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

🚀 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

license - permissive license

quality - confirmed to work

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

Tools

The ArXiv MCP Server bridges the gap between AI models and academic research by providing a sophisticated interface to arXiv's extensive research repository. This server enables AI assistants to perform precise paper searches and access full paper content, enhancing their ability to engage with scientific literature.

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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

Tools

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