A
Azure-Samples/mcp-agent-langchainjs
Sample agent that orders burgers
在 GitHub 查看 ↗链接已于 2026-11-12 核查 · HTTP 200 · 打开的是厂商自己的官网
安装与配置
客户端配置（取自该仓库的安装命令）
{
"mcpServers": {
"azure-samples-mcp-agent-langchainjs": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/inspector"]
}
}
}解析自我们于 2026-11-05 抓取的该仓库 README；我们未执行过。
command/args 来自仓库写明的安装命令；不确定时请以该项目 README 为准。
VS Code（.vscode/mcp.json —— 键名是 servers）
{
"servers": {
"azure-samples-mcp-agent-langchainjs": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/inspector"]
}
}
}VS Code 顶层键是 <code>servers</code>，其余与上面那份相同。
Claude Code（命令行）
claude mcp add --transport stdio azure-samples-mcp-agent-langchainjs -- npx -y @modelcontextprotocol/inspector
Claude Code 不收 JSON 文件，用这条命令直接加。
配置项的键名以你的客户端为准：客户端与 CLI
- 来源明细
- 收录自 abordage/awesome-mcp 榜单（我们从中收集的条目之一） · 条目类型：MCP 服务
- 收录与活跃
- 收录于 2026-11-05 · 最近见到 2026-11-14 · 共见到 10 次 · 被 10 个来源列出
- 评分构成
- 综合分 1,003 · 10 个来源 · mcp
- 安装
npx -y @modelcontextprotocol/inspector
常见问题（答案都来自我们的记录）
怎么安装？
该仓库给出的安装命令是
npx -y @modelcontextprotocol/inspector。我们的记录里它是仓库自己提供的；不保证在你的客户端上直接可用。它适合谁 / 用来做什么？
我们把它归在这些标签下: MCP 服务。标签来自条目自身，不是我们强加的分类。
谁维护它、还在更新吗？
我们收录于 2026-11-05；最近见到 2026-11-14。这里没有"官方认证"或"质量评级"——我们只记录来源与时间。
# AI Agent with MCP tools using LangChain.js
以下摘录自该仓库的 README（片段） · 在 GitHub 读完整 README →
⭐ If you like this sample, star it on GitHub — it helps a lot!
Overview • Architecture • Getting started • Deploy to Azure • Run locally • MCP tools • Resources
> [!IMPORTANT]
> 👉 Follow the full-length workshop to learn how we built this sample.
## Overview
This project demonstrates how to build AI agents that can interact with real-world APIs using the Model Context Protocol (MCP). It features a complete burger ordering system with a serverless API, web interfaces, and an MCP server that enables AI agents to browse menus, place orders, and track order status. The agent uses LangChain.js to handle LLM reasoning and tool calling. The system consists of multiple interconnected services, as detailed in the Architecture section below.
The system is hosted on Azure Static Web Apps (web apps) and Azure Functions (API and MCP servers), with Azure Cosmos DB for NoSQL for data storage. You can use it as a starting point for building your own AI agents.
> [!TIP]
> You can test this application locally without deployment needed or any cloud costs. The MCP server also works with popular AI tools like GitHub Copilot, Claude, and other MCP-compatible clients. Follow the instructions in the Local Development section to get started.
### Key features
LangChain.js agent with tool calling via MCP (Streamable HTTP transport)
Multi-service, end‑to‑end architecture (web UIs, APIs, MCP server)
User authentication with sessions history
100% serverless architecture, for cost-effective scaling
Single-command deployment using Infrastructure as Code (IaC)
## Architecture
Building AI applications can be complex and time-consuming, but using LangChain.js and Azure serverless technologies allows to greatly simplify the process. This application is a AI agent that can be access through different interfaces (web app, CLI) and that can call tools through MCP to interact with a burger ordering API.
