If you have used modern AI assistants recently - ChatGPT, Claude, Windsurf, Cursor, etc. - you have probably noticed that they can take actions on your behalf: run a SQL query, create a GitHub issue, or even deploy infrastructure. Behind the scenes, those assistants are not guessing at random HTTP calls, they are invoking well-defined “tools” exposed via the Model Context Protocol (MCP) of the integrated platforms.

In short, MCP is a thin specification that makes an API self-describing to large language model agents. Once an endpoint is wrapped as an MCP tool, any compliant AI client can discover it, decide when to call it, and pass correctly typed arguments - all from plain English prompts. Instead of integrating with a system by writing code (which requires a wide range of knowledge), you can just connect an MCP server, add your access token, and integrate using natural language.

What exactly is MCP?

The Model Context Protocol is an emerging open standard (originally proposed by Anthropic) that sits on top of your existing API. Each endpoint - name, parameters, and response schema - is described in a machine-readable manifest, typically autogenerated by the SDK from your code. The manifest is served by an MCP server over either STDIO (for local tools) or HTTP (for remote services).

Because the contract is explicit, an AI model can load the manifest into its context window and reason about how to call your function, just like developers reason about typed function signatures in code.

Instead of making an API request, you can just type “list all the projects in my account”, and all your projects will be listed. No need to write code, no need to understand the API, no need to understand the authentication flow. The AI assistant will handle all of that for you.

Why bother building one?

Building an MCP server adds surprisingly little surface area to your stack but unlocks three major benefits:

• A new distribution channel. Every AI app that supports MCP instantly becomes an integration partner. Your API can be discovered inside familiar AI interfaces, like ChatGPT’s tool palette or Claude’s “extensions” tab. These integrations unlock a high-potential distribution channel.
• Zero-friction onboarding. Non-technical users can “talk” to your API instead of reading docs, finding SDK keys, and writing boilerplate. The AI handles the call semantics for them, making your application instantly accessible to a much wider audience.
• Higher-value feedback. Because requests come through structured tools, you get rich telemetry: what users asked, which arguments were passed, and where they struggled. This is powerful input for both product and model improvement.

How does authentication work?

Authentication is transport-specific, but MCP intentionally mirrors existing patterns. Two transports are supported:

Transport	Typical Use-case	Auth Strategy
STDIO	Local CLI helpers, dev-only tools	Share an environment variable or read from a local config file.
HTTP	SaaS APIs, multi-tenant services	Issue user-scoped bearer tokens via OAuth (the spec re-uses RFC 6749).

Most assistants today ship with token-based prompting UIs, so once your server advertises its auth URL, they can guide the user through the flow automatically.

Real-world examples

1. Supabase MCP – Manage your account, edge functions, database, and many more features via chat.
2. Vercel MCP – Deploy, view logs, or roll back a Next.js app entirely via chat.
3. Stripe MCP – Generate payment links or refund a charge by describing the intent in natural language.

These are still early days, but the pattern is already proving itself across infrastructure, DevOps, and productivity tooling.

Final thoughts

MCP is not the final word on AI integrations, but it is today’s best lightweight standard - and it is moving fast. Early adopters are already capturing organic traffic from AI interfaces, gathering fine-grained usage data, and shortening their onboarding funnels.

If your product exposes an API, shipping an MCP server is one of the highest-leverage tasks you can do. Your future users will “talk” to software; make sure your software can “listen”.