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ArXiv MCP Server

🔍 Enable AI assistants to search and access arXiv papers through a simple MCP interface.

The ArXiv MCP Server provides a bridge between AI assistants and arXiv's research repository through the Model Context Protocol (MCP). It allows AI models to search for papers and access their content in a programmatic way.

🤝 Contribute • 📝 Report Bug

✨ Core Features

🔎 Paper Search: Query arXiv papers with filters for date ranges and categories
📄 Paper Access: Download and read paper content
📋 Paper Listing: View all downloaded papers
🗃️ Local Storage: Papers are saved locally for faster access
📝 Prompts: A Set of Research Prompts

Related MCP server: ArXiv MCP Server

🚀 Quick Start

Installing via Smithery

To install ArXiv Server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install arxiv-mcp-server --client claude

Installing Manually

Install using uv:

uv tool install arxiv-mcp-server

For development:

# Clone and set up development environment git clone https://github.com/blazickjp/arxiv-mcp-server.git cd arxiv-mcp-server # Create and activate virtual environment uv venv source .venv/bin/activate # Install with test dependencies uv pip install -e ".[test]"

🔌 MCP Integration

Add this configuration to your MCP client config file:

{ "mcpServers": { "arxiv-mcp-server": { "command": "uv", "args": [ "tool", "run", "arxiv-mcp-server", "--storage-path", "/path/to/paper/storage" ] } } }

For Development:

{ "mcpServers": { "arxiv-mcp-server": { "command": "uv", "args": [ "--directory", "path/to/cloned/arxiv-mcp-server", "run", "arxiv-mcp-server", "--storage-path", "/path/to/paper/storage" ] } } }

💡 Available Tools

The server provides four main tools:

1. Paper Search

Search for papers with optional filters:

result = await call_tool("search_papers", { "query": "transformer architecture", "max_results": 10, "date_from": "2023-01-01", "categories": ["cs.AI", "cs.LG"] })

2. Paper Download

Download a paper by its arXiv ID:

result = await call_tool("download_paper", { "paper_id": "2401.12345" })

3. List Papers

View all downloaded papers:

result = await call_tool("list_papers", {})

4. Read Paper

Access the content of a downloaded paper:

result = await call_tool("read_paper", { "paper_id": "2401.12345" })

📝 Research Prompts

The server offers specialized prompts to help analyze academic papers:

Paper Analysis Prompt

A comprehensive workflow for analyzing academic papers that only requires a paper ID:

result = await call_prompt("deep-paper-analysis", { "paper_id": "2401.12345" })

This prompt includes:

Detailed instructions for using available tools (list_papers, download_paper, read_paper, search_papers)
A systematic workflow for paper analysis
Comprehensive analysis structure covering:
- Executive summary
- Research context
- Methodology analysis
- Results evaluation
- Practical and theoretical implications
- Future research directions
- Broader impacts

⚙️ Configuration

Configure through environment variables:

Variable	Purpose	Default
`ARXIV_STORAGE_PATH`	Paper storage location	~/.arxiv-mcp-server/papers

🧪 Testing

Run the test suite:

python -m pytest

📄 License

Released under the MIT License. See the LICENSE file for details.

Made with ❤️ by the Pearl Labs Team

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security – no known vulnerabilities

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license - permissive license

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quality - confirmed to work

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Related Servers

ArXiv MCP Server

ArXiv MCP Server

✨ Core Features

🚀 Quick Start

Installing via Smithery

Installing Manually

🔌 MCP Integration

💡 Available Tools

1. Paper Search

2. Paper Download

3. List Papers

4. Read Paper

📝 Research Prompts

Paper Analysis Prompt

⚙️ Configuration

🧪 Testing

📄 License

Resources

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