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What a Microsoft MCP server actually is
MCP separates the component that exposes capabilities from the component that uses them. An MCP server publishes tools, resources, or prompts. An MCP client connects to that server and invokes those capabilities. In Microsoft’s examples, GitHub Copilot Chat in Visual Studio Code is the client, while your Node.js, Python, or .NET application is the server.
That is different from Microsoft’s Azure MCP Server, a prebuilt server for Azure resource operations. You configure that package in an mcp.json file and authenticate with credentials available from Azure CLI, Azure Developer CLI, Visual Studio, or Visual Studio Code. It is intended for developer use inside an organization, not as a general-purpose public application backend. Check Microsoft’s current quickstart for the package name and configuration syntax before installing it.
The walkthrough below builds a custom server. It uses Microsoft’s documented Node.js task-management pattern: Express, the MCP TypeScript SDK, and Zod. The exact package versions and Azure workflow can change, so verify the current Microsoft tutorial before copying version-specific commands.
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Choose the implementation path first
| Situation | Recommended route | Typical Azure host |
|---|---|---|
| New standalone service and a TypeScript team | Node.js with Express and the MCP TypeScript SDK | Azure Container Apps |
| Python-first team or existing FastAPI service | Python MCP SDK with FastAPI | Azure Container Apps |
| You already have an ASP.NET Core application | Add ModelContextProtocol.AspNetCore and expose an endpoint such as /api/mcp |
Azure App Service |
| Enterprise remote server for Microsoft Foundry Agent Service | Python Azure Functions template, tested with Functions Core Tools | Azure Functions |
| You need Azure operations without writing a server | Install and configure Microsoft’s Azure MCP Server | Local developer environment |
These are workflow choices, not performance rankings. Select based on your existing language, whether MCP is being added to an application or built separately, the intended client, and your authentication model.
Prerequisites for the Node.js example
- An active Azure subscription.
- Azure CLI 2.62.0 or later. Confirm the current minimum because CLI requirements are volatile.
- Node.js 20 LTS or later.
- Visual Studio Code with the GitHub Copilot extension.
- Docker Desktop is optional for testing a container locally.
You should also have permission to create resources in the target Azure subscription and a source-control workspace for the project.
Build a task-management MCP server with Node.js
1. Create the project
Create a directory, initialize a Node project, and add the runtime packages Microsoft uses in its task-management example:
mkdir mcp-task-server
cd mcp-task-server
npm init -y
npm install @modelcontextprotocol/sdk express zod
npm install --save-dev typescript tsx @types/express @types/nodeAdd a TypeScript configuration and a start script. A minimal package.json script section is:
{
"scripts": {
"dev": "tsx src/server.ts",
"build": "tsc",
"start": "node dist/server.js"
}
}Create the source directory:
mkdir src2. Define tools and transport
The SDK API can evolve, so use the current Microsoft tutorial’s imports and transport setup when you begin. The following illustrates the application shape: an Express endpoint receives MCP requests, and the server registers task tools with validated Zod input.
import express from "express";
import { z } from "zod";
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StreamableHTTPServerTransport } from "@modelcontextprotocol/sdk/server/streamableHttp.js";
const app = express();
app.use(express.json());
const tasks: { id: number; title: string; done: boolean }[] = [];
let nextId = 1;
const mcp = new McpServer({
name: "task-server",
version: "1.0.0"
});
mcp.tool(
"add_task",
"Add a task to the task list",
{ title: z.string().min(1).max(200) },
async ({ title }) => {
const task = { id: nextId++, title, done: false };
tasks.push(task);
return {
content: [{ type: "text", text: JSON.stringify(task) }]
};
}
);
mcp.tool(
"list_tasks",
"List all tasks",
{},
async () => ({
content: [{ type: "text", text: JSON.stringify(tasks) }]
})
);
app.post("/api/mcp", async (req, res) => {
const transport = new StreamableHTTPServerTransport({
sessionIdGenerator: undefined
});
res.on("close", () => transport.close());
await mcp.connect(transport);
await transport.handleRequest(req, res, req.body);
});
const port = Number(process.env.PORT || 3000);
app.listen(port, "0.0.0.0", () => {
console.log(`MCP server listening on ${port}`);
});The exact transport constructor and request-handling method may differ between SDK releases. If TypeScript reports an import or method error, align the code with the current SDK sample rather than disabling type checking.
