# LLM Deployment Guide for MCP Server
## Overview
This guide covers the deployment of Large Language Models (LLMs) using the Model Context Protocol (MCP) server. The MCP server provides a standardized interface for interacting with various LLM providers.
## Supported LLM Providers
### OpenAI
- Models: GPT-3.5, GPT-4, GPT-4o
- API Key Required: Yes
- Environment Variable: OPENAI_API_KEY
### Anthropic
- Models: Claude 3 Sonnet, Claude 3 Opus
- API Key Required: Yes
- Environment Variable: ANTHROPIC_API_KEY
### Google Gemini
- Models: Gemini Pro
- API Key Required: Yes
- Environment Variable: GEMINI_API_KEY
## Configuration
### Environment Variables
Create a `.env` file with the following variables:
```env
# OpenAI
OPENAI_API_KEY=your_openai_api_key
# Anthropic
ANTHROPIC_API_KEY=your_anthropic_api_key
# Google Gemini
GEMINI_API_KEY=your_gemini_api_key
# MCP Server
MCP_HOST=localhost
MCP_PORT=8000
```
### Model Selection
Configure the default model in your MCP server:
```python
DEFAULT_MODEL = "claude-3-sonnet-latest" # or your preferred model
```
## Deployment Steps
### 1. Local Deployment
```bash
# Install dependencies
pip install -r requirements.txt
# Start MCP server
python mcp_server.py
```
### 2. Docker Deployment
```bash
# Build Docker image
docker build -t mcp-llm-server .
# Run container
docker run -p 8000:8000 \
-e OPENAI_API_KEY=your_key \
-e ANTHROPIC_API_KEY=your_key \
-e GEMINI_API_KEY=your_key \
mcp-llm-server
```
## API Usage
### 1. Server Status
```bash
curl http://localhost:8000/status
```
### 2. Model Execution
```bash
curl -X POST http://localhost:8000/execute \
-H "Content-Type: application/json" \
-d '{
"tool": "llm_query",
"params": {
"model": "claude-3-sonnet-latest",
"prompt": "Your prompt here",
"max_tokens": 1000
}
}'
```
## Best Practices
### 1. Rate Limiting
- Implement rate limiting to prevent API abuse
- Monitor API usage and costs
- Use appropriate model sizes for tasks
### 2. Error Handling
- Implement retry logic for transient failures
- Log errors for debugging
- Provide fallback models
### 3. Security
- Keep API keys secure
- Use environment variables
- Implement authentication for API endpoints
## Monitoring
### 1. Logging
```python
import logging
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
```
### 2. Metrics
- Track API call success rates
- Monitor response times
- Log token usage
## Troubleshooting
### Common Issues
1. API Key Errors
- Verify API keys are correctly set
- Check for key expiration
- Ensure proper environment variable names
2. Rate Limit Errors
- Implement exponential backoff
- Monitor usage patterns
- Consider upgrading API tier
3. Model Availability
- Have fallback models ready
- Monitor provider status pages
- Implement graceful degradation
## Support
For issues and support:
1. Check the MCP documentation
2. Open GitHub issues
3. Contact support team
## Additional Resources
- [MCP Documentation](https://smithery.ai/docs)
- [OpenAI API Guide](https://platform.openai.com/docs)
- [Anthropic API Guide](https://docs.anthropic.com)
- [Google Gemini API Guide](https://ai.google.dev/docs)
