An educational implementation of a Model Context Protocol server that demonstrates how to build a functional MCP server integrating with various LLM clients.
A foundational implementation of a Model Context Protocol (MCP) server designed for educational purposes. It demonstrates the complete interaction between an LLM, an inference engine, and a client during an agentic call.
This server implements the Model Context Protocol to facilitate meaningful interaction and understanding development between humans and AI through structured tools and progressive interaction patterns.
A Python implementation of the Model Context Protocol that allows applications to provide standardized context for LLMs, enabling creation of servers that expose data and functionality to LLM applications through resources, tools, and prompts.
A Model Context Protocol server implementation built with FastAPI that enables AI agent interactions. Provides a structured foundation for building AI-powered applications with proper data validation and modern Python tooling.