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3. Run and test locally
- Compile or run in development mode with
npm run dev. - Confirm that the process listens on port 3000 (or the value in
PORT). - Configure a local MCP entry in your Copilot-compatible client using the server command or URL format documented by the current Microsoft tutorial.
- Open Copilot Chat in agent mode and ask it to list tasks, then add a task. Confirm that tool calls produce the expected JSON.
Keep local testing separate from deployment testing. A successful local connection proves that the server and client protocol agree; it does not prove that Azure networking, identity, TLS, or downstream permissions are correct.
Containerize and deploy to Azure Container Apps
1. Add a Dockerfile
FROM node:20-slim AS build
WORKDIR /app
COPY package*.json ./
RUN npm ci
COPY tsconfig.json ./
COPY src ./src
RUN npm run build
FROM node:20-slim
WORKDIR /app
ENV NODE_ENV=production
COPY package*.json ./
RUN npm ci --omit=dev
COPY --from=build /app/dist ./dist
EXPOSE 3000
CMD ["node", "dist/server.js"]Make sure the application binds to 0.0.0.0, not only localhost; otherwise the Container App ingress cannot reach it.
2. Sign in and create Azure resources
az login
az account set --subscription <SUBSCRIPTION_ID>
az group create --name mcp-demo-rg --location <REGION>
az containerapp up
--name mcp-task-server
--resource-group mcp-demo-rg
--location <REGION>
--source .
--ingress external
--target-port 3000The az containerapp up command can create supporting Container Apps resources, but the flags and prerequisites change over time. The current Microsoft tutorial may instead use a Container Apps environment, a registry, and separate deployment commands. Follow its current sequence if the command above is rejected.
3. Verify the deployed endpoint
Record the HTTPS application URL returned by Azure and verify the health of the service through the route your application exposes. If your MCP endpoint is /api/mcp, use that path in the Copilot configuration. Do not expose an unauthenticated production endpoint merely because it worked during a local demonstration.
4. Connect GitHub Copilot Chat
Add the deployed server to the MCP configuration supported by your current VS Code and Copilot extension, then open Copilot Chat in agent mode. Ask a narrowly scoped question such as “List my tasks” and inspect the proposed tool call before approving it. If the client cannot discover the tools, verify the HTTPS URL, path, transport expected by the SDK version, and any required authentication headers.
Python alternative: FastAPI and the MCP Python SDK
Microsoft’s parallel Python tutorial follows the same lifecycle: scaffold a FastAPI application, register tools with the MCP Python SDK, test locally, containerize, deploy to Container Apps, and connect Copilot Chat. Its stated prerequisites are Python 3.10 or later, Azure CLI 2.62.0 or later, VS Code with Copilot, and an active Azure subscription; Docker Desktop remains optional for local container testing.
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Choose Python when your business logic already lives in Python or your team is more comfortable with FastAPI. Keep the same boundaries as the Node example: validate every tool argument, bind the web process to the container interface, test locally before deploying, and configure identity before exposing sensitive operations. Use the current Microsoft Python tutorial for SDK imports and transport code because those APIs can change independently of Azure.
Expose an existing ASP.NET Core application
If MCP should surface functions already implemented in an ASP.NET Core app, adding a separate Node service creates unnecessary duplication. Microsoft’s ASP.NET Core route adds ModelContextProtocol.AspNetCore, exposes an endpoint such as /api/mcp, tests it in Copilot Chat agent mode, and deploys the application to App Service.
This path is appropriate when tools map directly to existing application services, authorization policies, and data access. The tutorial sample intentionally omits input validation and sanitization for simplicity; treat that omission as a teaching shortcut, not a production recommendation.
Use Azure Functions with Microsoft Foundry
For an enterprise remote server consumed by Microsoft Foundry Agent Service, Microsoft documents a Python template named remote-mcp-functions-python. The workflow uses Azure Functions Core Tools for local testing, azd up for deployment, optional registration in Azure API Center, and then adds the server to Foundry Agent Service.
