home / mcp / academic author network mcp server

Academic Author Network MCP Server

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

Installation

Add the following to your MCP client configuration file.

Configuration

{
  "mcpServers": {
    "alperenkocyigit-authorprofilemcp": {
      "command": "python",
      "args": [
        "server.py"
      ]
    }
  }
}

You can run the Academic Author Network MCP Server to analyze academic author collaborations and extract research keywords from profiles. It consolidates data from multiple sources, supports asynchronous requests, and provides easy-to-call endpoints for co-author discovery and keyword extraction.

How to use

You interact with the MCP Server through an MCP client. Use the available tools to retrieve an author’s co-authors and to extract research keywords from profiles. The server handles data from multiple sources, merges results, and returns structured insights that you can use for analysis or reporting.

To find co-authors for a researcher, provide the researcher’s first name and last name, and optionally the institution to narrow results. To fetch keywords, supply the researcher’s name. The server optimizes performance by running data fetches in parallel and caching results to reduce repeated calls.

How to install

Prerequisites: you need Python 3.8+ installed on your system.

Create a virtual environment and activate it.

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

Install the required dependencies.

pip install -r requirements.txt

Run the MCP Server locally.

python server.py

Configuration, data sources, and usage notes

The MCP Server relies on multiple reputable data sources to assemble a comprehensive view of author networks and keywords. It includes built-in rate limiting to respect source APIs and delays for web scraping, plus caching to minimize repeat calls and improve response times.

Key features include: rate limiting, caching, robust error handling, data merging from several sources, and asynchronous operations for better performance.

Start-up and runtime behavior are designed for practical use: you run the server locally, and the MCP client can request co-authors or keywords without needing to manage individual data source calls manually.

Data sources

The server uses several sources to gather data: Semantic Scholar for author and publication data, OpenAlex as a broad academic knowledge graph, Crossref for DOI resolution and metadata, and Google Scholar for deriving research interests when available.

Notes and tips

Available tools

get_coauthors

Find all co-authors for a given researcher, optionally filtered by institution to narrow results.

get_author_keywords

Extract research keywords from a researcher’s Google Scholar profile or equivalent sources.