MCP Movies Database Workshop

A comprehensive Model Context Protocol (MCP) demonstration project showcasing AI-powered movie database interactions through a complete client-server architecture.

🎯 Project Overview

This project demonstrates how to build production-ready MCP applications with:

• MCP Server: FastMCP-based server exposing movie database CRUD operations
• MCP Client: Streamlit chatbot for natural language movie queries
• Database Layer: SQLite with comprehensive movie data and analytics
• AI Integration: GPT-4o powered conversational interface

🏗️ Architecture

┌─────────────────────────────────────────────────────────────┐
│                    User Interface                           │
│                 (Streamlit Web App)                         │
└─────────────────┬───────────────────────────────────────────┘
                  │ Natural Language Queries
┌─────────────────▼───────────────────────────────────────────┐
│                 MCP Client                                  │
│            (movies_chatbot.py)                              │
│  ┌─────────────────────────────────────────────────────┐    │
│  │  MCPAgent (GPT-4o) + MCPClient (mcp-use)           │    │
│  └─────────────────────────────────────────────────────┘    │
└─────────────────┬───────────────────────────────────────────┘
                  │ MCP Protocol (HTTP)
┌─────────────────▼───────────────────────────────────────────┐
│                 MCP Server                                  │
│           (movies_mcp_server.py)                            │
│  ┌─────────────────────────────────────────────────────┐    │
│  │  FastMCP Tools: CRUD Operations & Analytics        │    │
│  │  • create_movie  • search_movies  • get_statistics │    │
│  │  • update_movie  • delete_movie   • advanced_search│    │
│  └─────────────────────────────────────────────────────┘    │
└─────────────────┬───────────────────────────────────────────┘
                  │ SQL Queries
┌─────────────────▼───────────────────────────────────────────┐
│              SQLite Database                                │
│                (movies.db)                                  │
│         Indexed movie data with metadata                    │
└─────────────────────────────────────────────────────────────┘

🚀 Quick Start Guide

Prerequisites

• Python 3.8+
• OpenAI API key
• Terminal/Command line access

1. Installation

# Clone or navigate to the project directory
cd dds_workshop

# Install all required dependencies
pip install -r requirements.txt

2. Environment Setup

Create a .env file in the project root:

# OpenAI API Configuration (Required)
OPENAI_API_KEY=your_openai_api_key_here

# Database Configuration (Optional - defaults provided)
DB_FILE_PATH=./data/movies.db
CSV_FILE_PATH=./data/mymoviedb.csv
MCP_CONFIG_FILE_PATH="./config/mcp_config.json

3. Database Setup (First Time Only)

# Ingest CSV data into SQLite database
python utilities/ingest_movies_to_sqlite.py

Expected Output:

Starting ingestion from: ./data/mymoviedb.csv
Reading CSV file...
Detected encoding: utf-8
Dataset info:
Total rows: [number]
Successfully inserted [number] movies into the database!
✅ Movie database ingestion completed successfully!

4. Start the MCP Server

Terminal 1:

# Navigate to server directory
cd server/

# Start the MCP server
python movies_mcp_server.py

Expected Output:

Starting Movies Database MCP Server...
Database: ./data/movies.db

Available tools:
- create_movie
- get_movie_by_id
- search_movies_by_title
- get_movies_by_genre
- ... and more

Server is running...

Server will be available at: http://0.0.0.0:4567

5. Start the MCP Client

Terminal 2:

# Navigate to client directory  
cd client/

# Start the Streamlit chatbot
streamlit run movies_chatbot.py

Expected Output:

You can now view your Streamlit app in your browser.

Local URL: http://localhost:8501
Network URL: http://[your-ip]:8501

6. Use the Application

1. Open your browser to http://localhost:8501
2. Wait for initialization - "Initializing MCP agent..."
3. Start chatting with example queries:
   • "Tell me about The Matrix"
   • "Find action movies with high ratings"
   • "What movies were released in 2023?"
   • "Show me sci-fi movies from the 2000s"

📁 Project Structure

dds_workshop/
├── README.md                    # This file - main project overview
├── requirements.txt             # Python dependencies
├── .env                        # Environment variables (create this)
│
├── client/                     # MCP Client (Streamlit Chatbot)
│   ├── movies_chatbot.py       # Main chatbot application
│   └── README.md              # Detailed client documentation
│
├── server/                     # MCP Server (FastMCP)
│   ├── movies_mcp_server.py    # MCP server with movie tools
│   └── README.md              # Detailed server documentation
│
├── config/                     # Configuration files
│   └── mcp_config.json        # MCP server connection config
│
├── data/                       # Database and source data
│   ├── movies.db              # SQLite database (generated)
│   └── mymoviedb.csv          # Source CSV data
│
├── utilities/                  # Database utilities
│   ├── constants.py           # Configuration constants
│   ├── ingest_movies_to_sqlite.py  # CSV to SQLite ingestion
│   └── movies_crud.py         # Direct database operations
│
└── supporting_files/          # Workshop materials
    ├── agenda.txt
    ├── mcp_architecture.png
    └── react.md

