Mastering the Claude API: A Complete Guide to Features, Tools, and Context Management

Claude's API is more than just a text generation endpoint. It's a full-featured platform designed to give you fine-grained control over how Claude reasons, interacts with external systems, and manages long-running conversations. Whether you're building a simple chatbot or a complex agentic workflow, understanding the API's surface areas is essential.

This guide breaks down the five core areas of the Claude API—model capabilities, tools, tool infrastructure, context management, and files/assets—and shows you how to use them effectively.

1. Model Capabilities: Steering Claude's Reasoning and Output

Model capabilities are the direct levers you pull to control how Claude thinks and responds. These include context windows, thinking modes, structured outputs, and more.

Context Windows

Claude supports context windows up to 1 million tokens (depending on the model), allowing you to process entire codebases, long documents, or extended conversations in a single request.

import anthropic
client = anthropic.Anthropic()
response = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=4096,
    messages=[
        {"role": "user", "content": "Summarize this 500-page document..."}
    ]
)

Adaptive Thinking

For complex reasoning tasks, you can enable adaptive thinking—Claude decides when and how much to "think" before responding. Use the effort parameter to control depth.

response = client.messages.create(
    model="claude-opus-4-20250514",
    thinking={"type": "enabled", "budget_tokens": 2048},
    messages=[{"role": "user", "content": "Solve this advanced math problem..."}]
)

Structured Outputs & Citations

Claude can output structured JSON directly, and with Citations, it can ground responses in source documents by referencing exact sentences.

response = client.messages.create(
    model="claude-sonnet-4-20250514",
    messages=[{"role": "user", "content": "Extract key dates from this contract..."}],
    response_format={"type": "json_object"}
)

Batch Processing

For high-volume, non-real-time tasks, use batch processing to send large numbers of requests asynchronously. Batch API calls cost 50% less than standard calls.

batch = client.batches.create(
    requests=[
        {"custom_id": "req-1", "params": {"model": "claude-sonnet-4-20250514", "messages": [...]}},
        {"custom_id": "req-2", "params": {"model": "claude-sonnet-4-20250514", "messages": [...]}}
    ]
)

2. Tools: Letting Claude Take Action

Tools extend Claude's capabilities beyond text generation. You can define custom tools or use built-in ones like web search, code execution, and file operations.

Defining Tools

Tools are defined as JSON schemas. Claude can request to call them, and you execute the action and return results.

tools = [
    {
        "name": "get_weather",
        "description": "Get current weather for a city",
        "input_schema": {
            "type": "object",
            "properties": {
                "city": {"type": "string"}
            },
            "required": ["city"]
        }
    }
]
response = client.messages.create(
    model="claude-sonnet-4-20250514",
    tools=tools,
    messages=[{"role": "user", "content": "What's the weather in Tokyo?"}]
)

Parallel Tool Use

Claude can call multiple tools in parallel, reducing latency for independent operations.

# Claude may request multiple tool calls in a single response
for tool_call in response.content:
    if tool_call.type == "tool_use":
        # Handle each tool call independently
        pass

Built-in Tools

Claude offers several server-side tools you can enable:

• Web Search Tool: Fetch real-time information from the web.
• Code Execution Tool: Run Python code in a sandboxed environment.
• File Editor Tool: Read, write, and edit files on the server.
• Computer Use Tool: Control a virtual desktop environment.

3. Tool Infrastructure: Orchestration at Scale

When building complex agents, you need more than just tool definitions. Claude's tool infrastructure handles discovery, orchestration, and context management for large tool sets.

Tool Runner (SDK)

The Tool Runner SDK simplifies building agents that use multiple tools. It handles tool call routing, error handling, and retries.

from anthropic import ToolRunner
runner = ToolRunner(tools=[get_weather, search_database])
result = runner.run("Find all orders from last week and check the weather for each shipping city")

Strict Tool Use

For deterministic workflows, enable strict tool use to force Claude to use specific tools in a defined order.

Prompt Caching with Tools

Cache tool definitions and system prompts to reduce latency and cost when using the same tools across multiple requests.

4. Context Management: Keeping Long Sessions Efficient

Long-running conversations or large document processing require careful context management.

Context Windows & Compaction

Claude supports up to 1M tokens, but you can use compaction to summarize or prune older context while preserving key information.

# Use compaction to reduce context size
response = client.messages.create(
    model="claude-sonnet-4-20250514",
    system="Compact the conversation history, keeping all important facts.",
    messages=[...]
)

Prompt Caching

Cache frequently used system prompts, tool definitions, or document chunks to reduce latency and cost.

response = client.messages.create(
    model="claude-sonnet-4-20250514",
    system=[
        {
            "type": "text",
            "text": "You are a helpful assistant...",
            "cache_control": {"type": "ephemeral"}
        }
    ],
    messages=[...]
)

Token Counting

Estimate token usage before sending a request to avoid hitting limits.

token_count = client.messages.count_tokens(
    model="claude-sonnet-4-20250514",
    messages=[{"role": "user", "content": "Hello"}]
)
print(token_count.input_tokens)

5. Files and Assets: Managing Input Data

Claude can process various file types, including PDFs, images, and code files.

PDF Support

Upload PDFs directly and Claude will extract and understand their content.

import base64
with open("document.pdf", "rb") as f:
    pdf_data = base64.b64encode(f.read()).decode()
response = client.messages.create(
    model="claude-sonnet-4-20250514",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "document",
                    "source": {
                        "type": "base64",
                        "media_type": "application/pdf",
                        "data": pdf_data
                    }
                },
                {"type": "text", "text": "Summarize this PDF"}
            ]
        }
    ]
)

Image & Vision

Claude can analyze images for tasks like object detection, OCR, and visual reasoning.

with open("chart.png", "rb") as f:
    image_data = base64.b64encode(f.read()).decode()
response = client.messages.create(
    model="claude-sonnet-4-20250514",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "source": {
                        "type": "base64",
                        "media_type": "image/png",
                        "data": image_data
                    }
                },
                {"type": "text", "text": "What does this chart show?"}
            ]
        }
    ]
)

Feature Availability & Lifecycle

Features on the Claude platform follow a lifecycle:

Always check the feature's documentation for its current status and any platform-specific limitations.

Best Practices

• Start simple: Begin with model capabilities and tools before adding complex infrastructure.
• Use caching: Cache system prompts and tool definitions to reduce latency and cost.
• Monitor token usage: Use the token counting endpoint to stay within limits.
• Leverage batch processing: For non-real-time workloads, batch processing saves 50% on API costs.
• Test with streaming: Enable streaming for real-time user experiences.

Key Takeaways

• Claude's API is organized into five areas: model capabilities, tools, tool infrastructure, context management, and files/assets.
• Use adaptive thinking for complex reasoning tasks and structured outputs for reliable JSON responses.
• Tools extend Claude's capabilities—define custom tools or use built-in ones like web search and code execution.
• Context management features like compaction and prompt caching keep long-running sessions efficient and cost-effective.
• Batch processing offers 50% cost savings for high-volume, non-real-time workloads.

Ready to build? Start with the Quickstart and explore the API reference for detailed endpoint documentation.