MCP Connect: Why Every AI Engineer and Developer Should Care About the Model Context Protocol
There is a quiet standardization happening underneath the AI agent boom, and it has a name: the Model Context Protocol (MCP). If you build agents, wire tools into Copilot, or ship anything that lets a language model act on the real world, MCP is fast becoming the layer you cannot ignore. That is exactly why the community is gathering for MCP Connect a full-day, vendor-neutral, community-run conference dedicated entirely to the protocol powering how AI agents connect with tools, data, and each other. This post is written for AI engineers and developers. It explains what MCP is and why it matters now, previews what MCP Connect offers builders, walks through real, runnable server code, and points you at the best Microsoft resources starting with MCP for Beginners so you arrive at the event ready to build, not just watch. What is MCP Connect? MCP Connect is described by its organizers as "Connecting Agents. Empowering Builders." It is a community-driven conference dedicated to the Model Context Protocol, the open standard that defines how AI agents talk to tools, data, and one another. The pitch is refreshingly direct: no vendor pitches, just builders talking to builders about making the protocol work in production. Expect a day built around practical, engineering-first content: Hands-on workshops on building and securing MCP servers. Talks on client integration and agent interoperability. A community showcase of what people are actually shipping with the protocol today. Deep protocol discussion the kind of conversation you rarely get outside a focused, single-topic event. The first two in-person dates on the calendar are: MCP Connect, San Francisco, Monday 14 September 2026 (event details), hosted by Global AI San Francisco. MCP Connect, Bengaluru, Saturday 26 September 2026 (event details), hosted by Global AI Bengaluru. It is organized under the Global AI Community umbrella built by and for the people shaping agent connectivity. You can subscribe for updates on the event page as new cities are announced. Why MCP matters now If you have built with large language models recently, you have hit the same wall everyone hits: the model reasons brilliantly but is blind to your world. It cannot read your database, call your internal API, search your documents, or trigger a deployment unless you hand-write glue code for every integration. Think of MCP as a universal translator for AI applications. Just as USB-C lets any peripheral connect to any laptop without a custom cable per device, MCP lets an AI model connect to any tool or data source through one standardized protocol. The economics are the real story. Before MCP, integrations were an M × N problem: every one of your M AI applications needed bespoke code to talk to each of your N tools. MCP turns that into an M + N problem. Build a tool once as an MCP server, and any MCP-compatible client VS Code, GitHub Copilot, Claude Desktop, Cursor, and many others can use it immediately. The protocol is built on a clean client–server model with a small, learnable set of primitives: Tools functions the model can call (query a database, send an email, run code). Resources data the server exposes for context (files, records, documents). Prompts reusable, parameterized prompt templates. Sampling a server asking the client's model to generate a completion, enabling collaborative workflows. Elicitation a server requesting structured input from the user mid-task. Roots boundaries that tell a server which directories or resources it is allowed to touch. Communication runs over JSON-RPC, with transports for local processes ( stdio ) and remote servers (streamable HTTP). Write to the spec, and you interoperate with the entire ecosystem. The canonical reference lives at modelcontextprotocol.io. Your first MCP server: see how little code it takes The best way to prepare for a builder-focused event is to build something. Here is a minimal MCP server in Python using FastMCP . Notice how the protocol plumbing disappears — you just decorate functions and describe them. # server.py — a minimal MCP server with two tools from mcp.server.fastmcp import FastMCP # Name your server; this identifies it to MCP clients mcp = FastMCP("Calculator") @mcp.tool() def add(a: int, b: int) -> int: """Add two numbers and return the result.""" return a + b @mcp.tool() def subtract(a: int, b: int) -> int: """Subtract b from a and return the result.""" return a - b if __name__ == "__main__": # Run over stdio so local hosts (VS Code, Claude Desktop) can connect mcp.run() The same idea in TypeScript, using the official @modelcontextprotocol/sdk : // server.ts — minimal MCP server in TypeScript import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js"; import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js"; import { z } from "zod"; const server = new McpServer({ name: "Calculator", version: "1.0.0" }); // Register a tool with a typed input schema server.tool( "add", { a: z.number(), b: z.number() }, async ({ a, b }) => ({ content: [{ type: "text", text: String(a + b) }], }) ); // Connect over stdio and start listening const transport = new StdioServerTransport(); await server.connect(transport); That is a complete, runnable server. The docstrings and schemas are not decoration — MCP exposes them to the model so it knows when and how to call each tool. Clear descriptions are effectively prompt engineering for your tools. A common pitfall is leaving them vague, which leads the model to misuse or ignore the tool. Connecting it in VS Code Once your server runs, an MCP host connects to it. A typical VS Code configuration looks like this: { "servers": { "calculator": { "command": "python", "args": ["server.py"] } } } VS Code has first-class MCP support for adding, managing, and debugging servers directly in the editor see Add and manage MCP servers in VS Code. From demo to production: what to focus on A calculator is a great first server, but MCP Connect is about production. The gap between the two is where most engineering effort — and most of the event's value lives. Three areas deserve your attention. 