ResearchMCP - AI-Powered Research Paper Assistant

An MCP (Model Context Protocol) server that helps analyze academic papers by fetching research from OpenAlex's database of 250M+ papers.

Features

• Search Papers: Search for academic papers by topic/keywords with advanced filtering

• Extract Claims: Automatically extract key findings and research claims from papers

• Compare Papers: Identify contradictions and consensus across multiple papers

• Citation Analysis: Explore citation networks to discover related research

• Research Gap Finder: Analyze multiple papers to identify gaps and future research opportunities

• Powered by OpenAlex API (millions of papers, no API key needed!)

Tools

search_papers

Search for academic papers on OpenAlex.

Parameters:

• query (string, required): Research topic or keywords

• max_results (int, optional): Maximum papers to return (default: 5)

• year_from (int, optional): Only papers from this year onwards

Example: "Search for papers on transformer models in NLP"

get_paper_abstract

Get the full abstract for a specific paper.

Parameters:

• paper_id (string, required): OpenAlex paper ID from search results

Example: "Get abstract for paper W2964027837"

extract_claims

Extract key claims and findings from a paper's abstract.

Parameters:

• paper_id (string, required): OpenAlex paper ID

Returns: Structured extraction of research questions, methodology, findings, and conclusions

Example: "Extract claims from paper W2964027837"

compare_papers

Compare claims across multiple papers to find contradictions and consensus.

Parameters:

• paper_ids (string, required): Comma-separated list of paper IDs (2-5 papers)

Returns: Comparative analysis showing agreements, contradictions, and research gaps

Example: "Compare these papers: W2964027837, W3177828909, W2123456789"

get_citations

Get citation network for a paper - see what cites it and what it references.

Parameters:

• paper_id (string, required): OpenAlex paper ID

• direction (string, optional): "cited_by", "references", or "both" (default: "both")

• max_results (int, optional): Maximum citations per direction (default: 10)

Returns: Lists of papers that cite this work and papers it references

Example: "Show me the citation network for W2964027837"

find_research_gaps

Analyze multiple papers on a topic to identify research gaps and unanswered questions.

Parameters:

• query (string, required): Research topic to analyze

• num_papers (int, optional): Number of papers to analyze (default: 5, max: 10)

Returns: Comprehensive gap analysis including:

• Unanswered research questions

• Methodological limitations

• Understudied areas

• Contradictions requiring further investigation

• Emerging research opportunities

Example: "Find research gaps in transformer architecture optimization"

Installation

Option 1: Deploy to FastMCP Cloud (Recommended)

1. Clone the repository:

2. Deploy to FastMCP Cloud:

3. Set the entrypoint as: src/server.py

4. Connect to Claude Desktop by adding to your MCP settings

Option 2: Run Locally

Usage with Claude Desktop

Once deployed or running locally, you can use natural language prompts in Claude Desktop:

Search for papers:

• "Search for recent papers on quantum computing"

• "Find papers about CRISPR gene editing from 2020 onwards"

Analyze specific papers:

• "Extract the key claims from paper W2964027837"

• "Get the abstract for paper W3177828909"

Compare research:

• "Compare these papers: W2964027837, W3177828909"

• "What do these papers agree and disagree on?"

Explore citations:

• "Show me what papers cite W2964027837"

• "What papers does W2964027837 reference?"

Find research gaps:

• "Find research gaps in transformer architecture optimization"

• "What are the unanswered questions in climate change modeling?"

Architecture

ResearchMCP uses a clean separation of concerns architecture:

• server.py: MCP server that defines tools available to Claude Desktop

• paper_fetcher.py: Worker class handling all OpenAlex API interactions

This design pattern ensures:

• Clean code organization

• Easy maintenance and testing

• Separation between tool orchestration and API logic

Technology Stack

• FastMCP: Python framework for building MCP servers

• OpenAlex API: Access to 250M+ academic papers with no API key required

• httpx: Modern async-capable HTTP client

• Python 3.12+: Type hints and modern Python features

Why MCP over RAG?

Traditional RAG systems require:

• Pre-indexing large document collections

• Vector database setup and maintenance

• Embedding generation costs

• Limited to pre-indexed documents

ResearchMCP with MCP:

• Live access to 250M+ papers without pre-indexing

• No infrastructure - no vector databases needed

• Always up-to-date - accesses latest published research

• Cost-effective - no embedding generation costs

• Tool orchestration - Claude intelligently chains multiple API calls

Contributing

Contributions are welcome! Feel free to submit issues or pull requests.

License

MIT License - feel free to use this project for your research needs!