How to build an MCP server

An MCP (Model Context Protocol) server is how you give an AI real capabilities — let Claude, ChatGPT, or Cursor call your API, query your database, or run your tool. This guide walks the honest path: what building one actually takes, step by step, and where the shortcut is when you just need one fast.

What an MCP server actually is

Strip away the branding and an MCP server is a small program that speaks a standard JSON-RPC protocol and exposes a list of tools. Each tool has a name, a description, and a schema for its inputs. When an AI client connects, it asks the server “what tools do you have?”, and from then on the AI can call those tools and get results back. That’s the whole idea: a typed, discoverable menu of actions an AI can take.

You don’t implement the JSON-RPC wire format yourself — the official SDKs do that. Your job is to define the tools and make them do something real.

Step 1 — Choose your transport

MCP servers run over one of two transports, and the choice shapes everything else:

• stdio — the server runs as a local process on the same machine as the AI client, talking over standard input/output. Simplest to start with; only usable locally.
• Streamable HTTP — the server is a web service clients connect to over the network. This is what you want for a remote server other people can use. As of the 2026 spec, MCP is a stateless request/response protocol, so a plain serverless function works.

If you’re building something for yourself, stdio is fine. If you want others to connect it — or you want it in the directory — build a remote HTTP server.

Step 2 — Set up the SDK

Use the official MCP SDK for your language. TypeScript and Python are the most mature. The SDK gives you a server object you register tools on and a transport you attach — a minimal “hello world” server that exposes one tool is a few dozen lines. Get that running and connecting from a client before you add real logic; a working skeleton you can iterate on beats a big design you can’t test.

Step 3 — Define your tools (this is the real work)

Everything an AI can do through your server is a tool, and the quality of your tool definitions decides whether the AI uses them well or misuses them. For each tool:

• Name it clearly and consistently — search_orders, not so.
• Describe it the way you’d brief a new employee: what it does, when to use it, and any gotchas. The AI reads this description to decide whether and how to call the tool. A vague description is the #1 cause of an AI misusing a tool.
• Schema the inputs with JSON-Schema — types, which are required, enums for fixed choices, and good field descriptions. Tight schemas prevent malformed calls.

Resist the urge to expose everything. A focused server with eight sharp tools beats a kitchen-sink server with sixty — every tool definition also consumes the AI’s context window, and bloated tool lists are a real, widely-complained-about problem.

Step 4 — Implement handlers & auth

Now the unglamorous 80%: each tool’s handler maps the call to real work — an API request, a database query, a computation — and returns a result. Handle errors explicitly and return useful messages; an AI can recover from “order not found” but not from a raw stack trace.

Then auth. If your tools touch anything private, the server needs credentials. Two common shapes: an API key / token the server holds, or OAuth so each user authorizes their own access. OAuth is more work — discovery metadata, dynamic client registration, PKCE — and it’s where a lot of servers get connection details subtly wrong. Follow the spec’s standard flow closely; non-standard auth is the top reason a healthy server fails to connect from real clients.

Step 5 — Host & maintain it

A remote server has to run somewhere reachable. With the stateless 2026 protocol that can be a plain serverless function or a small container behind a URL. Then comes the part nobody markets: maintenance. When the underlying API changes, your tools change; when the spec evolves, your server follows. A server is a small ongoing commitment, not a one-time build.

Step 6 — Connect & govern it

Point your AI client at the server’s URL and it works. But the moment a server can take real actions, you want three things a bare server doesn’t give you: rules on what the AI may actually do, a check that the server itself is safe, and a record of what happened. That’s what an MCP gateway adds — connect your server through it and you get per-tool allow/ask/block, a security scan, and a full activity log, in Claude, ChatGPT, and Cursor at once.

The shortcut: generate it from your API

Here’s the honest truth after all of the above: if you’re building an MCP server to wrap an existing API — yours or a third party’s — most of steps 1–5 are mechanical. Paste your API’s docs or OpenAPI spec into gate’s builder and it drafts the tools, schemas, and auth for you; you review every tool, add your key, and it runs through the gateway with no hosting to manage.

Building by hand is the right call when you’re shipping a product for thousands of users or doing something the generator can’t. For connecting your own API to your own AI, generating it is faster and lands governed, scanned, and logged for free.