Azure Container Apps - AI & MCP Playground

This project showcases how to use the MCP protocol with OpenAI, Azure OpenAI and GitHub Models. It provides a simple demo terminal application that interacts with a TODO list Agent.
The agent has access to a set of tools provided by the MCP server.

MCP Components

The current implementation consists of three main components:

1. MCP Host: The main application that interacts with the MCP server and the LLM provider. The host instanciates an LLM provider and provides a terminal interface for the user to interact with the agent.
2. MCP Client: The client that communicates with the MCP server using the MCP protocol. The application providers two MCP clients for both HTTP and SSE (Server-Sent Events) protocols.
3. MCP Server: The server that implements the MCP protocol and communicates with the DocumentDB database. The application provides two MCP server implementations: one using HTTP and the other using SSE (Server-Sent Events).
4. LLM Provider: The language model provider (e.g., OpenAI, Azure OpenAI, GitHub Models) that generates responses based on the input from the MCP host.
5. DocumentDB Local: A database used to store the state of the agent and the tools.
6. Tools: A set of tools that the agent can use to perform actions, such as adding or listing items in a shopping list.

flowchart TD
    user(("fa:fa-users User"))
    host["VS Code, Copilot, LlamaIndex, Langchain..."]
    client[MCP SSE Client]
    clientHttp[MCP HTTP Client]
    server([MCP SSE Server])
    serverHttp([MCP HTTP Server])
    agent[Agent]
    AzureOpenAI([Azure OpenAI])
    GitHub([GitHub Models])
    OpenAI([OpenAI])
    
    tools["fa:fa-wrench Tools"]
    db[(DocumentDB Local)]
user --> hostGroup 

    subgraph hostGroup["MCP Host"]

        host -.- client & clientHttp & agent

    end

    

    agent -.- AzureOpenAI & GitHub & OpenAI

    

    client a@ ---> |"Server Sent Events"| server

    clientHttp aa@ ---> |"Streamable HTTP"| serverHttp
subgraph container["ACA Container (*)"]

      server -.- tools

      serverHttp -.- tools

      tools -.- add_todo 

      tools -.- list_todos

      tools -.- complete_todo

      tools -.- delete_todo

    end
add_todo b@ --> db

    list_todos c@--> db

    complete_todo d@ --> db

    delete_todo e@ --> db

    

    %% styles
classDef animate stroke-dasharray: 9,5,stroke-dashoffset: 900,animation: dash 25s linear infinite;

    classDef highlight fill:#9B77E8,color:#fff,stroke:#5EB4D8,stroke-width:2px

    

    class a animate

    class aa animate

    class b animate

    class c animate

    class d animate

    class e animate
class container highlight

MCP Server supported features and capabilities

This demo application provides two MCP server implementations: one using HTTP and the other using SSE (Server-Sent Events). The MCP host can connect to both servers, allowing you to choose the one that best fits your needs.

| Feature | Completed |
| ------------------- | --------- |
| SSE (legacy) | ✅ |
| HTTP Streaming | ✅ |
| AuthN (token based) | wip |
| Tools | ✅ |
| Resources | #3 |
| Prompts | #4 |
| Sampling | #5 |

Quick Start (using Docker)

To get started with this project using Docker, follow the steps below:

1. Clone the repository:

git clone https://github.com/Azure-Samples/azure-container-apps-ai-mcp.git
cd azure-container-apps-ai-mcp

2. Start the Docker containers:

docker-compose up

3. Access the MCP servers using VS Code built-in MPC support, see ./.vscode/mcp.json. All data will be persisted in the DocumentDB Local database. You can use the VS Code extension for DocumentDB to explore the database.

Local development

To get started with this project, follow the steps below:

Prerequisites

- Node.js and npm (version 22 or higher)
- Docker (recommended for running the MCP servers, and DocumentDB Local in Docker)
- An OpenAI compatible endpoint:
- An OpenAI API key
- Or, a GitHub token, if you want to use the GitHub models: https://gh.io/models
- Or, if you are using Azure OpenAI, you need to have an Azure OpenAI resource and the corresponding endpoint.

Installation

1. Clone the repository.
2. Install the dependencies:

npm install --prefix mcp-host
npm install --prefix mcp-server-http
npm install --prefix mcp-server-sse

Configuring LLM providers to use

This sample supports the follwowing LLM providers:

| Provider | Supported API |
| ------------- | ------------------ |
| Azure OpenAI | Responses API |
| OpenAI | Responses API |
| GitHub Models | ChatCompletion API |

Azure OpenAI

> [!NOTE]
> Accessing Azure OpenAI using Managed Identity is not supported when running in a Docker container (locally). You can either run the code locally without Docker or use a different authentication method, such as AZURE_OPENAI_API_KEY key authentication.

In order to use Keyless authentication, using Azure Managed Identity, you need to provide the AZURE_OPENAI_ENDPOINT environment variable in the .env file:

```env
AZURE_OPENAI_ENDPOINT="https://<ai-foundry-openai-project>.openai.azure.com"
MODEL="gpt-4.1"