AiryLark MCP is a professional - level translation service interface. It uses a three - phase process to ensure translation quality, supports multi - language translation and domain term recognition, and is suitable for professional scenarios such as technical documents and academic papers.

An MCP server for searching arXiv academic papers, providing paper retrieval, metadata access, and result sorting and pagination functions.

The AMiner academic search server based on the Model Context Protocol provides functions for paper keyword, journal, and author search, as well as an AI research assistant.

An MCP server for searching and downloading papers from multiple academic platforms (such as arXiv, PubMed, bioRxiv, etc.), supporting standardized search and acquisition tools, facilitating AI - driven research workflows.

An MCP service for academic paper search based on the OpenAlex API, providing functions to retrieve papers by topic, keyword, or author, and obtain detailed paper information.

The PubMed Enhanced Search MCP Server provides advanced academic paper retrieval and medical subject term query functions.

An advanced medical literature research tool based on the PubMed database, providing functions for searching, analyzing, and retrieving academic medical papers, including literature search, obtaining detailed metadata, generating citations, and researcher statistics.

Academia MCP is an MCP server designed for academic research. It provides tools for searching, retrieving, analyzing, and reporting scientific papers and datasets. It supports platforms such as ArXiv, ACL Anthology, and Hugging Face, and includes functions like web page crawling, LaTeX compilation, PDF reading, and LLM enhancement.

Academic Hub is a revolutionary research platform that provides AI - assisted paper creation, sharing, and collaboration functions for researchers, including paper construction, plagiarism detection, and academic community interaction.

An implementation of the Semantic Scholar API service based on the FastMCP framework, providing functions for academic paper search, author information query, citation analysis, and recommendation.

Google Scholar MCP Server is an interface service that provides AI assistants with access to academic papers on Google Scholar through the Model Context Protocol (MCP). It supports keyword search, advanced retrieval, and author information query functions to help AI models retrieve academic paper metadata.

A PubMed academic paper search service based on the Model Content Protocol

An academic paper search server based on the MCP protocol, providing multi - source paper retrieval, metadata acquisition, and full - text access functions

The Sci-Hub MCP service is a bridge connecting AI assistants with Sci-Hub's academic resources, providing functions such as paper search, metadata acquisition, and PDF download through the Model Context Protocol.

Paper Search MCP is an academic paper search and download server based on the MCP protocol, supporting the retrieval of papers from multiple platforms such as arXiv, PubMed, and bioRxiv, providing a standardized output format, and seamlessly integrating with large language models like Claude.

A powerful scholarly research tool that integrates the search functions of multiple academic databases such as PubMed, Google Scholar, ArXiv, and JSTOR, providing functions such as paper search, full - text reading, key information extraction, citation management, and research organization.

The Crossref MCP Server is a bridge service that connects AI assistants with the Crossref academic database through the Model Context Protocol (MCP), supporting the search for papers, acquisition of metadata, and information on journals and funding agencies by DOI, title, or keywords.

The Unpaywall MCP server is a tool server based on the model context protocol, providing functions such as obtaining academic paper metadata via DOI, searching for article titles, obtaining open-access full-text links, and downloading and extracting PDF text.

The arXiv Research Assistant MCP Server is a local server built based on Python and the FastMCP framework, specifically designed to interact with the arXiv.org paper database. It provides functions such as keyword search, getting the latest papers in categories, querying paper details, author search, trend analysis, etc., and supports generating paper summaries and comparison prompts, facilitating users to efficiently explore academic literature.

An MCP server that provides access to the Semantic Scholar academic graph API, supporting functions such as searching for academic papers, author information, obtaining citation analysis, and PDF downloads.

ScholarScope MCP is an academic research MCP server built on FastMCP, providing functions such as paper, author, and institution search, citation relationship query, and full - text retrieval through the OpenAlex API, specifically designed for intelligent research assistants.

A microservice project based on FastMCP, providing search tools for Wikipedia and Arxiv academic papers, and supporting interaction with language models such as Claude through the MCP protocol.

The arXiv Research Assistant MCP Server is a local server built on Python and the FastMCP framework, specifically designed to interact with the arXiv.org paper database. It provides functions such as keyword search, getting the latest papers by category, querying paper details, retrieving author papers, and supports generating paper summaries and comparison prompts to help users efficiently explore academic literature.

A Semantic Scholar API service implementation based on the FastMCP framework, providing functions such as academic paper search, author information query, citation analysis, etc., supporting batch operations and advanced searches.

The Crossref MCP Server is a bridge service that provides academic paper metadata search for AI assistants through the Model Context Protocol (MCP). It supports retrieving information on academic literatures, journals, and funding institutions in the Crossref database by DOI, title, or keywords.