# Zephyr MCP Agent Best Practices Guide
## Table of Contents
1. [Overview](#overview)
2. [Installation and Configuration](#installation-and-configuration)
3. [Performance Optimization](#performance-optimization)
4. [Security Considerations](#security-considerations)
5. [Troubleshooting](#troubleshooting)
6. [Deployment Strategies](#deployment-strategies)
7. [LLM Integration Best Practices](#llm-integration-best-practices)
8. [Frequently Asked Questions](#frequently-asked-questions)
---
## Overview
Zephyr MCP Agent is a powerful modular control plane system for managing and executing various tools. This document provides best practices for using Zephyr MCP Agent to help you make the most of its capabilities and avoid common pitfalls.
### Core Values
- **Unified Interface**: Provides a unified HTTP API interface to manage various tools
- **Modular Design**: Flexible tool registration and discovery mechanisms
- **Reliability**: Comprehensive error handling and health check functions
- **Extensibility**: Supports LLM integration and custom tool development
---
## Installation and Configuration
### Environment Requirements
- Python 3.7 or higher
- Dependencies: Install via `pip install -r requirements.txt`
- Sufficient system resources (depending on the tools running and concurrent requests)
### Configuration File Best Practices
The configuration file is a crucial part of using Zephyr MCP Agent. Here are some configuration recommendations:
```json
{
"agent_name": "zephyr_mcp_agent",
"version": "1.0.0",
"description": "Zephyr MCP Agent",
"tools_directory": "./src/tools",
"utils_directory": "./src/utils",
"log_level": "INFO",
"port": 8000,
"host": "localhost",
"llm": {
"enabled": true,
"provider": "your_llm_provider",
"api_key": "${LLM_API_KEY}" // Use environment variables for sensitive information
}
}
```
**Best Practices:**
1. **Use environment variables for sensitive information**: Avoid hardcoding API keys and credentials in the configuration file
2. **Choose appropriate log level**: Use DEBUG for development, INFO or WARN for production
3. **Set appropriate port**: Avoid using system reserved ports
4. **Clearly specify tool directory**: Ensure tools can be loaded correctly
---
## Performance Optimization
### Tool Call Optimization
1. **Parameter Validation**: Validate parameters on the client side to reduce invalid requests
2. **Batch Processing**: Process operations in batches whenever possible to reduce API call frequency
3. **Asynchronous Calls**: Use asynchronous calls for long-running operations
### Server Performance Tuning
1. **Increase Concurrency**: Adjust the number of concurrent connections for the HTTP server
2. **Use Caching**: Consider implementing caching for frequently accessed data
3. **Monitor Resource Usage**: Regularly check CPU, memory, and network usage
---
## Security Considerations
### Authentication and Authorization
1. **Implement API Key Authentication**: Add key verification for API access
2. **Restrict IP Access**: Limit allowed IP addresses in production environments
3. **Use HTTPS**: Encrypt communication using HTTPS in production environments
### Data Security
1. **Sensitive Data Handling**: Avoid logging sensitive information
2. **Input Validation**: Strictly validate all input parameters to prevent injection attacks
3. **Principle of Least Privilege**: The user running the Agent should have only necessary permissions
---
## Troubleshooting
### Common Issues and Solutions
| Issue | Possible Cause | Solution |
|-------|----------------|----------|
| Tool execution failure | Parameter error or environment issue | Check parameter format and environment settings, view detailed logs |
| Server startup failure | Port conflict | Modify the port number in the configuration file or stop the process using the port |
| LLM integration failure | API key error or network issue | Verify API key and network connection, check LLM provider status |
| Performance degradation | Insufficient resources or too many concurrent requests | Increase server resources, implement request queue or rate limiting |
### Log Analysis
Using the `trace_id` feature for issue tracking is a powerful method:
1. **Include trace_id in requests**:
```bash
curl -X POST http://localhost:8000/api/tool -H 'Content-Type: application/json' -H 'X-Trace-ID: your-trace-id' -d '{
"tool": "your_tool_name",
"params": {}
}'
```
2. **Find logs by trace_id**: Search for specific trace_id in log files to track the complete request flow
---
## Deployment Strategies
### Development Environment Deployment
```bash
# Clone repository
git clone https://github.com/your-org/zephyr_mcp.git
cd zephyr_mcp
# Install dependencies
pip install -r requirements.txt
# Start Agent (development mode)
python agent.py --config config.dev.json
```
### Production Environment Deployment
1. **Use System Service**: Configure Agent as a system service to ensure automatic startup and restart
**Using systemd (Linux)**:
```ini
[Unit]
Description=Zephyr MCP Agent
After=network.target
[Service]
User=zephyr-user
WorkingDirectory=/path/to/zephyr_mcp
Environment="LLM_API_KEY=your_api_key"
ExecStart=/usr/bin/python agent.py --config config.prod.json
Restart=always
[Install]
WantedBy=multi-user.target
```
2. **Containerized Deployment**: Deploy using Docker containers
**Dockerfile Example**:
```dockerfile
FROM python:3.9-slim
WORKDIR /app
COPY . /app/
RUN pip install --no-cache-dir -r requirements.txt
EXPOSE 8000
CMD ["python", "agent.py"]
```
3. **Load Balancing**: For high-traffic scenarios, consider using a load balancer to distribute requests
---
## LLM Integration Best Practices
### Configuration and Usage
1. **Selective Enablement**: Enable LLM integration as needed to avoid unnecessary resource consumption
2. **API Key Management**: Use environment variables or secret management services to store API keys
3. **Request Limiting**: Implement request rate limiting to avoid exceeding API quotas
### Prompt Engineering
When using LLM integration, well-designed prompts can improve response quality:
1. **Be Specific**: Provide clear and specific instructions
2. **Contextual Relevance**: Include necessary context information
3. **Structured Output**: Request structured output formats from LLMs
---
## Frequently Asked Questions
### 1. How to customize tools?
**Question**: I need to add custom tools to the Agent, how should I implement it?
**Answer**:
1. Create a new Python module in the `tools_directory`
2. Implement the tool function, ensuring proper docstrings and parameter type hints
3. Ensure the function returns results in a standard format (usually a dictionary)
4. Restart the Agent, which will automatically discover and register the new tool
### 2. How to monitor Agent performance?
**Question**: I want to monitor the performance and health status of the Agent, what methods are available?
**Answer**:
1. Use the `--health-check` command for regular health checks
2. Configure appropriate log levels to record performance metrics
3. Consider integrating external monitoring tools like Prometheus and Grafana
4. Implement custom health check endpoints to expose key performance indicators
### 3. How to handle large-scale concurrent requests?
**Question**: When there are a large number of concurrent requests, the Agent performance degrades, how should it be optimized?
**Answer**:
1. Implement request queue mechanism to avoid system overload
2. Increase server resources (CPU, memory)
3. Use multi-process or multi-threading to process requests
4. Implement caching mechanism to reduce duplicate calculations
5. Consider deploying multiple Agent instances with load balancing
---
## Conclusion
By following the best practices in this guide, you can make the most of Zephyr MCP Agent's capabilities to build a reliable, efficient, and secure tool management system. Remember to continuously monitor system performance, regularly update configurations, and adjust strategies based on actual usage conditions.
---
*Last updated: December 2023*
