About
Paper Search MCP Server enables AI coding assistants to search and download academic papers from multiple scholarly sources including arXiv, PubMed, bioRxiv, and more. Key features: - Unified multi-source search across academic platforms with concurrent queries and automatic deduplication of results. - Intelligent PDF downloading with fallback mechanisms through publisher open access links. - Two-layer architecture combining high-level unified tooling with modular platform-specific connectors. - Free-first strategy prioritizing open and public data sources, with optional API keys for improved stability or coverage when needed. - LLM-friendly standardized output designed for downstream AI research workflows. - Source transparency that clearly indicates coverage gaps and retrieval limitations for each academic database.
README
Paper Search MCP
A Model Context Protocol (MCP) server for searching and downloading academic papers from multiple sources. The project follows a free-first strategy: prioritize open and public data sources, support optional API keys when they improve stability or coverage, and keep source-specific connectors extensible for advanced users.
[](https://smithery.ai/server/@openags/paper-search-mcp)
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Table of Contents
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Overview
paper-search-mcp is a Python-based MCP server that enables users to search and download academic papers from various platforms. It provides tools for searching papers (e.g., search_arxiv) and downloading PDFs (e.g., download_arxiv), making it ideal for researchers and AI-driven workflows. Built with the MCP Python SDK, it integrates seamlessly with LLM clients like Claude Desktop.
Project Principles
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Features
search_papers for multi-source concurrent search & deduplication, and download_with_fallback relying on publisher open access links with sequential fallbacks.
- Layer 2 (Platform Connectors): Modular connectors for specific academic platforms (arXiv, PubMed, bioRxiv, Semantic Scholar, etc.) equipped with intelligent DOI extraction via regex text analysis or API fields.
Paper class.download_with_fallback now follows source-native download → OpenAIRE/CORE/Europe PMC/PMC discovery → Unpaywall DOI resolution → optional Sci-Hub.academic_platforms module.Source Strategy
The long-term goal is not to depend on a single search engine, but to combine multiple free and public sources with clear roles:
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