MCP Server with LLM Integration

# MCP Server A Model Context Protocol (MCP) server implementation with LLM integration and chat memory capabilities. ## Features - **MCP Server**: Full Model Context Protocol server implementation - **LLM Integration**: Support for OpenAI and Anthropic models - **Chat Memory**: Persistent conversation storage and retrieval - **Tool System**: Extensible tool framework for various operations ## Installation 1. Clone this repository: ```bash git clone <repository-url> cd MCP ``` 2. Install dependencies: ```bash pip install -r requirements.txt ``` Or using the development environment: ```bash pip install -e .[dev] ``` ## Configuration Set up your API keys as environment variables: ```bash export OPENAI_API_KEY="your-openai-api-key" export ANTHROPIC_API_KEY="your-anthropic-api-key" ``` Or create a `.env` file: ```env OPENAI_API_KEY=your-openai-api-key ANTHROPIC_API_KEY=your-anthropic-api-key ``` ## Usage ### Running the MCP Server Start the server using the command line: ```bash python -m mcp ``` Or run directly: ```bash python mcp.py ``` ### Available Tools The server provides the following tools: #### Echo Tool Simple echo functionality for testing. ```json { "name": "echo", "arguments": { "text": "Hello, world!" } } ``` #### Chat Memory Tools **Store Memory** ```json { "name": "store_memory", "arguments": { "conversation_id": "conv_123", "content": "User preferences: dark mode enabled", "metadata": {"type": "preference"} } } ``` **Get Memory** ```json { "name": "get_memory", "arguments": { "conversation_id": "conv_123" } } ``` #### LLM Chat Tool ```json { "name": "llm_chat", "arguments": { "message": "What is the capital of France?", "model": "gpt-3.5-turbo" } } ``` ### Supported Models **OpenAI Models:** - gpt-3.5-turbo - gpt-4 - gpt-4-turbo - gpt-4o **Anthropic Models:** - claude-3-haiku-20240307 - claude-3-sonnet-20240229 - claude-3-opus-20240229 ## Development ### Running Tests ```bash pytest ``` ### Code Formatting ```bash black . isort . ``` ### Type Checking ```bash mypy . ``` ## Architecture ### Components - **mcp.py**: Main MCP server implementation and tool registration - **llmintegrationsystem.py**: LLM provider integration and chat completions - **chatmemorysystem.py**: Persistent conversation storage with SQLite ### Database Schema The chat memory system uses SQLite with two main tables: - `memories`: Individual conversation messages and metadata - `conversation_summaries`: Conversation overviews and statistics ## Contributing 1. Fork the repository 2. Create a feature branch 3. Make your changes 4. Add tests for new functionality 5. Submit a pull request ## License MIT License - see LICENSE file for details. ## Troubleshooting ### Common Issues **API Key Errors** Ensure your API keys are properly set in environment variables. **Database Permissions** The server creates a `chat_memory.db` file in the current directory. Ensure write permissions. **Port Conflicts** The MCP server uses stdio communication by default. No port configuration needed. ### Logging Enable debug logging: ```bash PYTHONPATH=. python -c "import logging; logging.basicConfig(level=logging.DEBUG); import mcp; mcp.main()" ```