💬 Example Interactions

Natural Language Movie Queries

🎬 User: "Tell me about Lord of the Rings movies"
🤖 Assistant: Here are the Lord of the Rings movies:

1. **The Lord of the Rings: The Fellowship of the Ring** (2001)
   - Rating: 8.8/10 (1.8M votes)
   - Genre: Adventure, Fantasy, Action
   - Overview: A meek Hobbit from the Shire and eight companions...

2. **The Lord of the Rings: The Two Towers** (2002)
   - Rating: 8.7/10 (1.6M votes)
   - Genre: Adventure, Fantasy, Action
   - Overview: Frodo and Sam are trekking to Mordor...

3. **The Lord of the Rings: The Return of the King** (2003)
   - Rating: 9.0/10 (1.8M votes)
   - Genre: Adventure, Fantasy, Action
   - Overview: Aragorn is revealed as the heir to the ancient kings...

Advanced Search Capabilities

🎬 User: "Find sci-fi movies from 2020-2023 with ratings above 7.5"
🤖 Assistant: Here are highly-rated sci-fi movies from 2020-2023:

1. **Dune** (2021) - Rating: 8.0/10
2. **Spider-Man: No Way Home** (2021) - Rating: 8.4/10
3. **The Batman** (2022) - Rating: 7.8/10
...

🛠️ Available Features

MCP Server Tools (11 total)

• Create: Add new movies with metadata
• Search: Title, genre, year, language filtering
• Update: Modify existing movie data
• Delete: Remove movies from database
• Analytics: Comprehensive database statistics

Client Capabilities

• Natural Language Processing: Ask questions in plain English
• Real-time Responses: Streaming AI responses
• Chat History: Persistent conversation context
• Error Handling: Graceful error recovery
• Example Queries: Built-in query suggestions

🔧 Troubleshooting

Common Issues

1. Database not found

# Solution: Run the ingestion script first
python utilities/ingest_movies_to_sqlite.py

2. MCP Server connection failed

# Check if server is running
curl http://0.0.0.0:4567/mcp

# Verify config in config/mcp_config.json

3. OpenAI API errors

# Verify API key is set
echo $OPENAI_API_KEY

# Check .env file exists and is loaded

4. Port conflicts

# Server: Change port in movies_mcp_server.py
mcp.run(transport="http", host="0.0.0.0", port=4568)

# Client: Use different Streamlit port
streamlit run movies_chatbot.py --server.port 8502

Debug Mode

# Enable verbose logging
export PYTHONPATH=.
python -c "import logging; logging.basicConfig(level=logging.DEBUG)"

📊 Database Information

• Total Movies: ~50,000+ entries
• Date Range: 1900s to 2024
• Languages: 100+ languages
• Genres: Action, Comedy, Drama, Sci-Fi, etc.
• Metadata: Ratings, popularity, vote counts, overviews

🔍 Advanced Usage

Direct Database Access

# Use CRUD utilities directly
python utilities/movies_crud.py

Custom Queries

# Test MCP server tools directly
python -c "
import asyncio
from mcp_use import MCPClient

async def test():
    client = MCPClient.from_config_file('config/mcp_config.json')
    result = await client.call_tool('search_movies_by_title', {'title_search': 'Matrix'})
    print(result)

asyncio.run(test())
"

Configuration Customization

• Server Config: Modify config/mcp_config.json
• Agent Behavior: Adjust max_steps in client code
• UI Styling: Customize Streamlit interface

🎓 Learning Objectives

This workshop demonstrates:

1. MCP Protocol Implementation: Client-server communication
2. AI Agent Integration: LLM + Tool selection
3. Database Operations: CRUD with intelligent querying
4. Web Interface Development: Real-time chat applications
5. Error Handling: Robust production patterns
6. Configuration Management: Environment-based setup

📚 Additional Resources

• Detailed Documentation:
  • Server README - Complete server API reference
  • Client README - UI and integration details
• MCP Specification: Model Context Protocol
• FastMCP Framework: FastMCP Documentation
• Streamlit: Streamlit Documentation

🤝 Contributing

1. Extend Tools: Add new MCP server tools for additional functionality
2. Enhance UI: Improve the Streamlit interface with new features
3. Add Data Sources: Integrate additional movie databases
4. Optimize Performance: Implement caching and query optimization
5. Documentation: Update READMEs with new features