Azure Container Apps - AI & MCP Playground

@manekinekko

リポジトリ

基本情報

カテゴリ

クラウドとインフラ

ライセンス

MIT

トランスポート

stdio

作者

manekinekko

Azure Container Apps - AI & MCP Playground について

This project showcases how to use the MCP protocol with Azure OpenAI. It provides a simple example to interact with OpenAI's API seamlessly via an MCP server and client.

#azure #mcp #openai #agents #llm

Azure Container Apps - AI & MCP Playground の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "azure-container-apps-ai-mcp": {
      "command": "docker",
      "args": [
        "compose",
        "up",
        "-d",
        "--build"
      ]
    }
  }
}

Azure Container Apps - AI & MCP Playground のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is Azure Container Apps - AI & MCP Playground?

Azure Container Apps - AI & MCP Playground is a demo terminal application that showcases the Model Context Protocol (MCP) with OpenAI, Azure OpenAI, and GitHub Models. It provides an agent that uses tools from an MCP server to manage a TODO list, and is intended for developers exploring MCP-based agentic workflows.

How to use Azure Container Apps - AI & MCP Playground?

Clone the repository, install dependencies with npm install, configure your LLM provider (OpenAI API key, Azure OpenAI endpoint, or GitHub token) in a .env file, then start the MCP servers (HTTP and SSE) using docker compose up or npm start in separate terminals. Finally, run the MCP host with npm start --prefix mcp-host and interact with the agent through a terminal or VS Code’s built-in MCP support.

Key features of Azure Container Apps - AI & MCP Playground

Two MCP server implementations: HTTP Streaming and SSE (legacy)
Tools to add, list, complete, and delete TODO items
Persistent state via DocumentDB Local database
Support for OpenAI, Azure OpenAI, and GitHub Models as LLM providers
Debug mode via DEBUG=mcp:* environment variable
Runs locally with Docker or directly on Node.js 22+

Use cases of Azure Container Apps - AI & MCP Playground

Learn and prototype MCP-based agent architectures
Test tool calling across multiple LLM providers (OpenAI, Azure OpenAI, GitHub Models)
Demonstrate a TODO list agent with persistent state in a local database
Experiment with both SSE and HTTP streaming MCP transports

よくある質問

What LLM providers are supported?

OpenAI (Responses API), Azure OpenAI (Responses API), and GitHub Models (ChatCompletion API) are supported. Authentication uses API keys or, for Azure OpenAI, Managed Identity (not available in local Docker containers).

How do I set up the MCP servers?

Two servers are provided: `mcp-server-http` and `mcp-server-sse`. They can be started via Docker Compose (`docker compose up`) or manually with `npm start --prefix mcp-server-http` and `npm start --prefix mcp-server-sse` in separate terminals.

Where is data stored?

All agent state and tool data (e.g., TODO items) are persisted in a DocumentDB Local database running in Docker. You can explore it using the VS Code DocumentDB extension.

What transport protocols are supported?

The MCP server supports both SSE (Server-Sent Events) and HTTP Streaming transports. The MCP host scans for both servers and uses whichever is available.

Is authentication supported?

Token-based authentication is a work‑in‑progress (wip). When running locally in Docker, Azure OpenAI access via Managed Identity is not supported; you must use an API key instead.

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