A robust, lightweight Model Context Protocol (MCP) server designed to empower your AI Agents with context-awareness, safe execution sandboxes, and dedicated thought logs.
An educational implementation of a Model Context Protocol server that demonstrates how to build a functional MCP server integrating with various LLM clients.
A hands-on demonstration project that teaches the Model Context Protocol (MCP) through Python code, allowing users to understand how AI models interact with their context through a provider-agent architecture.
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.
An educational implementation of a Model Context Protocol server that demonstrates how to build a functional MCP server for integrating with various LLM clients like Claude Desktop.
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.