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

Claude's API is more than just a text-in, text-out interface. It's a rich ecosystem of capabilities designed to handle complex reasoning, tool orchestration, long-running conversations, and multimodal inputs. Whether you're building a customer support agent, a code assistant, or a document analysis pipeline, understanding the full API surface is key to unlocking Claude's potential.

This guide covers the five core areas of the Claude API:

• Model capabilities – reasoning depth, structured outputs, streaming
• Tools – letting Claude act on the web or in your environment
• Tool infrastructure – discovery and orchestration at scale
• Context management – keeping long sessions efficient
• Files and assets – managing documents and data

We'll also touch on availability (Beta vs. GA) and practical code examples so you can start building immediately.

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1. Model Capabilities: Steering Claude's Output

Claude's model capabilities let you control how it reasons and what it returns. These are the building blocks for any application.

Extended Thinking & Adaptive Thinking

For complex tasks like math proofs, code generation, or multi-step reasoning, Claude can "think" before responding. The Extended Thinking feature allocates internal tokens for reasoning, improving accuracy on hard problems.

Adaptive Thinking (recommended for Opus 4.5+) lets Claude decide dynamically how much to think. Use the effort parameter to control depth:

import anthropic
client = anthropic.Anthropic()
response = client.messages.create(
    model="claude-opus-4-20250514",
    max_tokens=4096,
    thinking={
        "type": "enabled",
        "budget_tokens": 2048,
        "effort": "high"  # low, medium, high, or adaptive
    },
    messages=[{"role": "user", "content": "Prove that the square root of 2 is irrational."}]
)
print(response.content[0].thinking)  # The reasoning chain
print(response.content[1].text)      # The final answer

Tip: Use effort: "adaptive" for Opus 4.5+ to let Claude decide the thinking depth automatically. This saves tokens on simple queries and allocates more for complex ones.

Structured Outputs

Claude can return responses in a structured format (JSON, XML, or custom schemas). This is critical for programmatic consumption:

response = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    system="You are a data extraction assistant. Always respond in valid JSON.",
    messages=[{
        "role": "user",
        "content": "Extract the name, date, and total amount from this invoice: Invoice #1234, dated 2025-03-15, amount $2,450.00"
    }]
)

Streaming & Refusals

Streaming lets you receive tokens as they're generated, reducing perceived latency. Streaming refusals allow you to detect content policy violations mid-stream.

stream = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    stream=True,
    messages=[{"role": "user", "content": "Write a short poem about AI."}]
)
for event in stream:
    if event.type == "content_block_delta":
        print(event.delta.text, end="")
    elif event.type == "refusal":
        print(f"\n[Refusal detected]: {event.refusal.reason}")

Batch Processing

For high-volume, non-real-time tasks, the Batch API offers 50% cost savings. Send up to 10,000 queries per batch:

batch = client.batches.create(
    requests=[
        {
            "custom_id": "req-001",
            "params": {
                "model": "claude-sonnet-4-20250514",
                "max_tokens": 1024,
                "messages": [{"role": "user", "content": "Summarize: ..."}]
            }
        },
        # ... more requests
    ]
)
Poll for completion
result = client.batches.retrieve(batch.id)

Note: Batch processing is not ZDR eligible – data may be retained for processing. Use standard API for sensitive data.

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2. Tools: Letting Claude Act in the World

Tools extend Claude's capabilities beyond text. Claude can call functions, fetch web pages, execute code, and even control a computer.

Web Search & Web Fetch

Claude can search the web or fetch specific URLs to ground responses in real-time information:

response = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=2048,
    tools=[{
        "type": "web_search",
        "name": "web_search"
    }],
    messages=[{"role": "user", "content": "What's the latest news about Claude 4?"}]
)

Code Execution Tool

Claude can write and execute Python code in a sandboxed environment. Perfect for data analysis, calculations, or generating visualizations:

response = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=4096,
    tools=[{
        "type": "code_execution",
        "name": "execute_python"
    }],
    messages=[{"role": "user", "content": "Calculate the Fibonacci sequence up to 100 and plot it."}]
)

Computer Use (Beta)

For advanced automation, Claude can control a virtual desktop environment – clicking buttons, typing text, and navigating UIs. This is ideal for testing or legacy system integration.

