Serverless MCP Server: Protein Bar Ordering System

This project implements a serverless Model Context Protocol (MCP) server on AWS Lambda that enables AI assistants to interact with a protein bar ordering system.

Features

• MCP Server with tools for:
  • Listing available protein bars
  • Creating new orders
  • Admin functionality for managing orders
• Serverless Architecture using:
  • AWS Lambda with Express and Lambda Web Adapter
  • API Gateway for HTTP endpoint
  • DynamoDB for data storage
• Stateless Design that scales efficiently

Prerequisites

• Node.js 22 or higher
• AWS CLI configured with appropriate permissions
• AWS CDK installed (npm install -g aws-cdk)

Setup and Deployment

1. Install Dependencies

npm install

2. Build the Project

npm run build

3. Deploy to AWS

npm run cdk bootstrap  # Only needed first time
npm run deploy

The CDK deployment will output:

• The API Gateway URL
• The MCP Server URL (used for client configuration)

4. Seed Initial Data

After deployment, seed the DynamoDB table with initial protein bar data:

# Set your AWS_PROFILE if needed
export PRODUCTS_TABLE=protein_products  # Should match the table name in CDK stack
npm run seed-data

Testing Locally

To run the MCP server locally for testing:

npm run dev

This will start the server on port 3000, and you can send MCP requests to http://localhost:3000/mcp.

Connecting Clients

VS Code (Copilot Agent Mode)

1. Enable GitHub Copilot Chat and Agent Mode in VS Code
2. Create a .vscode/mcp.json file with:

{
  "servers": {
    "ProteinBarMCP": {
      "type": "http",
      "url": "https://your-api-id.execute-api.your-region.amazonaws.com/prod/mcp"
    }
  }
}

3. Reload VS Code and start a conversation with GitHub Copilot
4. You can now use the protein bar tools in your AI interactions

Claude Desktop

As of 2025, Claude Desktop has limited support for remote MCP servers, but you can:

1. Check for the latest Claude updates that might support direct remote connections
2. Alternatively, use a local proxy that forwards requests to your AWS MCP server

Security Considerations

For a production deployment, you should add:

1. API Key authentication for both public and admin endpoints
2. IAM roles with least privilege for the Lambda function
3. VPC configuration if needed
4. Proper error handling and logging

License

MIT

protein-bars-mcp-server