Research Citations MCP Server

A Model Context Protocol (MCP) server for searching and citing research papers using Retrieval-Augmented Generation (RAG). This server helps you find relevant citations, search for specific passages, and answer research questions based on your collection of PDF research papers.

Features

• 🔍 Semantic Search: Find relevant passages across all your research papers

• 📚 Citation Finder: Get properly formatted citations with source references

• 💡 Question Answering: Ask questions and get answers backed by your papers

• 📝 Paper Summarization: Generate summaries with custom prompts or pre-defined focus areas

• 🔬 Methodology Extraction: Extract structured methodology details from papers

• 📖 Bibliography Extraction: Get APA-formatted citations from paper reference sections

• 🏷️ Automatic Metadata Extraction: During index rebuild, automatically extracts authors, year, title, and APA citation from first 1-2 pages using GPT-4o

• 📄 PDF Processing: Automatic extraction and chunking of text from PDFs

• 🚀 SSE Transport: Remote access via Server-Sent Events

• 🎯 Vector Search: ChromaDB-powered semantic similarity search

• 🤖 LangChain Integration: Built on battle-tested RAG frameworks

Related MCP server: ScholarMCP

Architecture

┌─────────────────────────────────────────────────┐
│           MCP Client (Claude, etc.)             │
└───────────────────┬─────────────────────────────┘
                    │ SSE Transport
┌───────────────────▼─────────────────────────────┐
│              FastAPI + Starlette                │
│                 MCP Server                       │
├─────────────────────────────────────────────────┤
│              RAG Engine                         │
│  ┌─────────────┐        ┌──────────────┐       │
│  │  LangChain  │◄──────►│   ChromaDB   │       │
│  │   Retrieval │        │ Vector Store │       │
│  └─────────────┘        └──────────────┘       │
│         │                       ▲               │
│         ▼                       │               │
│  ┌─────────────┐        ┌──────────────┐       │
│  │ OpenAI LLM  │        │ PDF Processor│       │
│  │ & Embeddings│        │  (PyPDFLoader)│      │
│  └─────────────┘        └──────┬───────┘       │
└────────────────────────────────┼───────────────┘
                                 │
                        ┌────────▼────────┐
                        │  Research Papers │
                        │   (PDF Files)    │
                        └──────────────────┘

Installation

Prerequisites

• Python 3.10 or higher

• OpenAI API key

• UV package manager (recommended) or pip

Setup

1. Clone the repository

   git clone <your-repo-url>
   cd citations-mcp

2. Install dependencies

   # Using uv (recommended)
   uv sync
   
   # Or using pip
   pip install -e .

3. Configure environment

   cp .env.example .env

   Edit .env and set:

   • OPENAI_API_KEY: Your OpenAI API key

   • PAPERS_DIRECTORY: Path to your folder containing PDF research papers

   • Other optional settings (see .env.example)

Usage

Starting the Server

# Using uv
uv run uvicorn src.main:app --host 127.0.0.1 --port 8000 --reload

# Or using uvicorn directly
uvicorn src.main:app --host 127.0.0.1 --port 8000 --reload

The server will:

1. Start on http://127.0.0.1:8000

2. Automatically process all PDFs in your papers directory

3. Build a vector store index (cached for future runs)

4. Expose MCP tools via SSE at http://127.0.0.1:8000/mcp/sse

Available MCP Tools

1. search_papers

Search for relevant passages in your research papers.

{
  "query": "machine learning for natural language processing",
  "num_results": 5
}

2. search_in_paper

Search for relevant passages within a specific paper only.

{
  "query": "transformer architecture",
  "filename": "attention_is_all_you_need.pdf",
  "num_results": 5
}

3. find_citation

Find relevant citations for a specific topic, grouped by source paper.

{
  "topic": "transformer architecture",
  "num_citations": 3
}

4. answer_question

Ask a research question and get an answer with sources.

{
  "question": "What are the main challenges in few-shot learning?"
}

5. list_papers

List all indexed research papers.

{}

6. get_paper_info

Get information about a specific paper.

