MCP Server Demo

What is MCP Server Demo?

MCP Server Demo is a production-ready task management system built with MCP (Model Control Protocol) and Kafka. It enables AI agents to interact with a Kafka-based task queue for task creation, updates, completion, and real-time notification handling.

How to use MCP Server Demo?

Install dependencies with pip install -e ., configure Kafka cluster details in kafka_config.py, and start the server with python main.py. Optionally load test data using python kafka_test_data.py. AI agents interact via exposed MCP tools for tasks and notifications.

Key features of MCP Server Demo

• Task management: create, update, prioritize, complete tasks.
• Real-time notification system with priority levels.
• Kafka integration for reliable message queuing and event streaming.
• AI-friendly MCP tools for task and notification operations.
• Background consumer services for processing Kafka messages.

Use cases of MCP Server Demo

• AI agents manage production tasks via MCP tools.
• Real-time notification processing for task updates.
• Background task queue processing with Kafka consumers.
• Automated task prioritization and completion workflows.

FAQ from MCP Server Demo

What are the runtime dependencies?

Python 3.13 or later, a Kafka cluster (local or AWS MSK), and the Confluent Kafka Python client.

How do I configure the Kafka connection?

Update the KAFKA_CONFIG dictionary in kafka_config.py with your bootstrap servers, security protocol (e.g., SASL_SSL), SASL mechanism (e.g., SCRAM-SHA-512), username, and password.

What MCP tools are exposed?

Task management tools: fetch_queue, change_task_priority, pickup_task, complete_task, get_task_details, check_task_status. Notification tools: check_notification_count, get_notification_list, mark_notification_as_read.

How can I populate test data?

Run python kafka_test_data.py to generate sample tasks and notifications for demonstration.

What license is used?

The project is distributed under the MIT License.