A MCP server for searching and downloading academic papers from multiple sources.
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# 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) --- ## Table of Contents - [Overview](#overview) - [Project Principles](#project-principles) - [Features](#features) - [Source Strategy](#source-strategy) - [Sci-Hub Notice](#sci-hub-notice) - [Installation](#installation) - [Method 1 — Smithery](#method-1--smithery-one-command-recommended-for-claude-desktop) - [Method 2 — uvx](#method-2--uvx-no-install-always-latest) - [Method 3 — uv](#method-3--uv-persistent-install) - [Method 4 — pip](#method-4--pip-standard-python-install) - [Method 5 — npx](#method-5--npx-via-smithery-cli-no-local-python-needed) - [Method 6 — Docker](#method-6--docker) - [Method 7 — Clone & run from source](#method-7--clone--run-from-source-development--recommended-for-macos-local) - [Environment Variables](#environment-variables-env-file) - [Contributing](#contributing) - [Demo](#demo) - [Star History](#star-history) - [License](#license) - [TODO](#todo) --- ## 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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