In Part 1, we built a simple MCP server in TypeScript that exposed a “getWeather” tool. Now, let’s take the next step: connecting our MCP server to Microsoft Copilot Studio so that Copilot agents can call it directly. This section will cover: Step 1 — Publish Your MCP Server to Azure To make your MCP server accessible to Copilot Studio, you’ll need to host it online. There are multiple ways to deploy it — Azure App Service, Azure Container Apps, or even Azure Functions if you prefer serverless. For example, using Azure App Service: Test using curl to ensure it responds with MCP-compatible JSON: Step 2 — Create a New Copilot in Copilot Studio Step 3 — Add Knowledge Sources Optionally, you can enrich your Copilot by adding: This gives your Copilot a baseline knowledge to answer broader questions, while the MCP server will handle specific tasks (like fetching live weather data). Step 4 — Create a Custom Connector in Dataverse To let Copilot Studio talk to our MCP server, we need a custom connector inside Dataverse/CRM. Step 5 — Add the Custom Connector to Copilot Studio you’ll see the MCP server in your Tools section of copilot. To test the setup, let’s ask Copilot: “What’s the current weather in Mumbai?” On the first attempt, Copilot will prompt you to establish a connection. Simply open the Connection Manager, click Connect, and authorize the link to your MCP server. Once connected, Copilot will fetch the live weather details for Mumbai directly from your MCP server. and click retry on the Test window of your copilot. And just like that, your MCP server is live and fully integrated. It can now provide real-time weather updates for any city mentioned in your conversation with Copilot. You can try out different variations of questions or phrasings — Copilot will intelligently interpret your request, extract the city name, and seamlessly call the MCP server to deliver accurate weather details. Beyond Weather: Business Integrations The same process works for enterprise systems. For example, instead of getWeather, you could expose: By publishing these tools via MCP, your Copilot becomes a true enterprise assistant, capable of pulling structured business data and triggering workflows on demand. We hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudfronts.com.

As artificial intelligence continues to transform how we build and interact with software, AI agents are emerging as the new interface for users. Instead of clicking through menus or filling out forms, users can simply instruct agents to fetch reports, analyze datasets, or trigger workflows. The challenge is: how do these agents access external tools, APIs, or enterprise systems in a secure and standardized way? This is where the Model Context Protocol (MCP) comes into play. MCP is a protocol designed to connect AI agents to tools in a structured, consistent manner. Instead of building ad-hoc integrations for each agent and each tool, developers can expose their tools once via MCP — making them discoverable and callable by any MCP-compliant AI agent. In this article, we’ll explore: What an MCP server is and how it works a) How MCP uses JSON-RPC 2.0 as its communication layer b) How MCP solves the M×N integration problem c) How to implement a simple Weather Data MCP server in TypeScript d) How to test it locally using Postman or cURL What is an MCP Server? An MCP server is an HTTP or WebSocket endpoint that follows the Model Context Protocol, allowing AI systems to query, interact with, and call tools hosted by developers. MCP consists of several components: -Base Protocol – Core JSON-RPC message types -Lifecycle Management – Connection initialization, capability negotiation, and session handling -Server Features – Resources, prompts, and tools exposed by servers -Client Features – Sampling and root directory lists provided by clients -Utilities – Cross-cutting features such as logging or argument completion All MCP implementations must support the Base Protocol and Lifecycle Management. Other features are optional depending on the use case. Architecture: JSON-RPC 2.0 in MCP MCP messages follow the JSON-RPC 2.0 specification — a stateless, lightweight remote procedure call protocol that uses JSON for request and response payloads. Request format: json Copy Edit {   “jsonrpc”: “2.0”,   “id”: 1,   “method”: “methodName”,   “params”: {     “key”: “value”   } } id is required, must be a string or number, and must be unique within the session. method specifies the operation. params contains the