The application is made from these main components:
| Component | Folder | Purpose |
| Agent Web App | packages/agent-webapp | Chat interface + conversation rendering |
| Agent API | packages/agent-api | LangChain.js agent + chat state + MCP client |
| Burger API | packages/burger-api | Core burger & order management web API |
| Burger MCP Server | packages/burger-mcp | Exposes burger API as MCP tools |
| Burger Web App | packages/burger-webapp | Live orders visualization |
| Infrastructure | infra | Bicep templates (IaC) |
Additionally, these optional and support components are included:
| Component | Folder | Purpose |
| Beer MCP Server | packages/beer-mcp | AI-powered beer recommendations via hybrid RAG |
| Agent CLI | packages/agent-cli | Command-line interface LangChain.js agent and MCP client |
| Data generation | packages/burger-data | Scripts to (re)generate burgers data & images |
## Getting started
There are multiple ways to get started with this project. The quickest way is to use GitHub Codespaces that provides a preconfigured environment for you. Alternatively, you can set up your local environment following the instructions below.
Use GitHub Codespaces
You can run this project directly in your browser by using GitHub Codespaces, which will open a web-based VS Code:
Overview • Architecture • Getting started • Deploy to Azure • Run locally • MCP tools • Resources
> [!IMPORTANT]
> 👉 Follow the full-length workshop to learn how we built this sample.
## Overview
This project demonstrates how to build AI agents that can interact with real-world APIs using the Model Context Protocol (MCP). It features a complete burger ordering system with a serverless API, web interfaces, and an MCP server that enables AI agents to browse menus, place orders, and track order status. The agent uses LangChain.js to handle LLM reasoning and tool calling. The system consists of multiple interconnected services, as detailed in the Architecture section below.
The system is hosted on Azure Static Web Apps (web apps) and Azure Functions (API and MCP servers), with Azure Cosmos DB for NoSQL for data storage. You can use it as a starting point for building your own AI agents.
> [!TIP]
> You can test this application locally without deployment needed or any cloud costs. The MCP server also works with popular AI tools like GitHub Copilot, Claude, and other MCP-compatible clients. Follow the instructions in the Local Development section to get started.
### Key features
LangChain.js agent with tool calling via MCP (Streamable HTTP transport)
Multi-service, end‑to‑end architecture (web UIs, APIs, MCP server)
User authentication with sessions history
100% serverless architecture, for cost-effective scaling
Single-command deployment using Infrastructure as Code (IaC)
## Architecture
Building AI applications can be complex and time-consuming, but using LangChain.js and Azure serverless technologies allows to greatly simplify the process. This application is a AI agent that can be access through different interfaces (web app, CLI) and that can call tools through MCP to interact with a burger ordering API.
The application is made from these main components:
| Component | Folder | Purpose |
| Agent Web App | packages/agent-webapp | Chat interface + conversation rendering |
| Agent API | packages/agent-api | LangChain.js agent + chat state + MCP client |
| Burger API | packages/burger-api | Core burger & order management web API |
| Burger MCP Server | packages/burger-mcp | Exposes burger API as MCP tools |
| Burger Web App | packages/burger-webapp | Live orders visualization |
| Infrastructure | infra | Bicep templates (IaC) |
Additionally, these optional and support components are included:
| Component | Folder | Purpose |
| Beer MCP Server | packages/beer-mcp | AI-powered beer recommendations via hybrid RAG |
| Agent CLI | packages/agent-cli | Command-line interface LangChain.js agent and MCP client |
| Data generation | packages/burger-data | Scripts to (re)generate burgers data & images |
## Getting started
There are multiple ways to get started with this project. The quickest way is to use GitHub Codespaces that provides a preconfigured environment for you. Alternatively, you can set up your local environment following the instructions below.
Use GitHub Codespaces
You can run this project directly in your browser by using GitHub Codespaces, which will open a web-based VS Code:
收录徽章（可选）
想把收录标记放进 README 就复制这行，可选，不影响收录。
[](https://anclum.com/zh/s/azure-samples-mcp-agent-langchainjs/)
- 来源
- awesome · https://github.com/Azure-Samples/mcp-agent-langchainjs
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