🧠 Research MCP - Model Context Protocol Research Assistant

A complete Model Context Protocol (MCP)-based Research Assistant that enables LLMs to fetch, analyze, and summarize academic research papers in real-time from multiple trusted sources: arXiv, Semantic Scholar, and PubMed.

🔍 Overview

The Research MCP system provides standardized access to academic research databases through three specialized MCP servers. Each server implements the MCP specification, allowing AI assistants to query live research data, process results, and return structured insights like summaries, comparisons, and citations.

✨ Features

• 📚 Multi-Source Search: Query arXiv, Semantic Scholar, and PubMed simultaneously
• 🔄 Automatic Deduplication: Smart paper matching across different sources
• 📊 Citation Analysis: Track citation counts and influential papers
• 📝 BibTeX Generation: Automatic citation formatting for all sources
• ⚡ Rate Limiting: Built-in request throttling to respect API limits
• 🎯 Advanced Filtering: Filter by year, author, venue, and more
• 🔍 Full Metadata: Complete paper information including abstracts, authors, and links

🏗️ Architecture

┌─────────────────────────────────────────────────────────────┐
│                      LLM Client                             │
│  (Issues natural language queries)                          │
└────────────────┬────────────────────────────────────────────┘
                 │
                 │ MCP Protocol
                 │
┌────────────────┴───────────────────────────────────────────┐
│                   MCP Servers                              │
│  ┌──────────┐   ┌──────────────┐  ┌──────────┐             │
│  │  arXiv   │   │   Semantic   │  │  PubMed  │             │
│  │  Server  │   │    Scholar   │  │  Server  │             │
│  └────┬─────┘   └──────┬───────┘  └────┬─────┘             │
└───────┼────────────────┼───────────────┼───────────────────┘
        │                │               │
        │                │               │
┌───────┴────────────────┴───────────────┴───────────────────┐
│              External APIs                                 │
│    arXiv API    Semantic Scholar API    PubMed E-utils     │
└────────────────────────────────────────────────────────────┘

📋 Prerequisites

• Node.js 18+
• An MCP-compatible client (Claude Desktop, Cline, etc.)

🚀 Installation

Quick Start with npx (Recommended)

No installation or API keys needed! Just add to your MCP client configuration:

{
  "mcpServers": {
    "research-arxiv": {
      "command": "npx",
      "args": ["-y", "researchmcp", "arxiv"]
    },
    "research-semantic-scholar": {
      "command": "npx",
      "args": ["-y", "researchmcp", "semantic"]
    },
    "research-pubmed": {
      "command": "npx",
      "args": ["-y", "researchmcp", "pubmed"]
    }
  }
}

That's it! All three servers work perfectly without any API keys or configuration.

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Local Development

For contributing or modifying the code:

git clone https://github.com/gyash1512/ResearchMCP.git
cd ResearchMCP
npm install
npm run build

🎮 Usage

Using with npx (Recommended)

Just configure in your MCP client - that's it! No API keys needed.

Local Development

Start servers individually for testing:

npm run start:arxiv
npm run start:semantic
npm run start:pubmed

MCP Configuration

Simple setup - no API keys required:

{
  "mcpServers": {
    "research-arxiv": {
      "command": "npx",
      "args": ["-y", "researchmcp", "arxiv"]
    },
    "research-semantic-scholar": {
      "command": "npx",
      "args": ["-y", "researchmcp", "semantic"]
    },
    "research-pubmed": {
      "command": "npx",
      "args": ["-y", "researchmcp", "pubmed"]
    }
  }
}

<details> <summary><b>Optional:</b> Add API keys for higher rate limits (only if needed)</summary>

{
  "mcpServers": {
    "research-semantic-scholar": {
      "command": "npx",
      "args": ["-y", "researchmcp", "semantic"],
      "env": {
        "SEMANTIC_SCHOLAR_API_KEY": "your_key_here"
      }
    },
    "research-pubmed": {
      "command": "npx",
      "args": ["-y", "researchmcp", "pubmed"],
      "env": {
        "PUBMED_API_KEY": "your_key_here",
        "PUBMED_EMAIL": "your_email@example.com"
      }
    }
  }
}

