Get set up

Saying hi to Claude might warm your heart, but it's not really useful. In this lesson we'll send Claude something real and get structured insight back — in just under 20 lines of code.

First, grab an API key from platform. claude. com. You'll need to purchase some credits beforehand.

The Claude Console dialog showing a newly created API key with a Copy key button and a warning that the key won't be viewable again

Take the API key and store it in a . env. local file so it stays out of your version control. Hardcoding keys in source files is how they end up leaked on GitHub — keep them in environment files instead.

Next, install the SDK:

npm install @anthropic-ai/sdk

The anatomy of a request

Every API call goes through the **messages. create** function. You specify three things:

A model — which Claude model handles the request
A max tokens limit — a cap on how long the response can be
A list of messages — objects with either user or assistant roles, structured similarly to how you'd have a conversation with Claude elsewhere

Here's what that looks like in its most basic form:

import Anthropic from "@anthropic-ai/sdk";

const client = new Anthropic();

const msg = await client. messages. create({ model: "claude-opus-4-7", max_tokens: 1024, messages: [{ role: "user", content: "Hello, Claude", }], });

A real example: reviewing buggy code

Let's give Claude something a little more interesting than "hello. " We'll point it at some buggy code and ask for a review. Here's the whole thing — one file, about 20 lines of code:

import Anthropic from "@anthropic-ai/sdk";

const client = new Anthropic();

const buggyCode = ` function add(a, b) { return a - b; } `;

const response = await client. messages. create({ model: "claude-opus-4-8", max_tokens: 1024, system: "You are a terse senior code reviewer. Give feedback in one paragraph. ", messages: [ { role: "user", content: Review this code:\n${buggyCode} }, ], });

for (const block of response. content) { if (block. type === "text") { console. log(block. text); } }

Two things to notice here:

The system prompt is where you shape the persona. I want a terse senior reviewer, not a chatty one — so I just say that.
The **message. content in the response is an array of blocks** , not a string. For a basic text reply there's usually just one block of type text, but Claude can return multiple blocks — text, tool calls, thinking — so we always loop and check the type.

Run it, and Claude spots that add is subtracting and tells you in one paragraph. That's it. That's the whole API call.

Terminal output from running the script: Claude responds that the function is named add but uses subtraction, and suggests changing return a - b to return a + b

From script to product

In a real product, this same messages. create shape is the engine behind something like a summarize endpoint. Pull a meeting transcript out of the database, hand it to Claude with a system prompt that says "extract insights and risks," save the result back on the row, and return it to the UI. It's the same call — just wrapped in a route handler.

A meetings dashboard in a demo web app listing recorded project meetings, each with a transcript preview and a Generate summary button powered by the same API call

Recap

Your first API call is a **messages. create function with a model , a token limit , and messages**.
Store your API key in a . env. local file to keep it out of version control.
Add a system prompt to shape Claude's behavior.
The response content is an array of blocks — loop and check each block's type.
From here, everything builds on this pattern.