# 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:**
   ```bash
   cd "MCP Enhanced Data Retrieval"
   ```

2. **Create virtual environment:**
   ```bash
   python3 -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   ```

3. **Install dependencies:**
   ```bash
   pip install -r requirements.txt
   ```

4. **Configure environment variables:**
   ```bash
   cp .env.example .env
   # Edit .env with your credentials
   ```

5. **Run the server:**
   ```bash
   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