Model Context Protocol (MCP): Connecting AI to Your Tools and Data

Every AI integration used to be custom: one connector for the IDE, another for the chat app, another for the agent framework. The Model Context Protocol (MCP) replaces that with a single open standard. Build an MCP server once, and any MCP-compatible client can use it.

For developers, MCP is quickly becoming a core skill — it's how AI assistants get access to real systems.

One protocol lets any compatible AI client use any MCP server's tools, resources and prompts.

The Core Idea

MCP follows a client–server model:

• An MCP host is the AI application — a desktop assistant, an IDE or a custom agent.
• Inside it, an MCP client maintains a connection to each server.
• An MCP server exposes capabilities from one system: a database, GitHub, a file store or your company's API.

The analogy often used is USB-C for AI: a standard connector instead of a drawer of adapters.

What a Server Exposes

MCP servers can offer three kinds of capabilities:

• Tools — actions the model can invoke, such as create_issue or query_orders.
• Resources — read-only context such as files, records or documentation.
• Prompts — reusable templates for common tasks.

Servers can run locally (communicating over standard input/output) or remotely over HTTP, which is how SaaS products expose MCP endpoints to their customers.

A Minimal Server

Using the official TypeScript SDK, a tool is a name, a schema and a handler:

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: "projects", version: "1.0.0" });

server.tool(
  "search_projects",
  "Search portfolio projects by keyword",
  { query: z.string().min(2) },
  async ({ query }) => ({
    content: [{ type: "text", text: JSON.stringify(await searchProjects(query)) }],
  }),
);

await server.connect(new StdioServerTransport());

Security Is Your Responsibility

An MCP server is an API that an AI model can call on a user's behalf, so apply the same rigour as any public API — and then some:

• Authenticate and authorise remote servers properly (the spec builds on OAuth for HTTP transports).
• Least privilege — expose read-only tools by default and add write tools deliberately.
• Validate every argument server-side; never trust the model's input.
• Beware prompt injection — content returned by tools (emails, web pages, tickets) can contain instructions aimed at the model.
• Require confirmation for destructive actions in the client.

Where MCP Fits

MCP doesn't replace APIs; it wraps them for AI consumption. It's most valuable when:

• the same capability should work across several AI clients,
• you want customers to connect their own assistants to your SaaS,
• internal teams need AI access to many systems through one standard.

Key Takeaways

• MCP standardises how AI applications connect to tools and data.
• Servers expose tools, resources and prompts over local or remote transports.
• The official SDKs make simple servers only a few lines of code.
• Treat MCP servers as security-critical APIs: authenticate, restrict and validate.

Sources

• Model Context Protocol — Official documentation
• MCP specification
• MCP TypeScript SDK (GitHub)