</details>

<details> <summary>For local development</summary>

{
  "mcpServers": {
    "research-arxiv": {
      "command": "node",
      "args": ["./dist/servers/arxiv-server.js"],
      "cwd": "/absolute/path/to/ResearchMCP"
    },
    "research-semantic-scholar": {
      "command": "node",
      "args": ["./dist/servers/semantic-scholar-server.js"],
      "cwd": "/absolute/path/to/ResearchMCP"
    },
    "research-pubmed": {
      "command": "node",
      "args": ["./dist/servers/pubmed-server.js"],
      "cwd": "/absolute/path/to/ResearchMCP"
    }
  }
}

Note: Replace /absolute/path/to/ResearchMCP with your actual project path.

</details>

📚 Available Tools

arXiv Server

search_arxiv

Search for papers on arXiv by keyword, author, or subject.

Parameters:

• query (string, required): Search query
• maxResults (number, optional): Max results (default: 10, max: 100)
• startYear (number, optional): Filter by start year
• endYear (number, optional): Filter by end year
• author (string, optional): Filter by author name
• sortBy (string, optional): Sort by relevance, lastUpdatedDate, or submittedDate

Example:

{
  "query": "quantum computing",
  "maxResults": 5,
  "startYear": 2023,
  "sortBy": "relevance"
}

get_arxiv_paper

Get detailed information about a specific arXiv paper by ID.

Parameters:

• arxivId (string, required): arXiv paper ID (e.g., "2301.12345")

arxiv_to_bibtex

Convert arXiv paper to BibTeX format.

Parameters:

• arxivId (string, required): arXiv paper ID

---

Semantic Scholar Server

search_semantic_scholar

Search for papers with citation information.

Parameters:

• query (string, required): Search query
• maxResults (number, optional): Max results (default: 10, max: 100)
• startYear (number, optional): Filter by start year
• endYear (number, optional): Filter by end year

Example:

{
  "query": "transformer architecture",
  "maxResults": 10,
  "startYear": 2023
}

get_semantic_scholar_paper

Get paper by Semantic Scholar ID or DOI.

Parameters:

• identifier (string, required): Paper ID or DOI

get_paper_citations

Get papers that cite a specific paper.

Parameters:

• paperId (string, required): Semantic Scholar paper ID
• maxResults (number, optional): Max citing papers (default: 10, max: 100)

semantic_scholar_to_bibtex

Convert paper to BibTeX format.

Parameters:

• identifier (string, required): Paper ID or DOI

---

PubMed Server

search_pubmed

Search biomedical and life sciences papers.

Parameters:

• query (string, required): Search query (supports MeSH terms)
• maxResults (number, optional): Max results (default: 10, max: 100)
• startYear (number, optional): Filter by start year
• endYear (number, optional): Filter by end year

Example:

{
  "query": "cancer treatment",
  "maxResults": 5,
  "startYear": 2022
}

get_pubmed_paper

Get paper by PMID.

Parameters:

• pmid (string, required): PubMed ID

pubmed_to_bibtex

Convert paper to BibTeX format.

Parameters:

• pmid (string, required): PubMed ID

💡 Example Queries

Example 1: Multi-Source Research Query

Query: "Find recent papers on federated learning in healthcare"

Workflow:

1. Search arXiv: search_arxiv with query "federated learning healthcare", startYear: 2023
2. Search Semantic Scholar: search_semantic_scholar with same parameters
3. Search PubMed: search_pubmed with same parameters
4. Combine and deduplicate results
5. Sort by citation count and relevance
6. Generate summary with top 5 papers

Expected Output:

• Comprehensive list of papers from all sources
• Deduplicated results
• Citation counts where available
• Links to full papers
• BibTeX citations

Example 2: Most Cited Paper

Query: "What's the most cited 2023 paper on quantum machine learning?"

