Frequently Asked Questions (FAQ) about the Academic Author Network MCP Server

Q: What is an MCP Server? A: MCP (Model Context Protocol) stands for Model Context Protocol. An MCP server acts as a bridge, allowing AI models to access and interact with external data sources and tools by standardizing how applications provide context to LLMs.

Q: What data sources does the Academic Author Network MCP Server use? A: The server uses the Semantic Scholar API, OpenAlex API, Crossref API, and Google Scholar to gather data on authors and publications.

Q: What are the limitations of the server? A: Limitations include reliance on the availability and accuracy of external APIs, potential rate limits imposed by those APIs, and the possibility of occasional scraping failures due to anti-bot measures. Results quality depends on author name uniqueness.

Q: How do I install the server? A: The installation instructions are provided in the UBOS Asset Marketplace and involve cloning the repository, creating a virtual environment, and installing the required dependencies using pip install -r requirements.txt.

Q: Can I contribute to the development of the server? A: Yes, contributions are welcome! Please ensure that all API integrations respect rate limits and terms of service.

Q: What is UBOS? A: UBOS is a full-stack AI Agent Development Platform. It helps you orchestrate AI Agents, connect them with your enterprise data, build custom AI Agents with your LLM model, and create Multi-Agent Systems.

Q: How does the UBOS platform relate to the MCP Server? A: The UBOS platform provides the infrastructure and tools for deploying and managing the MCP Server. It allows you to seamlessly integrate the server into your AI Agent workflows and leverage its capabilities for various research tasks.

Q: Do I need to configure anything for basic usage? A: The server includes built-in rate limiting and error handling, so no additional configuration is required for basic usage.

Q: What is the get_coauthors function? A: The get_coauthors function finds all co-authors for a given researcher based on their name, surname, and optional institution.

Q: What is the get_author_keywords function? A: The get_author_keywords function extracts research keywords from a researcher’s Google Scholar profile, providing insights into their research interests.