What are the runtime dependencies?
Python 3.13 or later, a Kafka cluster (local or AWS MSK), and the Confluent Kafka Python client.
@never2average
基本信息
暂无概览
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"a2a-mcp-server": {
"command": "python",
"args": [
"main.py"
]
}
}
}工具列表暂未提供
当前收录暂时没有可展示的工具列表,不代表服务没有工具。请查阅项目文档了解支持的能力。
工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
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.
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.
Python 3.13 or later, a Kafka cluster (local or AWS MSK), and the Confluent Kafka Python client.
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.
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`.
Run `python kafka_test_data.py` to generate sample tasks and notifications for demonstration.
The project is distributed under the MIT License.

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