academic-search-mcp 0.1.7

A MCP server for searching and downloading academic papers from multiple sources.

Academic Search MCP

A Model Context Protocol (MCP) server for searching and downloading academic papers from multiple sources. Designed for seamless integration with large language models like Claude Desktop.

Fork Notice: This is an extended fork of openags/academic-search-mcp with additional platforms (CORE, SSRN, CyberLeninka) and improvements.

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Table of Contents

• Overview
• Features
• Installation
  • Quick Start
    • Install Package
    • Configure Claude Desktop
  • For Development
    • Setup Environment
    • Install Dependencies
• Contributing
• Demo
• License
• TODO

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Overview

academic-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.

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Features

• Multi-Source Support: Search and download papers from arXiv, PubMed, bioRxiv, medRxiv, Google Scholar, IACR ePrint Archive, Semantic Scholar, CrossRef, OpenAlex, CORE, SSRN, and CyberLeninka.
• Date Filtering: All sources support date_from and date_to parameters (YYYY-MM-DD format) to filter papers by publication date.
• Citation Counts: OpenAlex, Semantic Scholar, CrossRef, and Google Scholar include citation counts in search results.
• Citation Graph: OpenAlex tools to explore references (papers a work cites) and citations (papers citing a work), sorted by impact.
• Open Access PDFs: OpenAlex includes PDF URLs for open access papers.
• Token-Optimized Output: Configurable abstract_limit parameter to control abstract length (default: 200 chars, use -1 for full, 0 to omit).
• Standardized Output: Papers are returned in a consistent, compact dictionary format via the Paper class.
• Asynchronous Tools: Efficiently handles network requests using httpx.
• MCP Integration: Compatible with MCP clients for LLM context enhancement.
• Extensible Design: Easily add new academic platforms by extending the academic_platforms module.

Search Parameters

All search tools support these common parameters:

Parameter	Type	Default	Description
query	str	required	Search query string
max_results	int	10	Maximum number of papers to return
abstract_limit	int	200	Max chars for abstract (0=omit, -1=full)
date_from	str	None	Start date in YYYY-MM-DD format
date_to	str	None	End date in YYYY-MM-DD format

Note: Google Scholar only supports year-level filtering (month/day are ignored).

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Installation

Quick Start with uvx

The easiest way to use academic-search-mcp:

uvx academic-search-mcp

Or install globally:

uv tool install academic-search-mcp

Configure Claude Desktop

Add this configuration to ~/Library/Application Support/Claude/claude_desktop_config.json (Mac) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "academic_search": {
      "command": "uvx",
      "args": ["academic-search-mcp"]
    }
  }
}

Alternative: Install with pip/uv

pip install academic-search-mcp
# or
uv add academic-search-mcp

2. Configure Claude Desktop (alternative): Add this configuration to ~/Library/Application Support/Claude/claude_desktop_config.json (Mac) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

   {
     "mcpServers": {
       "paper_search_server": {
         "command": "uv",
         "args": [
           "run",
           "--directory",
           "/path/to/your/academic-search-mcp",
           "-m",
           "paper_search_mcp.server"
         ],
         "env": {
           "SEMANTIC_SCHOLAR_API_KEY": "", // Optional: For enhanced Semantic Scholar features
           "CORE_API_KEY": "" // Optional: for CORE repository access
         }
       }
     }
   }
   

   Note: Replace /path/to/your/academic-search-mcp with your actual installation path.

For Development

For developers who want to modify the code or contribute:

1. Setup Environment:

   # Install uv if not installed
   curl -LsSf https://astral.sh/uv/install.sh | sh
   
   # Clone repository
   git clone https://github.com/openags/academic-search-mcp.git
   cd academic-search-mcp
   
   # Create and activate virtual environment
   uv venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
   

2. Install Dependencies:

   # Install project in editable mode
   uv add -e .
   
   # Add development dependencies (optional)
   uv add pytest flake8
   

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Contributing

We welcome contributions! Here's how to get started:

1. Fork the Repository: Click "Fork" on GitHub.

2. Clone and Set Up:

   git clone https://github.com/yourusername/academic-search-mcp.git
   cd academic-search-mcp
   pip install -e ".[dev]"  # Install dev dependencies (if added to pyproject.toml)
   

3. Make Changes:

   • Add new platforms in academic_platforms/.
   • Update tests in tests/.

4. Submit a Pull Request: Push changes and create a PR on GitHub.

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Demo

TODO

Planned Academic Platforms

• arXiv
• PubMed
• bioRxiv
• medRxiv
• Google Scholar
• IACR ePrint Archive
• Semantic Scholar
• CrossRef
• OpenAlex
• CORE (200M+ open access papers)
• SSRN (social sciences, law, business preprints)
• CyberLeninka (Russian academic papers, VAK/RSCI/SCOPUS filters)
• PubMed Central (PMC)
• Science Direct
• Springer Link
• IEEE Xplore
• ACM Digital Library
• Web of Science
• Scopus
• JSTOR
• ResearchGate

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License

This project is licensed under the MIT License. See the LICENSE file for details.

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Happy researching with academic-search-mcp!

Credits

Based on openags/paper-search-mcp by P.S Zhang.