Workflow:

1. Call search_semantic_scholar:

   {
     "query": "quantum machine learning",
     "maxResults": 50,
     "startYear": 2023,
     "endYear": 2023
   }
   

2. Sort results by citationCount
3. Get detailed info with get_semantic_scholar_paper
4. Generate BibTeX with semantic_scholar_to_bibtex

Expected Output:

• Paper title and authors
• Citation count and venue
• Abstract and key findings
• BibTeX citation
• Link to paper

Example 3: Research Trend Analysis

Query: "Summarize transformer innovations after 2023"

Workflow:

1. Search multiple sources for "transformer architecture" papers after 2023
2. Extract key information from abstracts
3. Identify common themes and methods
4. Generate trend analysis
5. Provide top papers with citations

Expected Output:

• Overview of key innovations
• Timeline of developments
• Most influential papers
• Citation network analysis
• Recommended reading list

Example 4: Citation Network

Query: "Find papers citing 'Attention is All You Need'"

Workflow:

1. Find original paper: search_semantic_scholar with title
2. Get paper ID from results
3. Call get_paper_citations with the paper ID
4. Filter by year/relevance
5. Generate summary of citing papers

Expected Output:

• List of papers that cite the original work
• Citation contexts
• Related research directions
• Impact analysis

🔧 API Response Schemas

arXiv Paper Object

{
  id: string;              // arXiv ID (e.g., "2301.12345")
  title: string;
  authors: string[];
  abstract: string;
  published: string;       // ISO date
  updated: string;         // ISO date
  url: string;            // Paper URL
  pdfUrl: string;         // PDF download URL
  categories: string[];   // Subject categories
  primaryCategory: string;
}

Semantic Scholar Paper Object

{
  paperId: string;
  title: string;
  abstract: string | null;
  year: number | null;
  authors: Array<{
    authorId: string;
    name: string;
  }>;
  citationCount: number;
  referenceCount: number;
  influentialCitationCount: number;
  url: string;
  venue: string | null;
  publicationDate: string | null;
}

PubMed Paper Object

{
  pmid: string;            // PubMed ID
  title: string;
  abstract: string;
  authors: string[];
  journal: string;
  year: string;
  doi: string | null;
  url: string;
  publicationTypes: string[];
  meshTerms: string[];    // Medical Subject Headings
}

🛡️ Rate Limiting

All servers work great without API keys:

Server	Default Rate	With API Key	Do You Need Keys?
arXiv	3 req/sec	N/A	❌ No - works perfectly!
Semantic Scholar	1-3 req/sec	10 req/sec	❌ No - unless making 100+ queries/min
PubMed	3 req/sec	10 req/sec	❌ No - unless making 100+ queries/min

Recommendation: Start without any API keys. Only add them if you hit rate limits.

🔒 Security Notes

• No API keys needed - all servers work out of the box
• If using API keys, pass via MCP config env section (see optional config above)
• Never commit API keys to version control
• Respect API rate limits and terms of service

📖 MCP Specification Compliance

This implementation follows the Model Context Protocol specification:

• ✅ Standard tool definition schema
• ✅ JSON-based request/response format
• ✅ Error handling with proper status codes
• ✅ Resource management and cleanup
• ✅ Stdio transport for client communication

🤝 Contributing

Contributions are welcome! Areas for improvement:

• Additional research sources (IEEE, ACM, etc.)
• Advanced filtering and ranking algorithms
• Paper recommendation system
• Citation graph visualization
• Full-text analysis capabilities

📄 License

MIT License - See LICENSE file for details

🙏 Acknowledgments

• arXiv for open access to research papers
• Semantic Scholar for citation data and API
• PubMed/NCBI for biomedical research database
• Model Context Protocol team for the MCP specification

📞 Support

For issues, questions, or contributions:

• Open an issue on GitHub
• Check API documentation for each service
• Review MCP specification for protocol details

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Built with ❤️ using TypeScript and the Model Context Protocol