MCP Servers: The Missing Piece in Your AI Workflow

If you’ve been working with AI tools like Claude, you’ve probably hit the wall where the model doesn’t have access to the context it needs. It can’t read your database. It can’t check your analytics. It doesn’t know about your internal docs.

Model Context Protocol (MCP) servers solve this problem.

What Is MCP?

MCP is an open standard created by Anthropic that lets AI models connect to external data sources and tools through a simple, standardised interface. Think of it as a USB-C port for AI — one protocol that works everywhere.

An MCP server exposes “tools” and “resources” that an AI model can use:

import { McpServer } from '@modelcontextprotocol/sdk/server/mcp.js';

const server = new McpServer({
  name: 'analytics-server',
  version: '1.0.0',
});

// Expose a tool that lets AI query your analytics
server.tool(
  'get_page_views',
  'Get page view data for a specific URL path',
  {
    path: z.string().describe('The URL path to query'),
    days: z.number().default(30).describe('Number of days to look back'),
  },
  async ({ path, days }) => {
    const data = await analytics.getPageViews(path, days);
    return {
      content: [{ type: 'text', text: JSON.stringify(data, null, 2) }],
    };
  }
);

Why This Matters for Your Business

AI Gets Smarter With Your Data

Without MCP, AI models are limited to what’s in their training data and what you paste into the chat. With MCP, they can actively query your systems in real time.

Imagine asking Claude: “What were our top performing blog posts last month?” and getting an actual answer based on your real analytics data.

Your Team Gets More Productive

MCP servers turn AI assistants into genuine productivity tools. Instead of switching between five dashboards, your team can ask questions in natural language and get answers from across your stack.

It’s Easier Than You Think

A basic MCP server can be built in an afternoon. Here’s the general structure:

// 1. Define your tools
server.tool('tool_name', 'description', schema, handler);

// 2. Define your resources (read-only data)
server.resource('resource://path', 'description', handler);

// 3. Connect to your transport
const transport = new StdioServerTransport();
await server.connect(transport);

Real-World Use Cases

We’ve built MCP servers for clients that connect to:

• Supabase databases — let AI query customer data, orders, and metrics
• Analytics platforms — pull real-time traffic and conversion data
• CRM systems — surface customer context during support conversations
• Internal documentation — make company knowledge searchable by AI
• Deployment pipelines — let AI check build status and recent deploys

Getting Started

If you want to add MCP to your workflow:

1. Identify your most-queried data sources — what do your team members look up most often?
2. Start with read-only tools — don’t let AI modify data until you’re confident
3. Add authentication — MCP servers should respect your existing access controls
4. Monitor usage — track which tools get used and how they perform

We Build These

MCP server development is one of our specialities. If you want to give your team AI superpowers connected to your actual business data, check out our MCP Server Builds service or book a call and we’ll walk you through the options.