Setting Up a Sitecore MCP Server with XP and VS Code Copilot

As AI-assisted development becomes more mainstream, tools like GitHub Copilot are changing how developers interact with complex platforms like Sitecore XP. However, integrating Copilot directly with Sitecore isn’t straightforward. There’s no native bridge that allows Copilot to understand your content structure, templates, or APIs out of the box.

This is where an MCP (Model Context Protocol-style) server comes in.

In this guide, we’ll walk through how to set up a custom MCP server that acts as a smart middleware between Sitecore XP and VS Code Copilot—making your Sitecore data more accessible, structured, and usable for AI-assisted development.

Why You Need an MCP Server for Sitecore

Sitecore XP exposes data through APIs like REST (SSC) and GraphQL, but those responses are often too verbose and inconsistent for AI tools to use effectively.

An MCP server solves this by:

• Normalizing Sitecore data into predictable formats
• Providing clean, intent-driven endpoints
• Acting as a developer-friendly abstraction layer
• Enabling Copilot to generate better, context-aware code

Instead of Copilot guessing how Sitecore works, you give it a structured interface to interact with.

High-level Architecture Overview

At a high level, your setup looks like this:

The MCP server sits in the middle, translating Sitecore data into something AI tools can easily consume.

Design Principles (this is what makes it “Copilot-friendly”)

1. Flatten Sitecore responses
2. Expose intent-based endpoints (not raw APIs)
3. Add developer-friendly metadata
4. Generate types automatically
5. Keep endpoints predictable and named well

Recommended Repo Structure

Prerequisites

Before starting, make sure you have:

• Sitecore XP 9 or 10 running (CM/CD setup)
• Headless Services enabled (for GraphQL)
• Node.js (v16+ recommended)
• VS Code with GitHub Copilot installed

Step 1: Enable Sitecore APIs

You have two main options:

Option A: Sitecore Services Client (SSC)

• REST-based
• Quick to enable
• Less ideal for structured queries

Option B: GraphQL (Recommended)

• Install Sitecore Headless Services
• Use endpoints like:
• /sitecore/api/graph/edge
• Much better for AI use cases due to structured queries

Step 2: Create the MCP Server

Initialize your Node.js project:

mkdir sitecore-mcp

cd sitecore-mcp

npm init -y

npm install express axios

Step 3: Build a GraphQL Client

Create a reusable client to communicate with Sitecore:

// graphqlClient.js
const axios = require("axios");
class SitecoreClient {
  constructor({ endpoint, apiKey }) { 
    this.endpoint = endpoint;
    this.apiKey = apiKey; 
  }
  async query(query, variables = {}) { 
    const res = await axios.post( 
      this.endpoint, 
      { query, variables }, 
      { 
        headers: { 
          "X-API-Key": this.apiKey
        }
      }
    );
    return res.data.data;
  } 
}
module.exports = SitecoreClient; 
Shape 
Step 4: Normalize Sitecore Responses 
Raw Sitecore responses are noisy. You need to simplify them. 
// mapItem.js 
function mapItem(item) { 
  if (!item) return null; 
  const fields = {}; 
  (item.fields || []).forEach(f => { 
    fields[f.name.toLowerCase()] = f.value; 
  }); 
  return { 
    id: item.id, 
    name: item.name, 
    url: item.url?.path || "", 
    template: item.template?.name, 
    fields 
  }; 
} 
module.exports = { mapItem };

This step is critical—Copilot performs much better with clean, predictable JSON.

Step 5: Create MCP Endpoints

Now expose meaningful APIs:

// server.js
const express = require("express");
const SitecoreClient = require("./graphqlClient");
const { mapItem } = require("./mapItem");
const app = express();
app.use(express.json());
const client = new SitecoreClient({
  endpoint: process.env.GRAPHQL_ENDPOINT,
  apiKey: process.env.API_KEY
});
app.get("/content", async (req, res) => {
  const path = req.query.path;
  const query = `
    query GetItem($path: String!) {
      item(path: $path) {
        id
        name
        url { path }
        template { name }
        fields {
          name
          value
        }
      }
    }
  `;
  const data = await client.query(query, { path });
  res.json(mapItem(data.item)); 
});
app.listen(3000, () => { 
  console.log("MCP server running on port 3000"); 
});

Step 6: Make It Copilot-Friendly

Avoid generic endpoints like:

GET /content?path=/home

Instead, design intent-driven APIs:

GET /page/home

GET /component/hero

GET /datasource/footer

This helps Copilot infer meaning and generate better code automatically.

Step 7: Use It in VS Code with Copilot

Once your MCP server is running:

You can write prompts like:

// fetch homepage data from MCP server

Copilot will generate:

const res = await fetch("http://localhost:3000/content?path=/home");
const data = await res.json();

Because your API is clean and predictable, the generated code is actually usable.

Step 8: Add Type Generation (Optional but Powerful)

You can generate TypeScript interfaces from Sitecore templates:

function generateType(template) {
  return `
    export interface ${template.name} {
      ${template.fields.map(f => `${f}: string;`).join("\n")}
    }
  `; 
} 

This gives you:

• Strong typing
• Better IntelliSense
• Improved Copilot suggestions

Step 9: Add AI-Aware Endpoints

To go further, add endpoints like:

• /summarize-page
• /search-content
• /generate-component-data

These act as higher-level abstractions on top of Sitecore content.

Step 10: Secure Your MCP Server

Do not expose your Sitecore instance directly.

Best practices:

• Use API keys
• Add rate limiting
• Restrict endpoints
• Avoid exposing CM publicly

Common Pitfalls

• Expecting native MCP support in Sitecore
• Using raw Sitecore API responses
• Skipping normalization
• Not using GraphQL
• Poor endpoint naming

Final Thoughts

Setting up an MCP server for Sitecore XP isn’t just about integration—it’s about shaping your content and APIs in a way that AI tools can understand.

Once done right, the benefits are significant:

• Faster development with Copilot
• Cleaner API contracts
• Better developer experience
• AI-assisted content and component workflows

This approach effectively modernizes how developers interact with Sitecore—bridging the gap between traditional CMS architecture and AI-driven development.

If you want to take this further, the next logical steps are:

• Converting this into a monorepo (Nx or Turborepo)
• Adding Docker for local environments
• Integrating semantic search or vector databases
• Supporting XM Cloud for a fully headless architecture

The MCP layer becomes your foundation for all of it.

Happy Conding !