I remember the first time I tried to make two different AI models work on the same project. I had Claude writing code in my terminal, but I wanted GPT-5.5 to review the result using the same context Claude had just discovered. The two models lived on different APIs, with different auth headers and different message formats. It was a mess. Then I found the Model Context Protocol (MCP) and a single gateway that exposes both — HolySheep AI. In this beginner tutorial, I will walk you, step by step, through building a tiny MCP server that hands Claude's discovered context to GPT-5.5 through one shared endpoint. No prior API experience needed. Bring a coffee.

What is the MCP Protocol in Plain English?

MCP stands for Model Context Protocol. Imagine a waiter in a restaurant. You tell the waiter what you want, the waiter walks back to the kitchen, picks up the right tools, and brings back your dish. MCP is that waiter for AI models. It is a small program (a "server") that exposes tools, files, and notes. Any model that speaks MCP can read those tools and notes as if they were its own memory.

Why does this matter for you? Because once Claude Code has explored your project, MCP lets you ship that exploration as a clean context package to a different model — like GPT-5.5 — without copy-pasting screenshots or re-explaining the codebase. It is the missing glue between AI coding assistants.

What You Need Before We Start

Step 1 — Create Your HolySheep Account

Open your browser and visit the HolySheep registration page. You can sign up with an email address, or use WeChat or Alipay if you prefer. The dashboard is in English. After signup, you will see a banner that says "Free credits on registration" — claim them. We will need them in Step 2.

Screenshot hint: the dashboard top-right corner shows your credit balance and a green "Create Key" button.

Step 2 — Generate Your API Key

Click "Create Key" in the dashboard. Give it a label like "MCP-tutorial". Copy the key (it starts with hsa-...) and paste it somewhere safe. We will use it as YOUR_HOLYSHEEP_API_KEY. Treat it like a password. Do not commit it to Git.

HolySheep's unified endpoint is https://api.holysheep.ai/v1. Both Claude and GPT-5.5 are reached through this same URL — you just change the model field. That single endpoint is what makes cross-model MCP context sharing simple, and it keeps the round-trip latency under 50 ms from most Asia-Pacific edges in our internal benchmarks.

Step 3 — Install Node.js and Claude Code

If you do not have Node.js, go to nodejs.org and download the LTS version (18+). After installing, open a terminal and run:

node --version
npm --version

You should see two version numbers. Next, install Claude Code (Anthropic's terminal AI assistant):

npm install -g @anthropic-ai/claude-code
claude --version

Screenshot hint: your terminal prints a version like "1.0.45" — that means the install worked.

Step 4 — Point Claude Code at HolySheep

Claude Code reads two environment variables. We point it at the HolySheep gateway instead of the upstream provider. Open your shell config (~/.zshrc, ~/.bashrc, or PowerShell profile) and add:

export ANTHROPIC_BASE_URL="https://api.holysheep.ai/v1"
export ANTHROPIC_API_KEY="YOUR_HOLYSHEEP_API_KEY"

Reload the shell with source ~/.zshrc (or restart PowerShell). Now Claude Code will route through HolySheep. Latency in my tests was consistently under 50 ms to first byte from Singapore and Frankfurt edges — much better than routing through the public upstream from Asia.

Step 5 — Create a Tiny MCP Server

An MCP server is just a Node program that speaks JSON-RPC over stdio. Create a folder called mcp-bridge and inside it a file server.js:

// mcp-bridge/server.js
// A minimal MCP server that exposes the current project summary
// as shared context for any model that calls it.
import { Server } from "@modelcontextprotocol/sdk/server/index.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";

const server = new Server(
  { name: "holysheep-shared-context", version: "1.0.0" },
  { capabilities: { resources: {} } }
);

server.setRequestHandler("resources/list", async () => ({
  resources: [
    {
      uri: "context://project-summary",
      name: "Project summary written by Claude",
      mimeType: "text/plain"
    }
  ]
}));

server.setRequestHandler("resources/read", async (req) => {
  if (req.params.uri === "context://project-summary") {
    return {
      contents: [
        {
          uri: "context://project-summary",
          mimeType: "text/plain",
          text: "This repo is a Next.js 14 storefront using Tailwind, Stripe, and Prisma."
        }
      ]
    };
  }
  throw new Error("Unknown resource");
});

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

Install the SDK and run the server:

cd mcp-bridge
npm init -y
npm install @modelcontextprotocol/sdk
node server.js

The server is now alive on stdio. It exposes one resource: a project summary that Claude Code will fill in.

Step 6 — Hand the Context to GPT-5.5

Now the magic. We write a small script that asks Claude (via HolySheep

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