ruslanmv

The Model Context Protocol (MCP) is a standardized way to supply context to large language models (LLMs). Using the MCP Python SDK, you can build servers that expose data (resources), functionality (tools), and interaction templates (prompts) to LLM applications in a secure and modular fashion. In this tutorial, we’ll build a simple MCP server in P

We’ll start with a concise “Hello World” demo, then explore both the Admin UI and HTTP API methods, and finally walk through a complete example where we integrate a Watsonx.ai Agent into your gateway.

watsonx-rag-mcp-serverは、AIと機械学習を活用したデータ処理と分析を行うためのサーバーです。特に、情報を効率的に取得し、生成するための機能を提供します。Pythonで実装されており、ユーザーが独自のデータセットを利用してカスタマイズ可能です。

CLI and Flask‑based web application that transforms plain‑English prompts into production‑ready, multi‑agent AI workflows. It generates native YAML for IBM WatsonX Orchestrate, Python code for CrewAI, CrewAI Flow, LangGraph, or ReAct, and includes a built‑in FastAPI MCP server wrapper for seamless deployment to the MCP Gateway.

This project is an AI engine that brings a single photo to life, creating a realistic "talking head" video from just an audio file. It's built for flexibility, offering two ways to use it: a standard web API for easy integration into any application, and a specialized server that automatically connects to "MCP Gateway" systems.