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Sample Model Context Protocol Demos
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Sample Model Context Protocol Demos

This project provides a series of example modules using the AWS Model Context Protocol (MCP), covering multiple languages and technology stacks, including TypeScript, Python, Spring AI, etc., demonstrating the application of MCP in scenarios such as client-server communication, ECS deployment, and RAG integration.
Artificial intelligence chatbotsDeveloper tools#MCP protocol#AWS integration#Multi-language examples#ECS deployment.TypeScript
2.5 points
7.0K
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Overview

Content Details

Alternatives

What is the MCP server?

The MCP server is middleware that follows the Model Context Protocol standard. It serves as a bridge connecting AI clients (such as Amazon Bedrock) and business logic servers, supporting two-way communication and context management.

How to use the MCP server?

Through the standardized protocol interface, developers can quickly integrate AI capabilities into existing systems, supporting containerized deployment and local development modes.

Applicable scenarios

It is suitable for scenarios where large language models need to be integrated into business systems, such as AI-enhanced applications like intelligent customer service, document processing, and appointment management.

Main features

Multi-protocol support
Supports both Server-Sent Events (SSE) and standard input/output (stdio) communication protocols simultaneously.
Cloud platform integration
Provides an AWS ECS deployment template for seamless docking with Amazon Bedrock.
Multi-language implementation
Provides examples in multiple languages such as TypeScript, Python, Java/Kotlin, etc.
RAG enhancement
Some examples include the Retrieval Augmented Generation (RAG) function implemented by pgVector.
Advantages
The standardized protocol ensures compatibility between different components.
Flexible deployment options (local/container/cloud)
Abundant example code accelerates the development process.
Supports integration with mainstream AI service platforms
Limitations
Infrastructure knowledge is required for production deployment.
Some advanced features depend on specific cloud services.
Beginners may need time to understand the protocol details.

How to use

Select the implementation language
Choose the TypeScript, Python, or Java/Kotlin version according to your technology stack.
Configure the environment
Install the corresponding language environment and configure AWS credentials (if you need to use Bedrock).
Run the example
Start the client and server components according to the module instructions.

Usage cases

Intelligent customer service system
A customer service agent implemented using Spring AI to handle user inquiries and manage conversation states.
Pet adoption assistant
A dog adoption advisor based on RAG to retrieve matching pet information from the knowledge base.

Frequently Asked Questions

How is the MCP protocol different from ordinary API calls?
Is it necessary to use AWS services?
How to extend custom functions?

Related resources

MCP protocol specification document
The complete technical specification of the protocol
GitHub repository
All example code and deployment templates
AWS Bedrock documentation
The official guide of the Amazon Bedrock service

Installation

Copy the following command to your Client for configuration
Note: Your key is sensitive information, do not share it with anyone.

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