Cross-LLM MCP Server

# Cross-LLM MCP Server Usage Examples This document provides examples of how to use the Cross-LLM MCP Server with different MCP clients. ## Configuration ### 1. Set up your environment file Copy the example environment file and add your API keys: ```bash cp env.example .env ``` Edit `.env` with your actual API keys: ```env # OpenAI/ChatGPT API Key OPENAI_API_KEY=sk-your-openai-key-here # Anthropic/Claude API Key ANTHROPIC_API_KEY=sk-ant-your-anthropic-key-here # DeepSeek API Key DEEPSEEK_API_KEY=sk-your-deepseek-key-here # Default models (optional) DEFAULT_CHATGPT_MODEL=gpt-4 DEFAULT_CLAUDE_MODEL=claude-3-sonnet-20240229 DEFAULT_DEEPSEEK_MODEL=deepseek-chat ``` ### 2. Build the server ```bash npm run build ``` ## Usage with Different MCP Clients ### Claude Desktop 1. Open Claude Desktop 2. Go to Settings → Model Context Protocol 3. Add a new server with these settings: - **Name**: Cross-LLM - **Command**: `node` - **Arguments**: `[path-to-your-project]/build/index.js` - **Working Directory**: `[path-to-your-project]` ### Example Conversations #### Single LLM Call **User**: "Call ChatGPT to explain quantum computing" **Claude**: I'll use the Cross-LLM MCP server to call ChatGPT for you. ```json { "tool": "call-chatgpt", "arguments": { "prompt": "Explain quantum computing in simple terms", "temperature": 0.7, "max_tokens": 500 } } ``` #### Multi-LLM Comparison **User**: "Get responses from all LLMs about the future of AI" **Claude**: I'll call all available LLMs to get different perspectives. ```json { "tool": "call-all-llms", "arguments": { "prompt": "What do you think the future of artificial intelligence will look like in the next 10 years?", "temperature": 0.8, "max_tokens": 800 } } ``` #### Specific Model Selection **User**: "Ask Claude to write a poem about technology" **Claude**: I'll call the Claude API specifically for this request. ```json { "tool": "call-claude", "arguments": { "prompt": "Write a short poem about the impact of technology on modern life", "model": "claude-3-sonnet-20240229", "temperature": 0.9, "max_tokens": 300 } } ``` ## Advanced Usage ### Custom Model Selection You can specify different models for each provider: ```json { "tool": "call-llm", "arguments": { "provider": "chatgpt", "prompt": "Explain machine learning algorithms", "model": "gpt-4-turbo", "temperature": 0.5, "max_tokens": 1000 } } ``` ### Temperature Control Adjust creativity vs consistency: - **Low temperature (0.1-0.3)**: More focused, consistent responses - **Medium temperature (0.4-0.7)**: Balanced creativity and consistency - **High temperature (0.8-1.0)**: More creative, varied responses ### Token Management Control response length: - **Short responses**: 100-300 tokens - **Medium responses**: 500-800 tokens - **Long responses**: 1000+ tokens ## Error Handling The server provides clear error messages for common issues: ### Missing API Key ``` **ChatGPT Error:** OpenAI API key not configured ``` ### Network Issues ``` **Claude Error:** Claude API error: Network timeout ``` ### Rate Limiting ``` **DeepSeek Error:** DeepSeek API error: Rate limit exceeded ``` ## Best Practices 1. **Start with individual calls** to test each LLM before using `call-all-llms` 2. **Use appropriate temperature** for your use case 3. **Monitor token usage** to manage costs 4. **Handle errors gracefully** - one LLM failure shouldn't stop your workflow 5. **Compare responses** to understand different model strengths ## Troubleshooting ### Server won't start - Check that all dependencies are installed: `npm install` - Verify the build was successful: `npm run build` - Ensure the `.env` file exists and has valid API keys ### API errors - Verify your API keys are correct and active - Check your API usage limits and billing status - Ensure you're using supported model names ### No responses - Check that at least one API key is configured - Verify network connectivity - Look for error messages in the response ## Integration Examples ### With Claude Desktop The server integrates seamlessly with Claude Desktop, allowing you to: - Call other LLMs while chatting with Claude - Compare responses from different models - Use specialized models for specific tasks ### With Other MCP Clients Any MCP-compatible client can use this server to: - Access multiple LLM providers - Get diverse perspectives on topics - Build more robust AI applications