This project demonstrates how to integrate AI capabilities into a typical business application with .NET Platform It showcases a common scenario where business events flow through the system and get processed, stored, and then made available for AI-powered interactions.
This is a hands-on learning project that combines:
- Event-driven architecture with Kafka for processing business data
- AI integration using Model Context Protocol (MCP) to interact with stored data
- Modern development practices with .NET Aspire for orchestration
- .NET Platform features for building AI applicatıon
The demo simulates a simple e-commerce flow where orders are generated, processed, and stored - then made accessible through an AI chat interface that can answer questions about the business data.
The main orchestrator that manages all services and provides a unified development experience with observability and monitoring.
To run the complete demo:
dotnet run --project Demo.HostOpens Aspire dashboard with unified logging, metrics, and service management
Generates mock e-commerce order events to simulate real business activity.
Features:
- Creates realistic order data every 5-10 seconds
- Publishes events to the
order-eventsKafka topic
Processes incoming order events and stores them in the database for later use.
Features:
- Consumes messages from Kafka topics
- Stores order and payment data in PostgreSQL
- Handles business logic like fee calculations
Provides AI tools that can access business data stored in the database.
Features:
- Exposes business data through MCP protocol
- Allows AI to retrieve recent payments
- Caches frequently accessed data for performance
A web interface where users can chat with AI about their business data.
Features:
- Interactive chat interface
- AI can answer questions about business related data such payments
- Connects to external AI models through GitHub Models
┌─────────────────┐
│ 🎛️Demo.Host │
│ (.NET Aspire) │
│ - Orchestration │
│ - Observability │
│ - Configuration │
└─────────────────┘
│
│ manages development environment
▼
─────────────────────────────────────────────────────────────────────────────────────────────────────────
┌─────────────────┐ ┌───────────────────┐ PostgreSQL
│ Producer │ │ Consumer │ ┌─────────────┐
│ │ │ │ │ Orders │
│ - Order Events │ │ - Event Processing│──►│ Table │
│ - Mock Data │ │ - Business Logic │ │ │
│ - Publishing │ │ - Data Storage │ ├─────────────┤
└─────────────────┘ └───────────────────┘ │ Payments │
│ ▲ │ │ Table │
│ │ │ ├─────────────┤
│ │ │ │ Fees │
│ │ │ │ Table │
│ ┌─────────────────┐ │ │ └─────────────┘
│ │ Kafka │ │ │ │
│ │ │ │ │ │
└─────────► │ - Topics │ ──────────┘ │ │
│ - order-events│ │ │
│ │ │ │
└─────────────────┘ │ provides data│
│ │
│ │
│ │
│ │
┌─────────────────┐ │ │
│ MCP.Server │ │ │
│ │◄────────────────────────────────)────────────────┘
│ - Business APIs │ │
│ - Data Caching │ │
│ - MCP Tools │ ┌─────────────────┐ │
└─────────────────┘────────────►│ Cache(Valkey) │◄┘
│ │ │
│ │ │
│ │ │
│ │ │
│ └─────────────────┘
│
│
│ MCP protocol
▼
┌─────────────────┐ External AI
│ MCP.Host(web) │ ┌─────────────────┐
│ │───│ GitHub Models │
│ - Web Interface │ │ │
│ - AI Integration│ │ - LLMs │
│ - Chat Features │ │ │
└─────────────────┘ └─────────────────┘
- Data Generation: The Producer generates realistic order events with some payment info also. And publishes the order event to a
Kafkatopic.
- Event Processing: The Consumer processes events from given Kafka topic and stores business data. Also some mock fancy business operation is done. And all related data is inserted into
PostgreSQLdatabase.
- AI Access: An MCP Server exposes business data through standardized APIs within .NET Platform. It is a remote MCP endpoint. .NET Platform has a template to create a minimal MCP Server:
dotnet new mcpserver
curl -X POST https://localhost:5001/ \
-H 'Content-Type: application/json' \
-H 'Accept: application/json, text/event-stream' \
-H 'MCP-Protocol-Version: 2025-06-18' \
-d '{
"jsonrpc":"2.0",
"id":2,
"method":"tools/list",
"params":{}
}'event: message
data: {"result":{"tools":[{"name":"get_random_number","description":"Generates a random number between the specified minimum and maximum values.","inputSchema":{"type":"object","properties":{"min":{"description":"Minimum value (inclusive)","type":"integer","default":0},"max":{"description":"Maximum value (exclusive)","type":"integer","default":100}}}},{"name":"get_recent_payments","description":"Returns recent payment transactions from the payment system","inputSchema":{"type":"object","properties":{}}}]},"id":2,"jsonrpc":"2.0"}- User Interaction: Users chat with AI through the web interface to get insights about their business data. Based on one of .NET templates:
dotnet new aichatwebFor this demonstration,gpt-4o-miniis used viaGitHub Models
- In this view within a given simple prompt, host application can understand which MCP tool to be invoked and
get_recent_paymentstool is called remotly. - MCP server's
get_recent_paymentsreturns just some data from a database. - Host application uses the result from MCP server to generate more responsive and effective result for given prompt.
This demonstrates a practical approach to building AI-powered business applications using modern development tools and patterns with .NET.