Model Context Protocol (MCP): The Standard for Connecting AI to Your Data

The Context Problem

LLMs are powerful reasoners — but they're blind. Ask Claude or GPT-4o about your company's Q3 revenue, your database schema, or the contents of a specific Notion page, and it will either hallucinate or admit it doesn't know.

The traditional fix is RAG (Retrieval-Augmented Generation) — embed your documents, retrieve relevant chunks, stuff them into the prompt. It works, but it's brittle. Retrieval quality degrades at scale. Chunks lose context. And every new data source requires custom integration code.

Model Context Protocol (MCP) is Anthropic's answer to this problem — and it's becoming the standard way to give LLMs access to live data and tools.

What MCP Actually Is

MCP is an open protocol (think: USB-C for AI) that defines a standard interface between an LLM host (Claude Desktop, your app, an IDE) and external data sources or tools.

Instead of each AI application reinventing how to connect to databases, APIs, and file systems, MCP provides a single, well-defined contract:

An MCP server exposes these capabilities. An MCP client (Claude Desktop, a custom app using the Anthropic SDK) connects to one or more servers and makes their capabilities available to the model.

The Architecture

MCP Host (your app / Claude Desktop)
  └── MCP Client
        ├── connects to → MCP Server A (your PostgreSQL database)
        ├── connects to → MCP Server B (your REST API)
        └── connects to → MCP Server C (Google Drive)

Each MCP server runs as a separate process — either locally (stdio transport) or remotely (HTTP + SSE transport). The host manages connections, handles authentication, and routes model requests to the appropriate server.

Building Your First MCP Server

Here's a minimal MCP server exposing a restaurant's inventory data (using the official TypeScript SDK):

import { Server } from "@modelcontextprotocol/sdk/server/index.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";

const server = new Server(
  { name: "restaurant-inventory", version: "1.0.0" },
  { capabilities: { tools: {}, resources: {} } }
);

server.setRequestHandler(ListToolsRequestSchema, async () => ({
  tools: [{
    name: "get_stock_level",
    description: "Get current stock for an ingredient",
    inputSchema: {
      type: "object",
      properties: { ingredient_id: { type: "string" } },
      required: ["ingredient_id"]
    }
  }]
}));

server.setRequestHandler(CallToolRequestSchema, async (request) => {
  if (request.params.name === "get_stock_level") {
    const stock = await db.getStock(request.params.arguments.ingredient_id);
    return { content: [{ type: "text", text: JSON.stringify(stock) }] };
  }
});

const transport = new StdioServerTransport();
await server.connect(transport);

Now Claude can call get_stock_level with a real ingredient ID and get live data — not a hallucinated answer.

MCP vs Direct Tool Use

Direct tool use is fine for simple, single-app scenarios. MCP shines when you want to share a data source or tool across multiple AI surfaces — your app, Claude Desktop, an internal agent, a CI pipeline.

What We Build With MCP

Internal knowledge bases: Connect Claude to your Notion workspace, Google Drive, or Confluence. Ask questions, get answers from actual company documents — no chunking strategy required.

Live database access: Expose read-only query tools over your PostgreSQL database. Ask "which products had the highest return rate last month?" and get an answer from real data.

CRM integration: An MCP server on top of your CRM lets a sales agent look up customer history, deal status, and last interaction — before composing a follow-up email.

ERP surfaces (like our ProdEazy project): Configuration-driven systems benefit enormously from MCP — the model can read field definitions, workflow states, and document templates at query time rather than having them baked into a prompt.

Security Considerations

MCP servers have direct access to your data. Security is non-negotiable:

The Ecosystem in 2025

MCP adoption is accelerating fast. Official servers exist for: filesystem access, Google Drive, Slack, GitHub, PostgreSQL, Puppeteer, and dozens more. IDEs like Cursor and Zed are building MCP support natively. Anthropic's own Claude Desktop ships with MCP built in.

For product teams, this means: the infrastructure to connect your systems to AI is being standardised. The competitive advantage shifts from "can we connect AI to our data" to "how intelligently do we use it".

We design and deploy production MCP servers across a range of industries. If you're ready to give your AI surface access to live data, let's build it together.