TL;DR
Paper Search is an open-source Model Context Protocol (MCP) server that enables AI agents to search, fetch, and extract full text from major academic repositories like arXiv, PubMed, and Google Scholar. It is designed for researchers and developers who want to ground AI-driven literature reviews in verified scientific data rather than model training data.
What Users Actually Pay
No user-reported pricing yet.
Our Take
Paper Search occupies a unique position at the intersection of academic research and the burgeoning 'AI Agent' ecosystem. By utilizing the Model Context Protocol (MCP), it bridges the gap between Large Language Models (LLMs) and disparate academic databases, effectively turning a standard AI chat interface into a powerful research assistant. Its primary value proposition is the elimination of 'citation hallucination' by providing a direct pipeline for real-time metadata and PDF extraction. The tool's greatest strength lies in its aggregator approach. Rather than forcing researchers to toggle between bioRxiv, PubMed, and Semantic Scholar, Paper Search provides a unified interface with consistent metadata formats. This consistency is vital for building reliable RAG (Retrieval-Augmented Generation) pipelines where data cleanliness is as important as the data itself. For users of Claude Desktop or other MCP-compatible clients, it offers a seamless way to inject peer-reviewed evidence into a conversation. However, potential users should be aware that this is a developer-centric tool rather than a consumer SaaS product. There is no standalone web interface; installation requires a local environment setup and familiarity with JSON configurations. Because it relies on external APIs like Semantic Scholar and Google Scholar, performance and availability are subject to those providers' rate limits and terms of service, which can occasionally lead to throttled results during high-volume searches. Paper Search is best suited for AI-savvy academics, data scientists, and developers who are building custom research workflows. It is an excellent choice for those who find commercial tools like Elicit or Perplexity too restrictive and prefer a free, modular, and open-source solution that they can control and integrate into their existing local AI setups.
Similar Products
Pros
- + Unifies search across high-impact repositories including arXiv, PubMed, bioRxiv, and Google Scholar.
- + Automates the tedious process of downloading PDFs and extracting clean text for AI analysis.
- + Standardizes metadata across sources, making it easier to generate citations and filter results.
- + Leverages the Model Context Protocol (MCP) for native integration with modern LLM desktop clients.
- + Entirely free and open-source, allowing for full transparency and local data handling.
Cons
- - Requires technical proficiency to install and configure via terminal/JSON files.
- - Lacks a dedicated graphical user interface (GUI) for non-technical users.
- - Subject to API rate limits and potential breakages if source websites change their structure.
- - Minimal documentation compared to established commercial research platforms.
- - Requires the user to manage their own API keys for certain sources to ensure full functionality.
Sentiment Analysis
No reviews or mentions found for the software product 'Paper Search' (GitHub repo adamamer20/paper-search-mcp-openai) across G2, Capterra, TrustRadius, Reddit, or X (Twitter). The product is a very new, niche open-source MCP server with minimal visibility (11 GitHub stars, no issues/discussions), resulting in zero user feedback on these platforms.
Sentiment Over Time
Agent Readiness
16/100Paper Search MCP OpenAI is a local MCP server for AI agents to search and download academic papers from sources like arXiv, PubMed, etc., integrating well with Claude Desktop and OpenAI tools via the MCP protocol. However, lacking a public API, no-code platform integrations (Zapier/etc.), comprehensive docs, or dev tools like sandbox/versioning limits its readiness for broad autonomous agent usage beyond custom LLM setups.
Last checked Mar 23, 2026
MCP Integrations
1 server25 tools20,255 total usesSearch 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.
25 tools
searchDeep Research compatible search tool aggregating across sources.fetchFetch full document content for a search result.search_arxivSearch academic papers from arXiv. Args: query: Search query string (e.g., 'machine learning'). max_results: Maximum number of papers to return (default: 10). Returns: List of paper metadata in dictionary format.search_pubmedSearch academic papers from PubMed. Args: query: Search query string (e.g., 'machine learning'). max_results: Maximum number of papers to return (default: 10). Returns: List of paper metadata in dictionary format.search_biorxivSearch academic papers from bioRxiv. Args: query: Search query string (e.g., 'machine learning'). max_results: Maximum number of papers to return (default: 10). Returns: List of paper metadata in dictionary format.search_medrxivSearch academic papers from medRxiv. Args: query: Search query string (e.g., 'machine learning'). max_results: Maximum number of papers to return (default: 10). Returns: List of paper metadata in dictionary format.search_google_scholarSearch academic papers from Google Scholar. Args: query: Search query string (e.g., 'machine learning'). max_results: Maximum number of papers to return (default: 10). Returns: List of paper metadata in dictionary format.search_iacrSearch academic papers from IACR ePrint Archive. Args: query: Search query string (e.g., 'cryptography', 'secret sharing'). max_results: Maximum number of papers to return (default: 10). fetch_details: Whether to fetch detailed information for each paper (default: True). Returns: List of paper metadata in dictionary format.download_arxivDownload PDF of an arXiv paper. Args: paper_id: arXiv paper ID (e.g., '2106.12345'). save_path: