Summary
This server plugs into Semantic Scholar, OpenAlex, Crossref, and Google Scholar to map academic collaboration networks. You get two main operations: find all co-authors for a researcher by name and institution, and extract research keywords from their Google Scholar profile. It handles rate limiting across APIs, caches results to avoid redundant calls, and merges data from multiple sources. Reach for this when you're doing literature reviews, building research teams, or need to quickly understand someone's network and expertise areas without manually clicking through profile pages. The Google Scholar scraping includes delays to stay respectful of rate limits.
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one prompt install →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
- Clone or download this repository
- Create a virtual environment:
python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate - 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.
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Plug Mailtrap into your AI workflow and let it handle the email.
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Install now →Make coding agent sessions - Searchable, Shareable, Vendor-neutral & Scored.
Try For Free →Your agent targets a perfect 10 Code Health score. Deterministic. Every commit.
Try For Free →Integrate web data into your AI product. One API to scrape website & brand data.
Get API Key Now →belt cli automatically finds the best tools and skills for your agent. image, video, music, tts...
one prompt install →