Parallel Tool Use

Claude can call multiple tools simultaneously to speed up workflows:

response = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=4096,
    tools=[web_search_tool, code_execution_tool],
    parallel_tool_calls=True,
    messages=[{"role": "user", "content": "Find today's stock prices for AAPL and TSLA, then calculate their P/E ratios."}]
)

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3. Tool Infrastructure: Discovery & Orchestration

When you have many tools, managing them becomes a challenge. Claude's tool infrastructure handles:

• Tool search – automatically find the right tool for a task
• Tool combinations – chain multiple tools together
• Fine-grained tool streaming – stream results from each tool independently
• Programmatic tool calling – call tools from your own code without Claude

Strict Tool Use

For safety-critical applications, enable strict tool use to prevent Claude from deviating from defined tools:

response = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=2048,
    tools=[...],
    tool_choice={"type": "any"},  # Claude must use a tool
    strict=True,
    messages=[...]
)

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4. Context Management: Keeping Sessions Efficient

Long conversations consume tokens. Claude provides several mechanisms to manage context windows efficiently.

Context Windows

Claude supports up to 1M tokens of context – enough to process entire codebases or lengthy documents. But bigger contexts cost more. Use context compaction to summarize older turns:

response = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    system="Compress the conversation history into a concise summary, preserving key facts and decisions.",
    messages=[
        {"role": "user", "content": "Here is the full conversation log..."}
    ]
)

Prompt Caching

Prompt caching reduces latency and cost by reusing common prefixes (system prompts, few-shot examples) across multiple requests:

response = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    system=[
        {
            "type": "text",
            "text": "You are a helpful assistant specialized in Python programming.",
            "cache_control": {"type": "ephemeral"}
        }
    ],
    messages=[{"role": "user", "content": "Explain decorators."}]
)

Tip: Cache the system prompt and the first few user messages for maximum savings. Cache hits reduce latency by up to 80%.

Context Editing

For interactive applications, you can edit the context window – insert, delete, or replace messages without rebuilding the entire history.

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5. Files and Assets: Working with Documents

Claude supports multiple input modalities:

• PDF support – extract text, tables, and layout
• Images – analyze diagrams, screenshots, or photos
• Files API – upload and reference documents

import base64
with open("report.pdf", "rb") as f:
    pdf_data = base64.b64encode(f.read()).decode()
response = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=2048,
    messages=[{
        "role": "user",
        "content": [
            {
                "type": "document",
                "source": {
                    "type": "base64",
                    "media_type": "application/pdf",
                    "data": pdf_data
                }
            },
            {
                "type": "text",
                "text": "Summarize the key findings from this report."
            }
        ]
    }]
)

Citations

Claude can cite exact sentences from source documents, making it ideal for legal, academic, or compliance use cases:

response = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=2048,
    citations=True,
    messages=[
        {"role": "user", "content": "What does the contract say about termination clauses?"}
    ]
)
for citation in response.citations:
    print(f"Source: {citation.document_title}, Page {citation.page_number}")
    print(f"Quote: {citation.quoted_text}")

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Feature Availability at a Glance

Not all features are available everywhere. Here's a quick reference:

Beta features may change significantly. Use GA features for production workloads.

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Putting It All Together: A Practical Example

Let's build a research assistant that searches the web, reads a PDF, and generates a structured report:

import anthropic
import base64
client = anthropic.Anthropic()
Load PDF
with open("research_paper.pdf", "rb") as f:
    pdf_b64 = base64.b64encode(f.read()).decode()
response = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=4096,
    system="You are a research assistant. Search the web for recent developments, then analyze the provided PDF.",
    tools=[
        {"type": "web_search", "name": "web_search"},
        {"type": "code_execution", "name": "execute_python"}
    ],
    citations=True,
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "document",
                    "source": {
                        "type": "base64",
                        "media_type": "application/pdf",
                        "data": pdf_b64
                    }
                },
                {
                    "type": "text",
                    "text": "Search for the latest research on this topic, then summarize the PDF and the search results in a structured JSON report."
                }
            ]
        }
    ]
)
print(response.content[0].text)

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Key Takeaways

• Claude's API is organized into five pillars: model capabilities, tools, tool infrastructure, context management, and file handling. Master each to build sophisticated applications.
• Use Extended Thinking for complex reasoning and Adaptive Thinking for Opus 4.5+ to save tokens on simple tasks.
• Leverage tools like web search and code execution to give Claude real-world agency, but use strict tool mode for safety.
• Prompt caching and context compaction are essential for cost-effective long-running sessions – cache system prompts and frequent prefixes.
• Check feature availability before building – GA features are production-ready, while Beta features may change. Batch processing saves 50% but isn't ZDR eligible.