
Use Cases
- Conduct multi-source research queries on specific topics, combining results from various academic databases.
- Identify the most cited or influential papers within a specific year, subject, or author.
- Analyze research trends and summarize key innovations in a particular scientific or technical field.
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