Provides example implementations for building agentic AI solutions with AWS, utilizing the Model Context Protocol.
Sponsored
This collection offers various practical examples demonstrating how to construct Agentic AI applications using AWS services, specifically focusing on the Model Context Protocol (MCP). It includes diverse configurations, such as client-server interactions over SSE and stdio, deployments on Amazon ECS, and integrations with technologies like Spring AI, FastAPI, Anthropic Bedrock, and pgVector for RAG capabilities.
Key Features
01Spring AI Agent and MCP Server examples running on ECS
02Full client-server MCP/SSE demo deployable in Docker containers
0359 GitHub stars
04Local MCP/stdio client-server demo for Python
05Bedrock Converse Client with pgVector RAG and Spring AI MCP Server
06FastAPI client with Anthropic Bedrock and MCP SSE Server on ECS Fargate
Use Cases
01Developing agentic AI applications with Amazon Bedrock
02Integrating RAG (Retrieval Augmented Generation) into agentic AI workflows
03Learning and implementing the Model Context Protocol (MCP) on AWS