{
  "filename": "attention_is_all_you_need.pdf"
}

7. rebuild_index

Rebuild the vector store index (use after adding new papers). During rebuild, the server automatically:

• Extracts metadata from the first 1-2 pages of each paper using GPT-4o

• Captures authors, publication year, title, journal/conference, DOI, and generates APA citation

• Attaches this metadata to all chunks for easy citation reference

{
  "force": true
}

Note: Metadata extraction uses GPT-4o API calls (one per paper), so rebuilding with many papers may incur API costs.

8. extract_methodology

Extract structured methodology details from a research paper.

{
  "filename": "attention_is_all_you_need.pdf"
}

Returns: Research approach, datasets, models, evaluation metrics, experimental setup, baselines, and implementation details.

9. summarize_paper

Generate a summary of a research paper with custom prompts or pre-defined focus.

{
  "filename": "attention_is_all_you_need.pdf",
  "focus": "key_findings"  // Options: "general", "key_findings", "methodology", "limitations", "contributions"
}

Or with a custom prompt:

{
  "filename": "attention_is_all_you_need.pdf",
  "custom_prompt": "Summarize the experimental results and their statistical significance"
}

10. extract_bibliography

Extract the bibliography/references from a paper in APA format.

{
  "filename": "attention_is_all_you_need.pdf"
}

Returns: APA-formatted list of all citations used in the paper.

Connecting from Claude Desktop

Add this to your Claude Desktop MCP configuration:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "research-citations": {
      "url": "http://127.0.0.1:8000/mcp/sse"
    }
  }
}

Restart Claude Desktop and you'll see the research tools available.

Public Access via Ngrok (for ChatGPT and Remote Access)

To expose your server publicly via ngrok:

# Install ngrok (if not already installed)
brew install ngrok

# Configure your ngrok auth token
export NGROK_AUTHTOKEN="your_ngrok_token"

# Start the public server
./start_public.sh

This will give you a public URL like https://abc123.ngrok-free.app/mcp/sse that you can use with ChatGPT or other remote MCP clients.

See NGROK_SETUP.md for detailed instructions.

Configuration

Environment Variables

Adding Papers

1. Place PDF files in your PAPERS_DIRECTORY

2. Either:

   • Restart the server (auto-detects new papers)

   • Call the rebuild_index tool with force: true

Development

Project Structure

citations-mcp/
├── src/
│   ├── __init__.py
│   ├── config.py           # Configuration management
│   ├── pdf_processor.py    # PDF loading and chunking
│   ├── rag_engine.py       # RAG pipeline and search
│   ├── mcp_server.py       # MCP tool definitions
│   ├── sse_transport.py    # SSE transport layer
│   └── main.py             # FastAPI application
├── pyproject.toml          # Dependencies
├── .env.example            # Environment template
└── README.md               # This file

Running Tests

# Using uv
uv run pytest

# Or using pytest directly
pytest

Adding New Tools

Edit src/mcp_server.py and add a new function decorated with @mcp.tool():

@mcp.tool()
async def my_new_tool(param: str) -> Dict[str, Any]:
    """Tool description."""
    # Implementation
    return {"result": "value"}

Troubleshooting

Vector store not initializing

• Check that PAPERS_DIRECTORY exists and contains PDF files

• Ensure OPENAI_API_KEY is valid

• Check logs for specific error messages

PDFs not being processed

• Verify PDFs are valid and readable

• Check file permissions

• Look for processing errors in server logs

Poor search results

• Adjust CHUNK_SIZE and CHUNK_OVERLAP in .env

• Try different EMBEDDING_MODEL options

• Rebuild index with force: true

Performance Tips

• First Run: Initial indexing takes time proportional to number of papers

• Caching: Vector store is persisted and reused on subsequent runs

• Embeddings: text-embedding-3-small is fast and cost-effective

• Chunks: Smaller chunks (500-1000) work better for precise citations

License

MIT License - See LICENSE file for details

Contributing

Contributions welcome! Please open an issue or PR.

Acknowledgments

• Built with LangChain

• Uses Model Context Protocol

• Powered by OpenAI embeddings and LLMs

• PDF processing via pypdf

• Vector storage with ChromaDB