Claude API Cheat Sheet: SDK, CLI, MCP & Prompting

Quick reference tables

Models

Anthropic API — core requests

Messages API — key parameters

Claude Code CLI — essential commands

Claude Code CLI — flags

MCP — Model Context Protocol

Token limits (approx.)

Prompt caching

Detailed sections

Basic API call (Node.js)

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

const client = new Anthropic({ apiKey: process.env.ANTHROPIC_API_KEY });

const message = await client.messages.create({
  model: "claude-sonnet-4-6",
  max_tokens: 1024,
  messages: [{ role: "user", content: "Explain async/await in JavaScript." }],
});

console.log(message.content[0].text);

Streaming response

const stream = await client.messages.create({
  model: "claude-sonnet-4-6",
  max_tokens: 1024,
  stream: true,
  messages: [{ role: "user", content: "Write a short story." }],
});

for await (const event of stream) {
  if (event.type === "content_block_delta") {
    process.stdout.write(event.delta.text);
  }
}

System prompt + multi-turn conversation

const response = await client.messages.create({
  model: "claude-sonnet-4-6",
  max_tokens: 2048,
  system: "You are a senior backend engineer. Be concise and precise.",
  messages: [
    { role: "user", content: "What's wrong with N+1 queries?" },
    { role: "assistant", content: "N+1 queries happen when..." },
    { role: "user", content: "How do I fix it in PostgreSQL?" },
  ],
});

Tool use (function calling)

const response = await client.messages.create({
  model: "claude-sonnet-4-6",
  max_tokens: 1024,
  tools: [
    {
      name: "get_weather",
      description: "Get current weather for a city",
      input_schema: {
        type: "object",
        properties: {
          city: { type: "string", description: "City name" },
        },
        required: ["city"],
      },
    },
  ],
  messages: [{ role: "user", content: "What's the weather in Tokyo?" }],
});

// Check if Claude wants to use a tool
if (response.stop_reason === "tool_use") {
  const toolUse = response.content.find((b) => b.type === "tool_use");
  console.log(toolUse.name, toolUse.input); // get_weather { city: 'Tokyo' }
}

Prompt caching — reduce costs on large system prompts

const response = await client.messages.create({
  model: "claude-sonnet-4-6",
  max_tokens: 1024,
  system: [
    {
      type: "text",
      text: "You are an expert codebase assistant...\n\n[large context here]",
      cache_control: { type: "ephemeral" }, // cache this block
    },
  ],
  messages: [{ role: "user", content: "Explain the auth module." }],
});
// Subsequent calls with same system block → cache hit → ~90% cheaper

Claude Code — useful workflows

# One-shot: explain code without starting REPL
claude -p "Explain what this does" < src/utils/parser.ts

# Pipe output from another command
git diff | claude -p "Summarize what changed in plain English"

# Use in scripts
SUMMARY=$(claude -p "Summarize this log" < app.log)
echo "$SUMMARY"

# Ask Claude to write tests
claude "Write unit tests for src/auth/login.ts using Vitest"

# Ask Claude to fix a failing test
claude "The test in auth.test.ts is failing — fix it"

# Review a PR
claude /review-pr 42

CLAUDE.md — project instructions

Create CLAUDE.md in your repo root. Claude Code reads it on every session:

# Project: My App

## Stack
- Node.js 20, TypeScript, Fastify, PostgreSQL
- Tests: Vitest, run with `pnpm test`
- Lint: `pnpm lint` (ESLint + Prettier)

## Conventions
- Use named exports, no default exports
- Prefer `async/await` over `.then()`
- All DB queries go in `src/db/queries/`

## Commands
- `pnpm dev` — start dev server
- `pnpm build` — production build
- `pnpm test` — run tests

Batch API — process many prompts at once

// Create a batch (async, ~1hr processing)
const batch = await client.messages.batches.create({
  requests: [
    {
      custom_id: "req-1",
      params: {
        model: "claude-haiku-4-5-20251001",
        max_tokens: 256,
        messages: [{ role: "user", content: "Translate: Hello world" }],
      },
    },
    // ... up to 10,000 requests
  ],
});

// Poll for completion
const result = await client.messages.batches.retrieve(batch.id);
console.log(result.processing_status); // "ended" when done

Batch API is ~50% cheaper than individual calls. Good for bulk classification, data extraction, report generation.

Environment setup

# Install SDK
npm install @anthropic-ai/sdk

# Set API key
export ANTHROPIC_API_KEY=sk-ant-...

# Or use .env
echo "ANTHROPIC_API_KEY=sk-ant-..." >> .env

# Python SDK
pip install anthropic

import anthropic
client = anthropic.Anthropic()  # reads ANTHROPIC_API_KEY from env

message = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello!"}]
)
print(message.content[0].text)

Wondering how Claude stacks up? Read Claude vs Gemini 2.5 for Coding: Honest Comparison.

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