Azure-Samples/mcp-agent-langchainjs

Sample agent that orders burgers

Before you install: no copy-paste command · no license recorded · link reachable

First step: 3. Open a terminal and run this command to clone the repo: git clone

View on GitHub ↗Link checked 2026-11-12 · HTTP 200 · opens the vendor's own site

Install & config

Client config (built from the install command this repo gives)

{
  "mcpServers": {
    "azure-samples-mcp-agent-langchainjs": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/inspector"]
    }
  }
}

Parsed from the README we fetched on 2026-11-05; we have not executed it.

command/args come from the install command the repository states. When it is ambiguous, the project README wins.

VS Code (.vscode/mcp.json - the key is servers)

{
  "servers": {
    "azure-samples-mcp-agent-langchainjs": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/inspector"]
    }
  }
}

Same server, but VS Code keeps it under <code>servers</code>.

Claude Code (command line)

claude mcp add --transport stdio azure-samples-mcp-agent-langchainjs -- npx -y @modelcontextprotocol/inspector

Claude Code does not read a config file; add the server with this command.

Key names differ by client: Clients & CLI

Source: collected from the abordage/awesome-mcp list · type: MCP server
Harvest history: first seen 2026-11-05 · last seen 2026-11-14 · seen 10× · listed by 10 sources
Score: score 1,003 · 10 sources · mcp
Install: npx -y @modelcontextprotocol/inspector

FAQ (answers come from our records)

How do I install it?

The repository provides this install command: npx -y @modelcontextprotocol/inspector. It comes from the repository itself; we cannot guarantee it runs as-is in your client.

Who is it for?

We filed it under these tags: MCP 服务. The tags come from the entry itself, not a taxonomy we imposed.

Who maintains it?

first catalogued 2026-11-05；last seen 2026-11-14。There is no official certification or quality rating here — we record sources and dates only.

# 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:

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[![Listed on Anclum](https://anclum.com/badge/listed.svg)](https://anclum.com/en/s/azure-samples-mcp-agent-langchainjs/)

Source: awesome · https://github.com/Azure-Samples/mcp-agent-langchainjs
Browse by field: MCP servers
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added: today

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