Collection of examples of how to use Model Context Protocol with AWS.
pythontypescriptawsAPI Integration
Agentic AI Samples
Collection of examples of how to build Agentic AI with AWS, including Model Context Protocol.
List of modules:
| Module | Lang | Description |
|---|---|---|
| Server Client MCP/SSE Demo | TypeScript | This full demo creates an Amazon Bedrock MCP client using the converse API and MCP server. The sample is deployed in containers that connect over MCP/SSE. |
| Server Client MCP/stdio Demo | Python | This is a demo Amazon Bedrock MCP client using the converse API and a simple MCP stdio server. The sample runs locally connected with Amazon Bedrock. |
| Server Client MCP/SSE on ECS | Spring AI & Kotlin | Provides a sample Spring AI MCP Server that runs on ECS; which is used by a Spring AI Agent using Bedrock; which also runs on ECS and is exposed publicly via a Load Balancer. |
| Server Client MCP/SSE in Bedrock Converse Client w/ pgVector RAG | Spring AI & Java | A Spring AI dog adoption agent built on Bedrock using PostgreSQL with pgvector for RAG, and an MCP Server for managing adoption appointments. |
| Server MCP/SSE on ECS | Spring AI & Kotlin | Very basic Spring AI MCP Server over SSE running on ECS. |
| MCP/SSE Server - FastAPI Client with Anthropic Bedrock | Python | An MCP SSE server with a FastAPI client that leverages Anthropic Bedrock. The sample runs on ECS Fargate with public access through an Application Load Balancer. |
Security
See CONTRIBUTING for more information.
License
This library is licensed under the MIT-0 License. See the LICENSE file.
Install
No configuration available
For more configuration details, refer to the content on the left
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