mcp-knowledgebase-llm

# MCP Knowledge Base A simple MCP client-server ## Requirements - Python 3.9 or higher - Poetry for dependency management - OpenAI API key ## Setup 1. Install dependencies using Poetry: ```bash poetry install ``` 2Create a `.env` file in the project root or parent directory with your OpenAI API key: ``` OPENAI_API_KEY=your_api_key_here ``` ## Project Structure - `server.py`: MCP server implementation with tools - `client-sse.py`: MCP client implementation with LLM capabilities - `data/kb.json`: Knowledge base data with MCP-related Q&A - `pyproject.toml`: Poetry configuration file ## Running the Application 1. Start the server: ```bash poetry run python server.py ``` 2. In a separate terminal, run the client: ```bash poetry run python client-sse.py ``` ## Using the Client The client has two modes: 1. Direct tool calls: - Uncomment the `asyncio.run(test_direct_tool_calls())` line in `client-sse.py` - This directly calls the tools without using an LLM 2. LLM-powered interactions (default): - Uses OpenAI to interpret queries and call appropriate tools - Ask questions like "What is MCP?" or "What is the difference between stdio and SSE transports?" ## Customizing - Add new tools to `server.py` by creating additional functions with the `@mcp.tool()` decorator - Modify the knowledge base by updating `data/kb.json` - Change the OpenAI model by modifying the `model` parameter in the `MCPClient` class