1. Security is not optional An MCP server is an API that an autonomous model can invoke. Treat it that way. The practices to internalize before you ship: Least privilege via roots constrain what a server can reach. Tool annotations mark tools readOnlyHint or destructiveHint so clients can warn users before destructive actions. Never pass untrusted input through a shell a classic command-injection vector when a tool wraps a subprocess. Dependency hygiene audit regularly and pin patched releases. Proper auth use OAuth2 and, in Microsoft environments, Microsoft Entra ID rather than long-lived secrets. 2. Interoperability is the whole point The reason to write to the protocol instead of a single framework is that your server then works across the ecosystem. Test your server with the MCP Inspector before wiring it into any host — it is the single best debugging habit you can build early, letting you exercise tools, resources, and prompts in isolation. 3. Operations and observability Remote MCP servers are real services. Plan for deployment (containers scale well), authentication, rate limiting, structured logging, and monitoring. If you run on Azure, Application Insights and Container Apps give you a straightforward path from a local stdio prototype to a monitored HTTP-streaming server. Microsoft resources to prepare with You do not need to walk into MCP Connect cold. Microsoft maintains a strong, free, and current set of MCP resources for exactly this journey. MCP for Beginners the most complete hands-on curriculum, with code in C#, Java, JavaScript, Python, Rust, and TypeScript, from a 10-line server to a multi-lab production capstone. Start at https://aka.ms/mcp-for-beginners (the GitHub repository). Catalog of official Microsoft MCP servers reference implementations you can learn from and build on: github.com/microsoft/mcp. Azure MCP Server connect agents to Azure resources through MCP: Azure MCP Server documentation. MCP in VS Code add, configure, and debug servers in your editor: Add and manage MCP servers in VS Code. The official specification the source of truth for every primitive and transport: modelcontextprotocol.io. A fast way to prepare: fork MCP for Beginners using a sparse checkout to skip translations, then build and debug your first server before the event. git clone --filter=blob:none --sparse https://github.com/microsoft/mcp-for-beginners.git cd mcp-for-beginners git sparse-checkout set --no-cone "/*" "!translations" "!translated_images" Why AI engineers and developers should attend For AI engineers MCP is becoming the default integration layer for agents. Instead of re-implementing tool calling for every framework, you write to one open protocol and your tools work everywhere. MCP Connect's deep-dive sessions on sampling, roots, elicitation, scaling, and multi-agent patterns are exactly the techniques that move agents from demo to production and hearing them from practitioners who have shipped is worth more than any slide deck. For developers MCP is already wired into the tools you use daily: VS Code, GitHub Copilot, Claude Desktop, and Cursor. Learning to build an MCP server means you can expose your systems — internal APIs, databases, CI/CD to AI assistants safely. A vendor-neutral event is the ideal place to compare integration approaches and pick up the security patterns that keep you out of trouble. Responsible and secure by design Because MCP hands an autonomous model the keys to real tools, responsible engineering is a first-class concern, not an afterthought. Carry these principles into whatever you build: Constrain scope grant the minimum access a server needs, and make destructive actions explicit and reviewable. Guard the boundary validate inputs, avoid shells for user-supplied data, and authenticate remote servers properly. Evaluate and monitor log tool calls, watch for anomalous behavior, and govern what agents can do in production. Key takeaways MCP standardizes how AI connects to tools and data, turning a combinatorial integration problem into a simple, reusable one. MCP Connect is builder-first vendor-neutral, community-run, focused on making the protocol work in production. A working server takes minutes, but production requires deliberate attention to security, interoperability, and operations. Microsoft's MCP resources are the fastest on-ramp start with MCP for Beginners and the official spec. Show up ready to build, not just to watch, the value compounds when you can follow along hands-on. Get involved Explore the event: globalai.community/events/mcp-connect and subscribe for new city announcements. Register for a date near you San Francisco (14 Sep 2026) or Bengaluru (26 Sep 2026). Learn the protocol with MCP for Beginners and the official spec. Build your first server this week, debug it with the MCP Inspector, and connect it in VS Code. Bring a project to the community showcase the best way to learn a protocol is to ship something with it. MCP is quietly becoming the connective tissue of the AI ecosystem, and MCP Connect is where the builders shaping it are gathering. Learn the protocol, build a server, and come ready to connect your agents to the world.Unlock Your Python Potential with Azure
Dive into the world of Python with our latest blog to discover why Python remains a developer's best friend and explore the comprehensive Azure resources designed to enhance your coding journey. From boosting productivity with seamless integration to leveraging powerful libraries for data science and machine learning, find out how Azure supports your Python development every step of the way. Ready to take your skills to the next level?
