概要
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.
