What is Authorprofilemcp

AuthorProfileMCP is a Model Context Protocol (MCP) server designed for analyzing academic author networks and research collaborations.

Use cases

Use cases include identifying co-authors for a researcher, extracting relevant research keywords for publication analysis, and studying collaboration patterns within academic networks.

How to use

To use AuthorProfileMCP, clone the repository, create a virtual environment, install dependencies, and run the server using ‘python server.py’. You can then call functions like ‘get_coauthors’ and ‘get_author_keywords’ to retrieve data.

Key features

Key features include finding co-authors, extracting research keywords from Google Scholar, rate limiting, caching, error handling, data merging from multiple sources, and asynchronous operations for improved performance.

Where to use

AuthorProfileMCP can be used in academic research, bibliometrics, and any field that requires analysis of author collaboration and research interests.

Academic Author Network MCP Server

A Model Context Protocol (MCP) server for analyzing academic author networks and research collaborations.

Features

• get_coauthors: Find all co-authors for a given researcher
• get_author_keywords: Extract research keywords from Google Scholar profile

Installation

1. Clone or download this repository
2. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   

3. Install dependencies:

   pip install -r requirements.txt
   

Usage

Running the Server

python server.py

Example Tool Calls

Finding Co-authors

result = await get_coauthors(
    name="Yann", 
    surname="LeCun",
    institution="NYU"  # Optional
)

Getting Research Keywords from Google Scholar

keywords = await get_author_keywords(
    name="Yann",
    surname="LeCun"
)

Data Sources

The server uses:

• Semantic Scholar API: Primary source for author and publication data
• OpenAlex API: Open academic knowledge graph
• Crossref API: DOI resolution and metadata
• Google Scholar: Web scraping for research interests and keywords

Features

• Rate Limiting: Respects API rate limits and includes delays for web scraping
• Caching: Reduces redundant API calls and scraping requests
• Error Handling: Graceful handling of API failures and scraping issues
• Data Merging: Combines data from multiple sources for co-authors
• Async Operations: Parallel API requests for better performance

Configuration

The server includes built-in rate limiting and error handling. No additional configuration is required for basic usage.

Limitations

• Free tier API limits apply
• Google Scholar scraping includes respectful delays
• Results quality depends on author name uniqueness
• Web scraping may occasionally fail due to anti-bot measures

Contributing

Contributions are welcome! Please ensure all API integrations respect rate limits and terms of service.