gauravfs-14/lit-mcp

Any MCP-compatible client can use lit-mcp with the same configuration pattern:

{
  "mcpServers": {
    "lit-mcp": {
      "command": "uvx",
      "args": ["lit-mcp"]
    }
  }
}

Example Usage:

Once configured, you can use the available tools in your MCP client:

# Search tools
Search for 5 papers on "machine learning transformers" using arXiv.
Search for computer science papers on "GPS trajectory" using DBLP.

# AI-powered prompts (as "/" commands in Cursor)
/latest_info small language models
/related_topics transformer architectures  
/author_spotlight computer vision

Search for academic papers on arXiv with advanced query capabilities.

Parameters:

• query (string): Search query (supports arXiv syntax like au:Author_Name, ti:Title, etc.)
• max_results (integer, optional): Maximum number of results (default: 10)

Returns:

• List of paper objects with title, authors, publication date, summary, PDF URL, categories, and DOI

Example Queries:

# Search by author
"au:Gaurab_Chhetri"

# Search by title keywords
"ti:machine learning"

# Search by category
"cat:cs.AI"

# Combined search
"au:Chhetri AND ti:transport"

Search for computer science publications in the DBLP database.

Parameters:

• query (string): Search query for computer science papers
• max_results (integer, optional): Maximum number of results (default: 10)

Returns:

• List of publication objects with title, authors, venue, volume, number, pages, publisher, year, type, access, key, DOI, electronic edition link, and DBLP URL

Example Queries:

# Search for specific topics
"machine learning"
"computer vision"
"natural language processing"
"GPS trajectory"
"blockchain technology"

Generate comprehensive summaries of the most recent innovations, trends, and papers in a research field.

Parameters:

• topic (string): Research field or topic to analyze

Returns:

• Well-structured Markdown document with recent papers, key trends, and insights

Features:

• Identifies latest papers (preferably within last 12 months)
• Focuses on highly cited, emerging, or novel works
• Provides structured summaries with PDF links
• Includes "Key Trends & Insights" section
• Beautifully formatted for easy reading

Example Usage:

# As MCP prompt
Generate latest information about "small language models"
Analyze recent trends in "quantum machine learning"

# As "/" command in Cursor
/latest_info small language models
/latest_info quantum machine learning

Discover related and emerging research areas connected to a given topic.

Parameters:

• topic (string): Research topic to explore connections for

Returns:

• Structured Markdown document with related topics, representative papers, and emerging intersections

Features:

• Identifies 3-6 distinct related topics or subfields
• Shows connections between topics
• Provides representative papers with summaries
• Highlights emerging interdisciplinary areas
• Reveals novel applications and fusion trends

Example Usage:

# As MCP prompt
Find related topics for "transformer architectures"
Explore connections around "federated learning"

# As "/" command in Cursor
/related_topics transformer architectures
/related_topics federated learning

Identify leading authors, labs, and research groups advancing innovation in a field.

Parameters:

• topic (string): Research field to analyze for key contributors

Returns:

• Structured Markdown document with top authors, their affiliations, notable papers, and collaborative networks

Features:

• Ranks authors by publication frequency and impact
• Shows affiliations and research themes
• Lists notable papers with summaries
• Identifies collaborative networks and research groups
• Highlights cross-institution projects

Example Usage:

# As MCP prompt
Find leading authors in "computer vision"
Identify key researchers in "natural language processing"

# As "/" command in Cursor
/author_spotlight computer vision
/author_spotlight natural language processing

1. Clone the repository

   git clone https://github.com/gauravfs-14/lit-mcp.git
   cd lit-mcp
   

2. Install dependencies

   # Install UV if not already installed
   curl -LsSf https://astral.sh/uv/install.sh | sh
   
   # Install project dependencies
   uv sync
   

3. Run the MCP server

   uv run lit-mcp
   

Development Setup for MCP Clients

If you're developing locally, you can use the development setup:

{
  "mcpServers": {
    "lit-mcp": {
      "command": "uv",
      "args": [
        "--directory",
        "<absolute_path_to_the_cloned_repo>",
        "run",
        "lit-mcp"
      ]
    }
  }
}

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Run tests (uv run python tests/test_basic.py)
5. Commit your changes (git commit -m 'Add amazing feature')
6. Push to the branch (git push origin feature/amazing-feature)
7. Open a Pull Request

New contributors can help with:

• Adding new academic database integrations (PubMed, IEEE Xplore, ACM Digital Library)
• Creating new AI-powered research prompts
• Improving existing prompt templates
• Adding new evaluation metrics and benchmarks
• Enhancing documentation and examples

For detailed guidelines, see .