MCP Server Demo

A production-ready task management system built with MCP (Model Control Protocol) and Kafka.

Overview

This project demonstrates a robust task management system using MCP to enable AI agents to interact with a Kafka-based task queue. The system allows for:

Task management (creating, updating, completing tasks)
Notification handling
Real-time event processing via Kafka

Features

Task Management: Create, update, prioritize, and complete production tasks
Notification System: Real-time notifications with priority levels
Kafka Integration: Reliable message queuing and event streaming
MCP Tools: AI-friendly interfaces for task and notification operations
Consumer Services: Background processing of Kafka messages

Requirements

Python 3.13+
Kafka cluster (local or AWS MSK)
Confluent Kafka Python client

Installation

# Clone the repository
git clone https://github.com/yourusername/mcp-server-demo.git
cd mcp-server-demo

# Install dependencies
pip install -e .

Configuration

Update the Kafka configuration in kafka_config.py with your actual Kafka cluster details:

KAFKA_CONFIG = {
    'bootstrap.servers': 'your-kafka-bootstrap-servers',
    'security.protocol': 'SASL_SSL',
    'sasl.mechanisms': 'SCRAM-SHA-512',
    'sasl.username': 'your-username',
    'sasl.password': 'your-password',
}

Usage

Starting the Server

python main.py

Loading Test Data

To populate the system with sample tasks and notifications:

python kafka_test_data.py

MCP Tools

The system exposes the following MCP tools for AI agents:

Task Management

fetch_queue: Get a list of pending tasks
change_task_priority: Update task priority
pickup_task: Mark a task as in progress
complete_task: Mark a task as completed
get_task_details: Get detailed information about a task
check_task_status: Check the current status of a task

Notification Management

check_notification_count: Get count of unread notifications
get_notification_list: Get a filtered list of notifications
mark_notification_as_read: Mark a notification as read

Architecture

The system consists of several components:

MCP Server: Exposes tools for AI agents to interact with the system
Kafka Producers: Send messages to Kafka topics
Kafka Consumers: Process messages from Kafka topics
Task Service: Business logic for task management
Notification Service: Business logic for notification handling

Development

Project Structure

mcp-server-demo/
├── main.py                # MCP server initialization
├── kafka_config.py        # Kafka configuration
├── consumer_service.py    # Kafka consumer services
├── task_service.py        # Task management logic
├── notification_service.py # Notification handling logic
└── kafka_test_data.py     # Test data generator

License

MIT License

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.