Provides tools for searching academic papers on arXiv with automatic URL encoding and semantic similarity analysis using AI-powered embeddings to find the most relevant research papers
LitSynth MCP Server
A Model Context Protocol (MCP) server for intelligent academic paper discovery and semantic search using ArXiv. This server provides tools for searching academic papers and performing semantic similarity analysis using state-of-the-art sentence transformers.
Features
- ArXiv Search: Query ArXiv database with automatic URL encoding for complex search terms
- Semantic Search: Find papers most relevant to your research using AI-powered semantic similarity
- Robust Error Handling: Graceful handling of network issues and malformed data
- Flexible Input: Support for various query formats including spaces and special characters
Tools Available
1. greet(name: str)
Simple greeting function for testing server connectivity.
Parameters:
name
: String - Name to greet
Returns: Greeting message
2. search_query_arxiv(query: str, max_results: int = 5)
Search ArXiv database for academic papers matching your query.
Parameters:
query
: String - Search terms (automatically URL encoded)max_results
: Integer - Maximum number of results to return (default: 5)
Returns: Structured response with papers including:
- Title
- Authors
- Summary/Abstract
- ArXiv link
- Status message
Example:
3. search_semantic_arxiv(query: str, papers: list, top_k: int = 5)
Perform semantic search on a list of papers to find the most relevant ones.
Parameters:
query
: String - Research query for semantic matchingpapers
: List - Papers to search through (fromsearch_query_arxiv
or manual list)top_k
: Integer - Number of most relevant papers to return (default: 5)
Returns: Ranked papers with similarity scores including:
- Title
- Summary
- Authors
- ArXiv link
- Similarity score (0-1)
Example:
Installation
Prerequisites
- Python 3.8+
- pip
Setup
- Clone or download the project files
- Install dependencies:
- Run the MCP server:
Dependencies
The project requires the following packages (see requirements.txt
):
fastmcp>=0.1.0
- MCP frameworkfeedparser>=6.0.10
- RSS/Atom feed parsing for ArXiv APIrequests>=2.31.0
- HTTP requestssentence-transformers>=2.2.2
- Semantic search and embeddingstorch>=2.0.0
- PyTorch for neural networkstransformers>=4.21.0
- Hugging Face transformersnumpy>=1.21.0
- Numerical computing
Project Structure
Usage Examples
Basic ArXiv Search
Search for papers on a specific topic:
Semantic Paper Discovery
Find the most relevant papers from a search result:
Handling Complex Queries
The server automatically handles special characters and spaces:
Technical Details
Semantic Search Model
The server uses the sentence-transformers/all-MiniLM-L6-v2
model for semantic embeddings. This model:
- Provides 384-dimensional sentence embeddings
- Balances speed and accuracy
- Works well for academic text similarity
Error Handling
The server includes comprehensive error handling:
- URL Encoding: Automatic handling of spaces and special characters
- Network Errors: Graceful degradation when ArXiv is unavailable
- Data Validation: Safe handling of missing or malformed paper data
- Empty Results: Informative messages when no papers are found
Response Format
All functions return structured responses:
Troubleshooting
Common Issues
"URL can't contain control characters" error:
- This is fixed in the current version with automatic URL encoding
- Make sure you're using the latest version of the server
"No papers found" result:
- Check your query spelling
- Try broader search terms
- Verify ArXiv service availability
Slow semantic search:
- First run downloads the transformer model (~90MB)
- Subsequent runs are much faster
- Consider reducing
top_k
for faster results
Memory issues:
- The sentence transformer model requires ~500MB RAM
- Reduce batch sizes if experiencing memory problems
Contributing
Feel free to submit issues, feature requests, or pull requests to improve the AI Research Assistant.
License
This project is open source. Please check individual dependency licenses for commercial use.
Acknowledgments
- ArXiv for providing free access to academic papers
- Sentence Transformers for semantic search capabilities
- FastMCP for the MCP server framework
This server cannot be installed
remote-capable server
The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.
Enables intelligent academic paper discovery through ArXiv search and AI-powered semantic similarity analysis. Helps researchers find and rank the most relevant papers using natural language queries and state-of-the-art sentence transformers.