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:
search_query_arxiv("multimodal agents", 3)
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_arxivor 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:
papers = search_query_arxiv("machine learning")
relevant = search_semantic_arxiv("deep reinforcement learning", papers, 3)
4. search_hf_datasets(query: str, limit: int = 5)
Perform a research on Hugging Face to find the most relevant datasets on a topics
Parameters:
query: String - Research query for matching datasetslimit: Integer - Number of the limit of datasets showed
Returns: Ranked datasets info
- Id
- Description
- Url
search_hf_datasets("biology", 5)
5. get_dataset_details(dataset_id: str)
Get detailed information about a specific Hugging Face dataset. It provides comprehensive analysis including structure, usage examples, and research applications.
Parameters:
dataset_id: String - The dataset ID from Hugging Face (e.g., "microsoft/COCO")
Returns: Detailed dataset information including metadata, structure, and usage examples
- Dataset Id
- Url
- Basic Info
- Structure
- Files
- Usage Information
- Research Applications
6. explore_dataset_files(dataset_id: str)
Explore the structure of a Hugging Face dataset without downloading it entirely. Shows available files/splits and provides a sample preview of the data.
Parameters:
dataset_id: String - The dataset ID from Hugging Face (e.g., "microsoft/COCO")
Returns: Dataset structure information including files, splits, and sample data
- Dataset Id
- Url
- Structure
- Available Files
- Sample Preview
- Summary
7. explore_dataset_structure(dataset_id: str, split_name: str = None)
Explore the structure of a Hugging Face dataset with detailed split information.
Parameters:
dataset_id: String - The dataset ID from Hugging Face (e.g., "microsoft/COCO")split_name: Optional specific split to explore (e.g., "train", "test", "validation")
Returns: Dataset structure overview with splits, file types, and sample data
- Dataset Id
- Url
- Overview
- Available Splits
- File Types
- Sample Data
- Description
- Next Steps
Installation
Prerequisites
- Python 3.8+
- pip
Setup
- Clone or download the project files
git clone https://github.com/RayaneChCh-dev/LitSynth-MCP-Server.git
- Install dependencies:
pip install -r requirements.txt
- Run the MCP server:
fastmcp run my_server.py:mcp --transport http --port <your_port>
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
ai-research-assistant/
├── my_server.py # Main MCP server implementation
├── requirements.txt # Python dependencies
└── README.md # This file
Usage Examples
Basic ArXiv Search
Search for papers on a specific topic:
# Search for papers about transformers
results = search_query_arxiv("attention mechanisms transformers", 5)
Semantic Paper Discovery
Find the most relevant papers from a search result:
# First, get papers on a broad topic
papers = search_query_arxiv("artificial intelligence", 20)
# Then find the most relevant ones for your specific research
relevant_papers = search_semantic_arxiv("graph neural networks", papers, 5)
Handling Complex Queries
The server automatically handles special characters and spaces:
# These work automatically without manual encoding
search_query_arxiv("machine learning & deep learning: survey")
search_query_arxiv("reinforcement learning (RL) applications")
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:
{
"message": "Status or info message",
"results": [
{
"title": "Paper Title",
"author": ["Author 1", "Author 2"],
"summary": "Abstract text...",
"link": "https://arxiv.org/abs/...",
"similarity_score": 0.85 // Only in semantic search
}
]
}
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_kfor 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