Navigating the Claude API Feature Landscape: A Practical Guide to Capabilities, Tools, and Infrastructure

Claude's API surface is organized into five core areas: model capabilities, tools, tool infrastructure, context management, and files and assets. Understanding how these areas work together is essential for building efficient, scalable applications with Claude. This guide walks through each area with practical code examples and best practices.

1. Model Capabilities: Steering Claude’s Outputs

Model capabilities control how Claude reasons and formats responses. Key features include:

• Extended Thinking: Claude can reason step-by-step before answering, improving accuracy on complex tasks.
• Adaptive Thinking: Dynamically decides when and how much to think—ideal for Opus 4.7.
• Structured Outputs: Enforce JSON or other structured formats.
• Citations: Ground responses in source documents.

Example: Using Extended Thinking with Effort Control

import anthropic
client = anthropic.Anthropic()
response = client.messages.create(
    model="claude-opus-4-20250514",
    max_tokens=1024,
    thinking={
        "type": "enabled",
        "budget_tokens": 2048,
        "effort": "high"  # low, medium, high
    },
    messages=[
        {"role": "user", "content": "Solve this complex math problem: ∫(x^2 * e^x) dx"}
    ]
)
print(response.content[0].text)

Tip: Use effort to balance reasoning depth and latency. High effort is best for math, logic, or multi-step analysis.

Structured Outputs with JSON Mode

response = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "List three planets and their moons as JSON."}
    ],
    response_format={"type": "json_object"}
)
import json
data = json.loads(response.content[0].text)
print(data)

2. Tools: Letting Claude Act in the World

Tools extend Claude’s capabilities beyond text generation. Claude can call functions, fetch web pages, execute code, and more.

Built-in Tools

Example: Tool Use with a Custom Function

def get_weather(city: str) -> str:
    # Simulated weather lookup
    return f"The weather in {city} is sunny, 22°C."
response = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=512,
    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 Paris?"}
    ]
)
Claude will return a tool_use block
print(response.content)

Parallel Tool Use

Claude can call multiple tools in one turn, reducing latency for independent tasks.

tools = [
    {
        "name": "search_flights",
        "description": "Search for flight options",
        "input_schema": {
            "type": "object",
            "properties": {
                "origin": {"type": "string"},
                "destination": {"type": "string"},
                "date": {"type": "string"}
            },
            "required": ["origin", "destination", "date"]
        }
    },
    {
        "name": "search_hotels",
        "description": "Search for hotel availability",
        "input_schema": {
            "type": "object",
            "properties": {
                "city": {"type": "string"},
                "check_in": {"type": "string"},
                "check_out": {"type": "string"}
            },
            "required": ["city", "check_in", "check_out"]
        }
    }
]
Claude may call both tools simultaneously

3. Tool Infrastructure: Discovery and Orchestration at Scale

When you have many tools, you need infrastructure to manage them. Key concepts:

• Tool Runner (SDK): Automates tool execution and result handling.
• Strict Tool Use: Forces Claude to use only the tools you define.
• Tool Combinations: Chain tools together for complex workflows.
• Programmatic Tool Calling: Call tools from your code without waiting for Claude.

Example: Tool Runner with Error Handling

import Anthropic from '@anthropic-ai/sdk';
const client = new Anthropic();
const response = await client.messages.create({
  model: 'claude-sonnet-4-20250514',
  max_tokens: 1024,
  tools: [
    {
      name: 'database_query',
      description: 'Execute a read-only SQL query',
      input_schema: {
        type: 'object',
        properties: {
          query: { type: 'string' }
        },
        required: ['query']
      }
    }
  ],
  tool_choice: { type: 'any' },  // Force tool use
  messages: [
    { role: 'user', content: 'Get all users who signed up last week' }
  ]
});
// Handle tool calls
for (const block of response.content) {
  if (block.type === 'tool_use') {
    console.log(Calling tool: ${block.name});
    console.log(Input: ${JSON.stringify(block.input)});
    // Execute and return result
  }
}

4. Context Management: Keeping Long Sessions Efficient

Claude supports up to 1M tokens of context. But long sessions need careful management to control cost and latency.

• Context Windows: Up to 1M tokens for processing large documents.
• Compaction: Summarize or prune old context to stay within limits.
• Prompt Caching: Cache repeated system prompts or document chunks to reduce cost and latency.
• Token Counting: Estimate token usage before sending.

Example: Prompt Caching

response = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    system=[
        {
            "type": "text",
            "text": "You are a helpful assistant with knowledge of our product documentation.",
            "cache_control": {"type": "ephemeral"}
        }
    ],
    messages=[
        {"role": "user", "content": "How do I reset my password?"}
    ]
)
print(f"Cache read: {response.usage.cache_read_input_tokens}")
print(f"Cache creation: {response.usage.cache_creation_input_tokens}")

Cost Tip: Caching can reduce input token costs by up to 90% for repeated system prompts or large reference documents.

5. Files and Assets: Managing Documents and Data

Claude can process files directly—PDFs, images, code files, and more.

• PDF Support: Extract text and layout from PDFs.
• Images and Vision: Claude can analyze images (photos, diagrams, screenshots).
• Files API: Upload and reference files in conversations.

Example: Sending an Image for Analysis

import base64
with open("chart.png", "rb") as f:
    image_data = base64.b64encode(f.read()).decode("utf-8")
response = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "source": {
                        "type": "base64",
                        "media_type": "image/png",
                        "data": image_data
                    }
                },
                {
                    "type": "text",
                    "text": "Describe this chart and explain the trend."
                }
            ]
        }
    ]
)
print(response.content[0].text)

Feature Availability and Lifecycle

Features on the Claude Platform go through stages:

Check the Availability column in the official docs for each feature’s status on your platform (Claude API, AWS Bedrock, Vertex AI, Microsoft Foundry).

Best Practices for Building with Claude

• Start with model capabilities and tools – these are the building blocks.
• Use prompt caching for repeated system prompts or large reference documents.
• Leverage batch processing for non-real-time workloads (50% cost savings).
• Monitor token usage with the token counting API to avoid surprises.
• Design for tool orchestration – use Tool Runner or programmatic calling for complex workflows.

Key Takeaways

• Claude’s API is organized into five areas: model capabilities, tools, tool infrastructure, context management, and files.
• Use extended thinking with effort control for complex reasoning tasks, and structured outputs for reliable JSON responses.
• Prompt caching and batch processing are your primary levers for reducing cost and latency.
• Tools can be used in parallel, chained, or forced with strict tool use for deterministic workflows.
• Always check feature availability (Beta vs. GA) before building production systems.