paper-search-mcp-openai-v2
MCP ServerFreeFind and download academic papers from leading sources like arXiv, PubMed, bioRxiv, medRxiv, Google Scholar, Semantic Scholar, CrossRef, and IACR. Get standardized results and fetch full-text PDFs when available. Accelerate literature reviews with deep search and effortless retrieval.
Capabilities3 decomposed
multi-source academic paper retrieval
Medium confidenceThis capability enables users to search for academic papers across multiple leading sources like arXiv, PubMed, and Google Scholar. It employs a unified query interface that standardizes results from diverse databases, allowing for seamless integration and retrieval of full-text PDFs when available. The architecture leverages API calls to each source, aggregating and normalizing the data for consistent output, which enhances the user experience during literature reviews.
Utilizes a model-context-protocol (MCP) to streamline interactions with multiple academic databases, ensuring a cohesive search experience.
More comprehensive than single-source search tools because it aggregates results from multiple databases in real-time.
standardized result formatting
Medium confidenceThis capability formats search results into a standardized structure, making it easier for users to parse and utilize the information. It employs a consistent schema for metadata across different sources, ensuring that fields like title, authors, and publication date are uniformly presented. This design choice enhances usability and allows for easier integration with other tools or workflows.
Implements a custom schema for result formatting that is adaptable to various academic sources, ensuring that users receive a coherent view of their search results.
Provides a more uniform output than typical search APIs, which often return results in varying formats.
full-text pdf fetching
Medium confidenceThis capability allows users to retrieve full-text PDFs of academic papers when available by directly accessing the hosting sources' APIs. It intelligently checks for the presence of a PDF link in the search results and initiates a download if accessible. This implementation reduces the need for manual searching and enhances the efficiency of obtaining necessary documents.
Integrates direct PDF fetching capabilities with a focus on seamless user experience, reducing the friction of accessing full-text articles.
More efficient than manual searches as it automates the retrieval process, saving time for users.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
Related Artifactssharing capabilities
Artifacts that share capabilities with paper-search-mcp-openai-v2, ranked by overlap. Discovered automatically through the match graph.
Paper Search
Search and download academic papers from arXiv, PubMed, bioRxiv, medRxiv, Google Scholar, Semantic Scholar, and IACR. Fetch PDFs and extract full text to accelerate literature reviews. Get consistent metadata for easier filtering, citation, and analysis.
AI Research Assistant
The server provides immediate access to millions of academic papers through Semantic Scholar and arXiv, enabling AI-powered research with comprehensive search, citation analysis, and full-text PDF extraction from multiple sources (arXiv and Wiley open-access). - No API key is required.
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Best For
- ✓researchers conducting literature reviews
- ✓students gathering academic resources
- ✓developers building academic search tools
- ✓developers integrating academic search into applications
- ✓research teams needing consistent data formats
- ✓data analysts working with academic literature
- ✓students needing quick access to research papers
- ✓researchers compiling resources for studies
Known Limitations
- ⚠Dependent on the availability of APIs from academic sources; if an API is down, retrieval fails.
- ⚠Limited to sources that provide public access to full-text PDFs.
- ⚠Standardization is limited to the fields provided by the source APIs; some unique fields may be lost.
- ⚠Requires additional processing to convert results into desired formats.
- ⚠Fetching is contingent on the availability of PDFs from the source; not all papers will have accessible PDFs.
- ⚠May encounter rate limits imposed by source APIs.
Requirements
Input / Output
UnfragileRank
UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.
Repository Details
About
Find and download academic papers from leading sources like arXiv, PubMed, bioRxiv, medRxiv, Google Scholar, Semantic Scholar, CrossRef, and IACR. Get standardized results and fetch full-text PDFs when available. Accelerate literature reviews with deep search and effortless retrieval.
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