这是一个演示项目，展示了使用 FastMCP 实现模型上下文协议（MCP），示例包括 stdio 和 HTTP 传输、与 LangChain 和 Agent Framework 的集成，以及部署到 Azure 容器应用。

目录

• 开始
• GitHub代码空间
• VS Code 开发容器
• 局部环境
• 运行本地MCP服务器
• 与GitHub Copilot一起使用
• 使用VS代码调试
• 使用 MCP 检查器进行检查
• 使用 Aspire 仪表盘查看跟踪
• 运行本地代理MCP
• 部署到 Azure
• 部署到带有私有网络的 Azure
• 部署到 Azure 并使用 Keycloak 认证
• 使用 Entra OAuth 代理部署到 Azure
• 资源

开始

你有几个设置这个项目的选项。最快的入门方式是GitHub Codespaces，因为它会帮你设置所有工具，但你也可以本地搭建。

GitHub 代码空间

您可以通过GitHub Codespaces虚拟运行该项目。点击下方按钮之一，在浏览器中打开基于网页的VS Code实例：

Default （Azure OpenAI）:

Ollama（本地型号，需要64GB+内存）:

Python MCP Demo

A demonstration project showcasing Model Context Protocol (MCP) implementations using FastMCP, with examples of stdio and HTTP transports, integration with LangChain and Agent Framework, and deployment to Azure Container Apps.

Table of Contents

• Getting started
  • GitHub Codespaces
  • VS Code Dev Containers
  • Local environment
• Run local MCP servers
  • Use with GitHub Copilot
  • Debug with VS Code
  • Inspect with MCP inspector
  • View traces with Aspire Dashboard
• Run local Agents MCP
• Deploy to Azure
• Deploy to Azure with private networking
• Deploy to Azure with Keycloak authentication
• Deploy to Azure with Entra OAuth Proxy
• Resources

Getting started

You have a few options for setting up this project. The quickest way to get started is GitHub Codespaces, since it will setup all the tools for you, but you can also set it up locally.

GitHub Codespaces

You can run this project virtually by using GitHub Codespaces. Click one of the buttons below to open a web-based VS Code instance in your browser:

Default (Azure OpenAI):

Ollama (local models, requires 64GB+ memory):

The Ollama Codespace pre-installs Ollama and pulls the gemma4:e2b model, and copies .env.sample.ollama as your .env file. Note that the 64GB memory requirement will consume your Codespace quota faster.

Once the Codespace is open, open a terminal window and continue with the deployment steps.

VS Code Dev Containers

A related option is VS Code Dev Containers, which will open the project in your local VS Code using the Dev Containers extension:

1. Start Docker Desktop (install it if not already installed)
2. Open the project:
3. In the VS Code window that opens, once the project files show up (this may take several minutes), open a terminal window.
4. Continue with the deployment steps.