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MCP is an open protocol, so Azure Functions is only one hosting choice. Microsoft also identifies ASP.NET Core, Express.js, and Flask as alternatives. API Center catalog registration is optional; it is useful when your organization needs a discoverable inventory of internal APIs and MCP servers.
Security requirements before production
- Authentication and authorization: require an authenticated caller unless anonymous access is an explicit, reviewed requirement.
- Least privilege: expose only the downstream operations each client needs. A read-only tool should not share credentials that can delete or modify data.
- Validation and sanitization: constrain strings, identifiers, URLs, file paths, and numeric ranges before invoking business logic.
- HTTPS: use TLS for remote connections and reject insecure transport in production.
- Secrets: store credentials in a managed secret store or platform configuration; never hard-code them in source or container images.
- Rate limiting: protect expensive or mutating tools from accidental loops and abuse.
- Logging and monitoring: record caller identity, tool name, latency, outcome, and correlation identifiers without logging secret values or unnecessary personal data.
- Dependency maintenance: update the MCP SDK, web framework, container base image, and Azure tooling on a planned schedule.
Troubleshooting common failures
Copilot cannot discover tools
Check that the server is running, the configured URL includes the MCP route, and the client expects the same transport as the SDK version. Inspect server logs for a request arriving at the endpoint. A normal browser GET to an MCP POST endpoint is not a meaningful protocol test.
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The container starts but Azure reports an unhealthy revision
Confirm that the process listens on 0.0.0.0 and the port matches the Container App target port. Review startup logs for a failed build, missing production dependency, or an incorrect start script.
Local calls work but remote calls fail
Test the public HTTPS URL from outside your development network. Check ingress, TLS, authentication headers, firewall rules, and any client-side timeout. If the server calls another API, verify that its credentials and outbound network access exist in Azure rather than only on your workstation.
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Tool input is rejected
Zod or another schema validator may be rejecting an empty, oversized, or incorrectly typed value. Read the validation error, correct the client request, and keep strict limits. Do not remove validation to make an agent call succeed.
Azure CLI or package commands differ from the tutorial
Microsoft updates CLI extensions, SDK packages, and hosting workflows. Check the current Microsoft Learn page and run az version before troubleshooting a command copied from an older tutorial.
Performance, reliability, and cost decisions
The tutorials establish deployment workflows, not benchmark results. Measure your own tool latency, concurrency, downstream API limits, and container scaling behavior. Keep tool handlers short, paginate large results, set explicit timeouts for downstream calls, and return structured errors that an agent can understand. For stateful sessions, choose a durable store rather than relying on a single container instance’s memory. For mutating operations, design idempotency keys or safe retries so a client retry cannot duplicate an action.
Azure cost depends on the hosting service, region, compute, storage, networking, and request volume. The examples do not establish a single price or performance figure; use the Azure pricing calculator and your measured workload when budgeting.
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Or skip the browser setup
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cURL:
curl -G "https://api.screenshotneo.com/v1/shot" -d access_key=YOUR_API_KEY --data-urlencode url=https://stripe.com -o shot.webpPython:
import requests
r = requests.get("https://api.screenshotneo.com/v1/shot", params={"access_key": "YOUR_API_KEY", "url": "https://stripe.com"}, timeout=90)
open("shot.webp", "wb").write(r.content)Node.js:
const q = new URLSearchParams({ access_key: 'YOUR_API_KEY', url: 'https://stripe.com' });
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Frequently Asked Questions
Is an MCP server the same as an MCP client?
No. The server exposes tools or other protocol capabilities; the client or host connects to it and invokes them. Copilot Chat is the client in Microsoft’s custom-server examples.
Can I expose an MCP server publicly without authentication?
Technically you can configure anonymous access, but Microsoft’s guidance is to require authentication unless anonymity is a deliberate, reviewed requirement. Public tools should also use least privilege, validation, rate limiting, HTTPS, and monitoring.
Should I use Microsoft’s Azure MCP Server or build my own?
Use the prebuilt Azure MCP Server when you need its Azure resource operations from a developer environment. Build a custom server when you need domain-specific tools, application data, or business workflows.
Does deploying to Azure automatically make a server production-ready?
No. Hosting solves deployment, not authorization, secret management, validation, observability, retry behavior, or downstream security.
Quick Recap
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