json{
  "system": "You are a code review assistant. CRITICAL RULES: 1) Flag security vulnerabilities as HIGH priority 2) Check type safety rigorously 3) Verify error handling completeness.\n\n[Supporting guidelines...]\n\nREMEMBER: Every response must include severity labels and specific line references.",
  "messages": [...]
}

json{
  "tools": [{
    "name": "analysis_output",
    "description": "Structure the code analysis results",
    "input_schema": {
      "type": "object",
      "properties": {
        "severity": {"type": "string", "enum": ["low", "medium", "high", "critical"]},
        "issues": {
          "type": "array",
          "items": {
            "type": "object",
            "properties": {
              "line": {"type": "integer"},
              "description": {"type": "string"},
              "fix_suggestion": {"type": "string"}
            },
            "required": ["line", "description", "fix_suggestion"],
            "additionalProperties": false
          }
        }
      },
      "required": ["severity", "issues"],
      "additionalProperties": false
    }
  }],
  "tool_choice": {"type": "tool", "name": "analysis_output"}
}

xml<code_review>
<summary>Found 3 security issues requiring immediate attention</summary>
<issues>
<issue severity="high" line="42">
SQL injection vulnerability in user input handling
</issue>
</issues>
</code_review>

pythonimport anthropic

client = anthropic.Anthropic()

response = client.messages.create(
    model="claude-3-5-haiku-20241022",
    max_tokens=4000,
    system="You are a database optimization expert. Think through each query systematically.",
    messages=[{
        "role": "user",
        "content": """Analyze this query for performance bottlenecks:
        
        SELECT u.name, p.title, c.content 
        FROM users u 
        JOIN posts p ON u.id = p.user_id 
        JOIN comments c ON p.id = c.post_id 
        WHERE u.created_at > '2024-01-01' 
        ORDER BY p.created_at DESC;
        
        <thinking>
        Let me work through this step by step:
        1. What tables are involved?
        2. How are the joins structured?
        3. What indexes might be needed?
        4. What's the likely cardinality?
        </thinking>
        
        Provide specific optimization recommendations."""
    }]
)

xml<examples>
<example>
<input>Function returns undefined for edge case</input>
<output>
Severity: Medium
Line: 15
Issue: Function lacks return statement for null input
Fix: Add explicit return value or throw descriptive error
Code: if (input === null) return null; // or throw new Error("Input required")
</output>
</example>
</examples>

pythondef validate_claude_response(response_text):
    try:
        # Extract XML content
        import xml.etree.ElementTree as ET
        root = ET.fromstring(f"<root>{response_text}</root>")
        
        # Validate required fields
        severity = root.find('.//severity')
        issues = root.findall('.//issue')
        
        if not severity or not issues:
            return False, "Missing required analysis components"
            
        return True, None
    except ET.ParseError as e:
        return False, f"XML parsing failed: {e}"

def robust_claude_call(prompt, max_retries=3):
    for attempt in range(max_retries):
        response = client.messages.create(
            model="claude-3-5-sonnet-20241022",
            messages=[{"role": "user", "content": prompt}]
        )
        
        is_valid, error = validate_claude_response(response.content[0].text)
        if is_valid:
            return response.content[0].text
        
        # Add correction guidance for retry
        prompt += f"\n\nPrevious response had formatting issues: {error}. Please ensure proper XML structure."
    
    raise Exception("Failed to get valid response after retries")