method arguments. Response format: json Copy Edit {   “jsonrpc”: “2.0”,   “id”: 1,   “result”: {     “key”: “value”   } } Or, if an error occurs: json Copy Edit {   “jsonrpc”: “2.0”,   “id”: 1,   “error”: {     “code”: -32603,     “message”: “Internal error”   } } The ID must match the request it is responding to. The M×N Problem and How MCP Solves It. Without MCP, connecting M AI agents to N tools requires M×N separate integrations. This is inefficient and unscalable. With MCP, each agent implements a single MCP client, and each tool implements a single MCP server. Agents and tools can then communicate through a shared protocol, reducing integration effort from M×N to M+N. Project Setup Create the project directory: mkdir weather-mcp-sdk cd weather-mcp-sdk npm init -y Install dependencies: npm install @modelcontextprotocol/sdk zod axios express npm install –save-dev typescript ts-node @types/node @types/express npx tsc –init Implementing the Weather MCP Server We’ll use the WeatherAPI to fetch real-time weather data for a given city and expose it via MCP as a getWeather tool. src/index.ts import express from “express”; import axios from “axios”; import { McpServer } from “@modelcontextprotocol/sdk/server/mcp.js”; import { StreamableHTTPServerTransport } from “@modelcontextprotocol/sdk/server/streamableHttp.js”; import { z } from “zod”; const API_KEY = “YOUR_WEATHER_API_KEY”; // replace with your API key function getServer() {   const server = new McpServer({     name: “Weather MCP Server”,     version: “1.0.0”,   });   server.tool(     “getWeather”,     { city: z.string() },     async ({ city }) => {       const res = await axios.get(“http://api.weatherapi.com/v1/current.json”, {         params: { key: API_KEY, q: city, aqi: “no” },       });       const data = res.data;       return {         content: [           {             type: “text”,             text: `Weather in ${data.location.name}, ${data.location.country}: ${data.current.temp_c}°C, ${data.current.condition.text}`,           },         ],       };     }   );   return server; } const app = express(); app.use(express.json()); app.post(“/mcp”, async (req, res) => {   try {     const server = getServer();     const transport = new StreamableHTTPServerTransport({});     res.on(“close”, () => {       transport.close();       server.close();     });     await server.connect(transport);     await transport.handleRequest(req, res, req.body);   } catch (error) {     if (!res.headersSent) {       res.status(500).json({         jsonrpc: “2.0”,         error: { code: -32603, message: “Internal server error” },         id: null,       });     }   } }); const PORT = 3000; app.listen(PORT, () => {   console.log(`MCP Stateless HTTP Server running at http://localhost:${PORT}/mcp`); }); Testing the MCP Server Since MCP requires specific request formats and content negotiation, use Content-Type: application/json and Accept: application/json, text/event-stream headers. Step 1 — Initialize curl -X POST http://localhost:3000/mcp \   -H “Content-Type: application/json” \   -H “Accept: application/json, text/event-stream” \   -d ‘{     “jsonrpc”: “2.0”,     “id”: 1,     “method”: “initialize”,     “params”: {       “protocolVersion”: “2025-06-18”,       “capabilities”: { “elicitation”: {} },       “clientInfo”: { “name”: “example-client”, “version”: “1.0.0” }     }   }’ Example response: {   “jsonrpc”: “2.0”,   “id”: 1,   “result”: {     “protocolVersion”: “2025-06-18”,     “capabilities”: { “tools”: { “listChanged”: true } },     “serverInfo”: { “name”: “Weather MCP Server”, “version”: “1.0.0” }   } } Step 2 — Call the getWeather Tool curl -X POST http://localhost:3000/mcp \   -H “Content-Type: application/json” \   -H “Accept: application/json, text/event-stream” \   -d ‘{     “jsonrpc”: “2.0”,     “id”: 2,     “method”: “tools/call”,     “params”: {       “name”: “getWeather”,       “arguments”: { “city”: “London” }     }   }’ Example response: {   “jsonrpc”: “2.0”,   “id”: 2,   “result”: {     “content”: [       {         “type”: “text”,         “text”: “Weather in London, United Kingdom: 21°C, Partly cloudy”       }     ]   } } To conclude, we have built an MCP-compliant server in TypeScript that exposes a weather-fetching tool over HTTP. This simple implementation demonstrates: How to define and register tools with MCP How JSON-RPC 2.0 structures communication, and how to make your server compatible with any MCP-compliant AI agent. From here, you … Continue reading Creating an MCP Server Using TypeScript →