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 matching

• papers: List - Papers to search through (from search_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:

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 datasets

• limit: Integer - Number of the limit of datasets showed

Returns: Ranked datasets info

• Id

• Description

• Url

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

1. Clone or download the project files

2. Install dependencies:

1. Run the MCP server:

Dependencies

The project requires the following packages (see requirements.txt):

• fastmcp>=0.1.0 - MCP framework

• feedparser>=6.0.10 - RSS/Atom feed parsing for ArXiv API

• requests>=2.31.0 - HTTP requests

• sentence-transformers>=2.2.2 - Semantic search and embeddings

• torch>=2.0.0 - PyTorch for neural networks

• transformers>=4.21.0 - Hugging Face transformers

• numpy>=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