Which integrations are available for this server?

Provides tools for searching, retrieving, analyzing, and exporting academic papers from arXiv.org, including paper search, author search, category browsing, related papers, citation analysis, trend analysis, and multi-format export.

How do I use arXiv MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@arXiv MCP Server search for recent papers in cs.AI" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

arXiv MCP Server Client

A comprehensive Model Context Protocol (MCP) implementation for interacting with arXiv.org, enabling AI assistants to search, retrieve, analyze, and export academic papers seamlessly.

🚀 Features

Core Functionality

Paper Search: Advanced search with keywords, authors, titles, and categories
Paper Retrieval: Get detailed metadata for specific arXiv papers
Smart Summaries: Generate formatted paper summaries with key information

Advanced Tools

Author Search: Find all papers by specific researchers
Category Browsing: Explore papers in specific arXiv categories (cs.AI, cs.LG, etc.)
Recent Papers: Get latest publications with customizable time ranges
Paper Comparison: Side-by-side analysis of multiple papers
Related Papers: Discover papers related to a given work using intelligent matching
Citation Analysis: Estimated citation metrics and impact analysis
Trend Analysis: Research trend analysis with publication counts, top authors, and keyword frequency
Multi-format Export: Export papers in BibTeX, JSON, CSV, and Markdown formats

📦 Installation

Using Poetry (Recommended)

# Clone the repository
git clone https://github.com/yourusername/arxiv-mcp-client.git
cd arxiv-mcp-client

# Install dependencies
poetry install

# Activate virtual environment
poetry shell

Using pip

# Install dependencies
pip install mcp httpx

# Run the client
python arxiv_mcp_client.py

🛠️ Usage

Basic Setup

import asyncio
from arxiv_mcp_client import ArxivMCPClient

async def main():
    # Initialize client
    client = ArxivMCPClient()
    
    # Connect to MCP server
    await client.connect("arxiv_mcp_server.py")
    
    # Your code here...
    
    # Close connection
    await client.close()

asyncio.run(main())

Search Examples

# General search
results = await client.search_papers("transformer neural networks", max_results=10)

# Search by author
papers = await client.search_by_author("Geoffrey Hinton", max_results=20)

# Search by category
ai_papers = await client.search_by_category("cs.AI", max_results=15)

# Get recent papers
recent = await client.get_recent_papers("cs.LG", days_back=7, max_results=10)

Paper Analysis

# Get paper details
paper = await client.get_paper_details("2301.07041")

# Get formatted summary
summary = await client.get_paper_summary("2301.07041")

# Compare papers
comparison = await client.compare_papers(
    ["2301.07041", "2302.13971"], 
    comparison_fields=["authors", "abstract", "categories"]
)

# Find related papers
related = await client.find_related_papers("2301.07041", max_results=10)

Export and Analysis

# Export to BibTeX
bibtex = await client.export_papers(
    ["2301.07041", "2302.13971"], 
    format="bibtex", 
    include_abstract=True
)

# Analyze research trends
trends = await client.analyze_trends(
    category="cs.AI", 
    time_period="3_months", 
    analysis_type="top_authors"
)

🔧 Available Tools

Tool	Description	Key Parameters
`search_arxiv`	General paper search	`query`, `max_results`, `sort_by`
`get_paper`	Get specific paper details	`arxiv_id`
`summarize_paper`	Generate formatted summary	`arxiv_id`
`search_by_author`	Find papers by author	`author_name`, `max_results`
`search_by_category`	Browse category papers	`category`, `max_results`, `sort_by`
`get_recent_papers`	Get latest publications	`category`, `days_back`, `max_results`
`compare_papers`	Compare multiple papers	`arxiv_ids`, `comparison_fields`
`find_related_papers`	Discover related work	`arxiv_id`, `max_results`
`get_paper_citations`	Citation analysis	`arxiv_id`
`analyze_trends`	Research trend analysis	`category`, `time_period`, `analysis_type`
`export_papers`	Multi-format export	`arxiv_ids`, `format`, `include_abstract`

📊 Supported Categories

Computer Science

cs.AI - Artificial Intelligence
cs.LG - Machine Learning
cs.CV - Computer Vision
cs.CL - Computation and Language
cs.CR - Cryptography and Security
cs.DB - Databases
cs.DS - Data Structures and Algorithms

Mathematics

math.CO - Combinatorics
math.ST - Statistics Theory
math.PR - Probability
math.OC - Optimization and Control

Physics

physics.comp-ph - Computational Physics
physics.data-an - Data Analysis
quant-ph - Quantum Physics

See full category list

📄 Export Formats

BibTeX

@article{2301_07041,
  title={Title of the Paper},
  author={Author One and Author Two},
  journal={arXiv preprint arXiv:2301.07041},
  year={2023},
  url={https://arxiv.org/abs/2301.07041}
}

JSON

{
  "id": "2301.07041",
  "title": "Title of the Paper",
  "authors": ["Author One", "Author Two"],
  "abstract": "Paper abstract...",
  "published": "2023-01-17",
  "categories": ["cs.AI", "cs.LG"]
}

CSV

id,title,authors,published,categories,url
2301.07041,Title of the Paper,"Author One; Author Two",2023-01-17,"cs.AI; cs.LG",https://arxiv.org/abs/2301.07041

🔍 Search Query Examples

Basic Searches

# Keyword search
await client.search_papers("neural networks")

# Multiple keywords
await client.search_papers("transformer attention mechanism")

# Exact phrase
await client.search_papers('"large language models"')

Advanced Searches

# Author search
await client.search_by_author("Yoshua Bengio")

# Category search  
await client.search_by_category("cs.AI")

# Recent papers in category
await client.get_recent_papers("cs.LG", days_back=14)

📈 Trend Analysis Types

Publication Count Analysis

Tracks the number of papers published over time in a specific category.

trends = await client.analyze_trends("cs.AI", "6_months", "publication_count")
# Returns: monthly publication counts

Top Authors Analysis

Identifies the most prolific authors in a field over a given period.

trends = await client.analyze_trends("cs.LG", "1_year", "top_authors")
# Returns: authors ranked by paper count

Keyword Frequency Analysis

Analyzes the most common keywords in paper titles within a category.

trends = await client.analyze_trends("cs.CV", "3_months", "keyword_frequency")
# Returns: keywords ranked by frequency

🚨 Error Handling

The client includes comprehensive error handling:

try:
    results = await client.search_papers("machine learning")
    if "error" in results:
        print(f"Search failed: {results['error']}")
    else:
        print(f"Found {len(results['papers'])} papers")
except Exception as e:
    print(f"Connection error: {e}")

🔧 Configuration

Rate Limiting

The client respects arXiv's API guidelines with built-in rate limiting and timeout handling.

Customization

# Custom timeout
client = ArxivMCPClient()
client.http_client = httpx.AsyncClient(timeout=60.0)

# Custom result limits
results = await client.search_papers("AI", max_results=50)  # Max: 100

🤝 Contributing

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

📝 License

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

🙏 Acknowledgments

arXiv.org for providing free access to academic papers
Anthropic for the Model Context Protocol
The open-source community for the underlying libraries

📞 Support

Issues: GitHub Issues
Documentation: Wiki
Discussions: GitHub Discussions

Made with ❤️ for the research community