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.

✨ 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

🚀 Quick Start#

Installing via Smithery#

To install ArXiv Server for Claude Desktop automatically via Smithery:

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

Installing Manually#

Install using uv:

bash
uv tool install arxiv-mcp-server

For development:

bash
# 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:

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

For Development:

json
{
    "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:

python
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:

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

3. List Papers#

View all downloaded papers:

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

4. Read Paper#

Access the content of a downloaded paper:

python
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:

python
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:

🧪 Testing#

Run the test suite:

bash
python -m pytest

📄 License#

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

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