Chao Gong created GSOC-293:
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Summary: [GSOC][HertzBeat] AI Agent Based on the MCP Protocol for
Monitoring Info Interaction
Key: GSOC-293
URL: https://issues.apache.org/jira/browse/GSOC-293
Project: Comdev GSOC
Issue Type: Task
Reporter: Chao Gong
Website: https://hertzbeat.apache.org/
Github: http://github.com/apache/hertzbeat/
**Background**
Apache HertzBeat is an open-source real-time monitoring tool that supports a
wide range of monitoring targets, including web services, databases,
middleware, and more. It features high performance, scalability, and security.
With the advancement of artificial intelligence (AI) technologies, integrating
AI with monitoring systems can significantly enhance their usability and
interactivity. By developing an AI Agent based on the Model Context Protocol
(MCP), we aim to enable conversational interaction for querying monitoring
information, adding new monitoring tasks, and retrieving monitoring metrics.
This will provide a more user-friendly and intelligent monitoring management
experience.
**Objectives**
1. Research and Implementation: Develop an AI Agent based on Apache HertzBeat
and the MCP protocol to enable conversational interaction with users.
2. Functional Implementation:
- Query Monitoring And Alarm Information: Allow users to query the status of
monitoring targets (e.g., normal, abnormal) and retrieve metrics data (e.g.,
CPU usage, memory usage, response time), alarm data through conversational
commands.
- Add New Monitoring Tasks: Enable users to add new monitoring targets (e.g.,
web services, databases, middleware) and configure alert thresholds via
conversational commands.
- Retrieve Monitoring Metrics Data: Allow users to obtain metrics data for
specific monitoring targets and support data visualization via conversational
commands.
**Requirements Analysis**
- Apache HertzBeat: As the core backend for the monitoring system, it provides
functions for data collection, storage, and management.
- MCP Protocol: An open protocol that enables seamless integration between LLM
applications and external data sources and tools.
- Front-end Interaction: Develop a user-friendly interface that supports voice
or text input and displays monitoring information and interaction results.
**Recommended Skills**
- Java + TypeScript: Apache HertzBeat is developed based on this technology
stack. Therefore, mastering these technologies is crucial for integrating with
HertzBeat.
- SpringAi: It is recommended to use SpringAi to build the AI agent.
- LLM + MCP: You need to have an understanding of LLM (Large Language Models)
and the MCP protocol. SpringAi seem supports the MCP protocol or consider use
the mcp-sdk directly.
**Size**
- Difficulty: Hard
- Project size: ~350 hours
**Potential Mentors**
- Chao Gong: gongc...@apache.org
- Shenghang Zhang: shengh...@apache.org
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