Directory to save the PDF (default: './downloads'). Returns: Path to the downloaded PDF file.download_pubmedAttempt to download PDF of a PubMed paper. Args: paper_id: PubMed ID (PMID). save_path: Directory to save the PDF (default: './downloads'). Returns: str: Message indicating that direct PDF download is not supported.download_biorxivDownload PDF of a bioRxiv paper. Args: paper_id: bioRxiv DOI. save_path: Directory to save the PDF (default: './downloads'). Returns: Path to the downloaded PDF file.download_medrxivDownload PDF of a medRxiv paper. Args: paper_id: medRxiv DOI. save_path: Directory to save the PDF (default: './downloads'). Returns: Path to the downloaded PDF file.download_iacrDownload PDF of an IACR ePrint paper. Args: paper_id: IACR paper ID (e.g., '2009/101'). save_path: Directory to save the PDF (default: './downloads'). Returns: Path to the downloaded PDF file.read_arxiv_paperRead and extract text content from an arXiv paper PDF. Args: paper_id: arXiv paper ID (e.g., '2106.12345'). save_path: Directory where the PDF is/will be saved (default: './downloads'). Returns: str: The extracted text content of the paper.read_pubmed_paperRead and extract text content from a PubMed paper. Args: paper_id: PubMed ID (PMID). save_path: Directory where the PDF would be saved (unused). Returns: str: Message indicating that direct paper reading is not supported.read_biorxiv_paperRead and extract text content from a bioRxiv paper PDF. Args: paper_id: bioRxiv DOI. save_path: Directory where the PDF is/will be saved (default: './downloads'). Returns: str: The extracted text content of the paper.read_medrxiv_paperRead and extract text content from a medRxiv paper PDF. Args: paper_id: medRxiv DOI. save_path: Directory where the PDF is/will be saved (default: './downloads'). Returns: str: The extracted text content of the paper.read_iacr_paperRead and extract text content from an IACR ePrint paper PDF. Args: paper_id: IACR paper ID (e.g., '2009/101'). save_path: Directory where the PDF is/will be saved (default: './downloads'). Returns: str: The extracted text content of the paper.search_semanticSearch academic papers from Semantic Scholar. Args: query: Search query string (e.g., 'machine learning'). year: Optional year filter (e.g., '2019', '2016-2020', '2010-', '-2015'). max_results: Maximum number of papers to return (default: 10). Returns: List of paper metadata in dictionary format.download_semanticDownload PDF of a Semantic Scholar paper. Args: paper_id: Semantic Scholar paper ID, Paper identifier in one of the following formats: - Semantic Scholar ID (e.g., "649def34f8be52c8b66281af98ae884c09aef38b") - DOI:<doi> (e.g., "DOI:10.18653/v1/N18-3011") - ARXIV:<id> (e.g., "ARXIV:2106.15928") - MAG:<id> (e.g., "MAG:112218234") - ACL:<id> (e.g., "ACL:W12-3903") - PMID:<id> (e.g., "PMID:19872477") - PMCID:<id> (e.g., "PMCID:2323736") - URL:<url> (e.g., "URL:https://arxiv.org/abs/2106.15928v1") save_path: Directory to save the PDF (default: './downloads'). Returns: Path to the downloaded PDF file.read_semantic_paperRead and extract text content from a Semantic Scholar paper. Args: paper_id: Semantic Scholar paper ID, Paper identifier in one of the following formats: - Semantic Scholar ID (e.g., "649def34f8be52c8b66281af98ae884c09aef38b") - DOI:<doi> (e.g., "DOI:10.18653/v1/N18-3011") - ARXIV:<id> (e.g., "ARXIV:2106.15928") - MAG:<id> (e.g., "MAG:112218234") - ACL:<id> (e.g., "ACL:W12-3903") - PMID:<id> (e.g., "PMID:19872477") - PMCID:<id> (e.g., "PMCID:2323736") - URL:<url> (e.g., "URL:https://arxiv.org/abs/2106.15928v1") save_path: Directory where the PDF is/will be saved (default: './downloads'). Returns: str: The extracted text content of the paper.search_crossrefSearch academic papers from CrossRef database. CrossRef is a scholarly infrastructure organization that provides persistent identifiers (DOIs) for scholarly content and metadata. It's one of the largest citation databases covering millions of academic papers, journals, books, and other scholarly content. Args: query: Search query string (e.g., 'machine learning', 'climate change'). max_results: Maximum number of papers to return (default: 10, max: 1000). **kwargs: Additional search parameters: - filter: CrossRef filter string (e.g., 'has-full-text:true,from-pub-date:2020') - sort: Sort field ('relevance', 'published', 'updated', 'deposited', etc.) - order: Sort order ('asc' or 'desc') Returns: List of paper metadata in dictionary format. Examples: # Basic search search_crossref("deep learning", 20) # Search with filters search_crossref("climate change", 10, filter="from-pub-date:2020,has-full-text:true") # Search sorted by publication date search_crossref("neural networks", 15, sort="published", order="desc")get_crossref_paper_by_doiGet a specific paper from CrossRef by its DOI. Args: doi: Digital Object Identifier (e.g., '10.1038/nature12373'). Returns: Paper metadata in dictionary format, or empty dict if not found. Example: get_crossref_paper_by_doi("10.1038/nature12373")download_crossrefAttempt to download PDF of a CrossRef paper. Args: paper_id: CrossRef DOI (e.g., '10.1038/nature12373'). save_path: Directory to save the PDF (default: './downloads'). Returns: str: Message indicating that direct PDF download is not supported. Note: CrossRef is a citation database and doesn't provide direct PDF downloads. Use the DOI to access the paper through the publisher's website.read_crossref_paperAttempt to read and extract text content from a CrossRef paper. Args: paper_id: CrossRef DOI (e.g., '10.1038/nature12373'). save_path: Directory where the PDF is/will be saved (default: './downloads'). Returns: str: Message indicating that direct paper reading is not supported. Note: CrossRef is a citation database and doesn't provide direct paper content. Use the DOI to access the paper through the publisher's website.
Last checked Apr 27, 2026
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