claude-sdk-expert

name	Claude SDK Expert
description	Build autonomous AI agents using Claude Agent SDK with computer use, tool calling, MCP integration, and production best practices
version	1.1.0
last_updated	"2026-01-06T00:00:00.000Z"
external_version	Claude Opus 4.5, Sonnet 4.5
resources	resources/code-examples.py

Claude SDK Expert Skill

Purpose

Build autonomous AI agents using Claude Agent SDK, leveraging computer use, tool orchestration, and MCP integration for production deployments.

SDK Overview

Claude Agent SDK (2026)

Enables building autonomous agents that control computers, write files, run commands, and iterate on work.

Core Philosophy: Give Claude a computer to unlock agent effectiveness beyond chat.

Key Capabilities

1. Computer Use

Claude can control a computer environment:

• File system operations (read, write, edit)
• Terminal command execution
• Iterative debugging and refinement
• Multi-step autonomous workflows

Use Cases: Finance agents, personal assistants, customer support, development agents, research agents

2. Built-in Tools

Category	Tools
Files	Read, Write, Edit
Commands	Bash
Search	Grep, Glob
Web	WebFetch, WebSearch

3. MCP Integration

Define custom tools via Model Context Protocol servers.

Benefits:

• Standardized tool interface
• Reusable across agents
• Enterprise data connectivity

Popular MCP Servers: GitHub, Slack, PostgreSQL, MongoDB, Stripe, Salesforce

Architecture Patterns

Pattern 1: Autonomous Task Completion

Agent completes multi-step task without intervention.

User Request → Analyze → Subtasks → Execute Tools → Iterate → Result

Pattern 2: Human-in-the-Loop

Agent proposes actions, waits for approval.

Task → Plan → Human Review → Approve? → Execute → Result

Pattern 3: Iterative Refinement

Agent retries on errors automatically.

Attempt 1 → Error → Analyze → Attempt 2 → Success

See: resources/code-examples.py for full implementations

Tool Design Best Practices

DO

• Provide tools relevant to the task
• Use clear, descriptive names
• Write detailed descriptions (Claude reads these!)
• Define strict input schemas
• Implement error handling
• Return structured outputs

DON'T

• Give agents unnecessary tools
• Use ambiguous names ("handler", "processor")
• Skip input validation
• Return raw errors without context
• Hide side effects

See: resources/code-examples.py for good/bad tool examples

MCP Integration

Connecting MCP Servers

mcp_config = {
    "servers": {
        "github": {
            "command": "npx",
            "args": ["-y", "@modelcontextprotocol/server-github"],
            "env": {"GITHUB_TOKEN": os.getenv("GITHUB_TOKEN")}
        }
    }
}

Full example: resources/code-examples.py

Production Best Practices

1. Streaming

Show real-time progress to build user trust.

2. Error Handling

• Catch API errors, rate limits, tool failures
• Implement fallbacks and retries
• Log errors for debugging

3. Cost Optimization

• Use Haiku for simple tasks, Sonnet for complex
• Cache repetitive contexts
• Batch similar requests
• Monitor token usage

4. Security

• Restrict file/command access
• Sanitize dangerous inputs
• Audit all agent actions
• Validate tool outputs

Implementation: resources/code-examples.py

Model Selection (January 2026)

Model	Best For	Pricing (per M tokens)	Speed
claude-opus-4-5	Flagship reasoning, complex agents, highest accuracy	$5 in / $25 out	Slower
claude-sonnet-4-5	Best balance - coding, agents, computer use	$3 in / $15 out	Medium
claude-haiku-4	Simple tasks, format conversions, high-throughput	$0.25 in / $1.25 out	Fast

Note: Opus 4.5 achieved 80.9% on SWE-bench Verified. Sonnet 4.5 supports 1M token context with beta header.

Testing Agents

Unit Testing

Test individual tools in isolation.

Integration Testing

Test agent workflows with multiple tools.

Evaluation Framework

Measure accuracy, latency, tool efficiency.

Examples: resources/code-examples.py

Monitoring Metrics

Metric	Description
Tool Call Success Rate	% of tool invocations succeeding
Task Completion Rate	% of requests fully resolved
Average Iterations	Tool calls per task
Latency	Time to complete requests
Token Usage	Input + output tokens
Error Rate	% of requests with errors

Decision Framework

Use Claude SDK when:

• Building on Anthropic models
• Need computer use capabilities
• Want production-ready agent framework
• Require MCP integration
• Building autonomous agents

Consider alternatives when:

• Committed to OpenAI ecosystem → AgentKit
• Need visual agent builder → AgentKit
• Require complex state machines → LangGraph
• Want full OSS control → AutoGen/LangGraph

Resources

Documentation:

• Agent SDK Docs
• Computer Use Guide
• MCP Integration

GitHub:

• Python SDK
• TypeScript SDK

Key Principles

1. Computer Use is Game-Changing - Leverage file/bash capabilities fully
2. Tools are First-Class - Design tools as carefully as prompts
3. MCP for Data - Use MCP servers for enterprise connectivity
4. Stream for UX - Real-time feedback builds trust
5. Security Always - Validate inputs, restrict permissions, audit
6. Right Model for Task - Haiku for simple, Sonnet for complex

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Build powerful, autonomous agents using Claude's cutting-edge capabilities.