MCP Enhanced Data Retrieval System

An MCP (Model Context Protocol) server that standardizes AI context sharing by integrating organizational knowledge sources (GitHub, internal docs, APIs) to enable domain-aware AI assistance for enterprise development workflows.

Project Overview

This system implements the Model Context Protocol to provide:

• Standardized AI context sharing across organizational knowledge sources
• GitHub repository integration with OAuth 2.1 authentication
• Vector-based semantic search using embeddings
• Optimized 1500-token context chunking for sub-500ms TTFT
• Parallel retrieval strategy with 2-second timeout
• Streamable HTTP transport using FastAPI

Architecture

AI Applications
    ↓
Authentication (OAuth 2.1 + RBAC)
    ↓
MCP Client
    ↓
MCP Protocol (JSON-RPC + HTTP)
    ↓
MCP Server
    • Multi-threaded parallel retrieval
    • 1500-token chunking
    ↓
Knowledge Tiers (Public, Internal, Restricted)
    ↓
Data Sources: GitHub | Docs
Vector Storage: Embeddings

Features

• MCP Protocol Compliance: JSON-RPC 2.0 over Streamable HTTP
• GitHub Integration: Repository data retrieval and contextualization
• Vector Embeddings: Semantic search using ChromaDB and Sentence Transformers
• Context Optimization: 1500-token chunking with parallel retrieval
• OAuth 2.1 Security: Secure authentication for GitHub access
• Performance: Sub-500ms response times with 2-second retrieval timeout

Project Structure

.
├── src/
│   ├── server/          # MCP server core and FastAPI app
│   ├── auth/            # OAuth 2.1 authentication
│   ├── github/          # GitHub API integration
│   ├── vector/          # Vector database and embeddings
│   └── utils/           # Utilities and helpers
├── tests/               # Test suite
├── config/              # Configuration files
├── data/                # Data storage (vector DB, cache)
├── logs/                # Application logs
├── requirements.txt     # Python dependencies
└── .env.example         # Environment variables template

Setup

1. Clone and navigate to the project:

   cd "MCP Enhanced Data Retrieval"
   

2. Create virtual environment:

   python3 -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   

3. Install dependencies:

   pip install -r requirements.txt
   

4. Configure environment variables:

   cp .env.example .env
   # Edit .env with your credentials
   

5. Run the server:

   uvicorn src.server.main:app --reload
   

Milestone 1 Goals

• ✅ MCP protocol analysis and communication flow evaluation
• ✅ High-level architecture design for enterprise knowledge integration
• 🔄 Functional MCP server with GitHub integration
• 🔄 OAuth 2.1 authentication implementation
• 🔄 1500-token context chunking mechanism
• 🔄 Vector-based semantic search

Success Criteria

• Functional MCP server that can retrieve and contextualize GitHub repository information
• OAuth 2.1 authentication for secure GitHub access
• 1500-token context chunking maintaining sub-500ms TTFT
• Parallel retrieval with 2-second timeout
• Vector-based semantic search for relevant content

Technologies

• MCP SDK: Anthropic MCP Python SDK
• Web Framework: FastAPI with Streamable HTTP transport
• GitHub API: PyGithub
• Authentication: OAuth 2.1 (authlib)
• Vector Database: ChromaDB
• Embeddings: Sentence Transformers (all-MiniLM-L6-v2)
• Token Processing: tiktoken

Author

Kalpalathika Ramanujam Advisor: Dr. Thomas Kinsman Rochester Institute of Technology

License

Academic Project - RIT Capstone