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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 Message Control Protocol (MCP). It allows AI models to search for papers and access their content in a programmatic way.
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🤝 **[Contribute](https://github.com/blazickjp/arxiv-mcp-server/blob/main/CONTRIBUTING.md)** •
📝 **[Report Bug](https://github.com/blazickjp/arxiv-mcp-server/issues)**
</div>
## ✨ 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](https://smithery.ai/server/arxiv-mcp-server):
```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:
| Variable | Purpose | Default |
|----------|---------|---------|
| `ARXIV_STORAGE_PATH` | Paper storage location | ~/.arxiv-mcp-server/papers |
## 🧪 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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Made with ❤️ by the Pearl Labs Team
<a href="https://glama.ai/mcp/servers/04dtxi5i5n"><img width="380" height="200" src="https://glama.ai/mcp/servers/04dtxi5i5n/badge" alt="ArXiv Server MCP server" /></a>
</div>
