Which integrations are available for this server?

Provides scripts and utilities to download research papers from arXiv for indexing and semantic search within the personal research library. Integrates with OpenAI's embedding models to provide semantic indexing and high-accuracy natural language retrieval for documents.

How do I use Personal Research Assistant MCP?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Personal Research Assistant MCP What are the main challenges in RAG systems according to my research?" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

📚 Personal Research Assistant MCP

Python 3.11+ MCP License: MIT

A production-ready MCP (Model Context Protocol) server that enables semantic search across your personal research library. Built for AI Engineers who need fast, accurate document retrieval integrated with Claude Desktop and other AI tools.

🎯 Problem Statement

Researchers and professionals accumulate dozens of papers and documents but struggle to:

Find relevant information across multiple documents
Remember which paper contained specific insights
Connect related concepts across different sources
Spend 2+ hours daily searching for information

Traditional keyword search misses semantic connections, and reading everything is impractical.

💡 Solution

An MCP server that:

Indexes documents into a vector database using semantic embeddings
Enables Claude (or any MCP client) to query your research library conversationally
Provides sub-500ms response times with 85%+ retrieval accuracy
Includes a Streamlit dashboard for management and metrics

🏗️ Architecture

Documents (PDF/DOCX/HTML/MD) ↓ Document Processor → Text Chunker → Embeddings ↓ ChromaDB Vector Store ↓ ├─→ MCP Server (FastMCP) → Claude Desktop └─→ Streamlit UI → Monitoring/Testing

✨ Features

Semantic Search: Natural language queries across your entire library
Multi-Format Support: PDF, DOCX, HTML, Markdown, TXT
Fast Retrieval: <500ms query latency on 1000+ chunks
MCP Integration: Works with Claude Desktop, VS Code, and any MCP client
Metadata Extraction: Automatically extracts titles, authors, keywords
Query Logging: Track usage and performance metrics
Streamlit Dashboard: Upload, search, and visualize metrics

📊 Performance Metrics

Metric	Target	Actual
Retrieval Accuracy	85%	See METRICS.md
Query Latency	<500ms	See METRICS.md
Scale	10k+ chunks	1782+ chunks

🚀 Installation

Prerequisites

Python 3.11+
2GB RAM minimum
Git

Setup

# Clone repository git clone https://github.com/yourusername/research-assistant-mcp.git cd research-assistant-mcp # Create virtual environment python -m venv venv source venv/bin/activate # Windows: venv\Scripts\activate # Install dependencies pip install -r requirements.txt # Install local embeddings pip install sentence-transformers # Configure environment cp .env.example .env # Edit .env - add OPENAI_API_KEY if using OpenAI embeddings

Download Sample Data

# Download 25 AI/ML papers from arXiv python scripts/download_sample_papers.py --count 25

Index Documents

# Index sample papers python scripts/index_docs.py --folder ./sample_papers # Or index your own documents python scripts/index_docs.py --folder /path/to/your/papers --recursive

📖 Usage

Start MCP Server

python mcp_server/server.py

Configure Claude Desktop

Add to claude_desktop_config.json:

Mac: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json

{ "mcpServers": { "research-assistant": { "command": "python", "args": ["/full/path/to/research-assistant-mcp/mcp_server/server.py"], "env": {} } } }

Restart Claude Desktop.

Launch Streamlit UI

streamlit run ui/app.py

Opens at http://localhost:8501

🛠️ MCP Tools

`search_documents`

Semantic search across your library.

Query: "What are the challenges in RAG systems?" Returns: Top-k results with sources, scores, and metadata

`get_document_summary`

Get quick overview of a document.

Input: Document path or title Returns: Title, author, keywords, preview

`find_related_papers`

Find documents similar to a topic.

Query: "prompt engineering techniques" Returns: Related papers with relevance scores

📁 Project Structure

research-assistant-mcp/ ├── mcp_server/ # MCP server implementation │ └── server.py ├── rag_pipeline/ # RAG components │ ├── config.py │ ├── document_processor.py │ ├── chunker.py │ ├── vector_store.py │ ├── retriever.py │ └── metadata_extractor.py ├── ui/ # Streamlit dashboard │ ├── app.py │ └── pages/ ├── scripts/ # CLI utilities │ ├── index_docs.py │ └── download_sample_papers.py ├── tests/ # Testing & benchmarks │ ├── sample_queries.json │ └── benchmark_performance.py ├── data/ # Data storage │ ├── chroma_db/ │ └── query_logs/ └── docs/ # Documentation └── METRICS.md

🧪 Testing

# Run performance benchmarks python tests/benchmark_performance.py # Output: Accuracy, latency, scale metrics

🐳 Docker Deployment

# Build and run docker-compose up -d # Access UI at http://localhost:8501 # MCP server runs on localhost:8000

📈 Example Queries

Cross-document synthesis
"Compare different fine-tuning approaches for LLMs"
Concept exploration
"How does RLHF improve model alignment?"
Technical details
"Explain transformer attention mechanisms"
Literature review
"What are recent developments in RAG systems?"

🔧 Customization

Change Embedding Model

Edit .env:

# OpenAI (paid, best quality) EMBEDDING_MODEL=text-embedding-3-small # Or use local (free) by default - already configured

Adjust Chunk Size

Edit .env:

CHUNK_SIZE=1000 # Characters per chunk CHUNK_OVERLAP=200 # Overlap between chunks

Add Document Types

Edit rag_pipeline/document_processor.py to add new file type handlers.

🐛 Troubleshooting

ChromaDB errors: Delete data/chroma_db and re-index
Import errors: Verify pip install -r requirements.txt completed
UI blank: Check browser console, try Chrome/Firefox
Slow queries: Reduce TOP_K_RESULTS in .env

🚧 Future Enhancements

Auto-watch folder for new documents
Cross-encoder reranking for better accuracy
Multi-modal support (images, diagrams)
Citation network graph
Export to Notion/Obsidian
Web interface (FastAPI + React)

🎥 Demo Video

[Link to 2-minute demo video - Coming soon]

🤝 Contributing

Contributions welcome! Please open issues or PRs.

📄 License

MIT License - see LICENSE

🙏 Acknowledgments

Built with FastMCP
Powered by LangChain
Vector storage by ChromaDB

Built by [Your Name] | GitHub | LinkedIn

Personal Research Assistant MCP