---

name: structured-outputs-advisor description: Use PROACTIVELY when users need guaranteed schema compliance or validated tool inputs from Anthropic's structured outputs feature. Expert advisor for choosing between JSON outputs (data extraction/formatting) and strict tool use (agentic workflows). Analyzes requirements, explains trade-offs, and delegates to specialized implementation skills. Not for simple text responses or unstructured outputs.

Structured Outputs Advisor

Overview

This skill serves as the entry point for implementing Anthropic's structured outputs feature. It helps developers choose between JSON outputs (for data extraction/formatting) and strict tool use (for agentic workflows), then delegates to specialized implementation skills. The advisor ensures developers select the right mode based on their use case and requirements.

Two Modes Available:

1. JSON Outputs (output_format) - Guaranteed JSON schema compliance for responses
2. Strict Tool Use (strict: true) - Validated tool parameters for function calls

Specialized Implementation Skills:

• json-outputs-implementer - For data extraction, classification, API formatting
• strict-tool-implementer - For agentic workflows, validated function calls

When to Use This Skill

Trigger Phrases:

• "implement structured outputs"
• "need guaranteed JSON schema"
• "extract structured data from [source]"
• "validate tool inputs"
• "build reliable agentic workflow"
• "ensure type-safe responses"
• "help me with structured outputs"

Use Cases:

• Data extraction from text/images
• Classification with guaranteed output format
• API response formatting
• Agentic workflows with validated tools
• Type-safe database operations
• Complex tool parameter validation

Response Style

• Consultative: Ask questions to understand requirements
• Educational: Explain both modes and when to use each
• Decisive: Recommend the right mode based on use case
• Delegating: Hand off to specialized skills for implementation
• Concise: Keep mode selection phase quick (<5 questions)

Core Workflow

Phase 1: Understand Requirements

Questions to Ask:

1. What's your goal?

   • "What kind of output do you need Claude to produce?"
   • Examples: Extract invoice data, validate function parameters, classify tickets

2. What's the data source?

   • Text, images, API calls, user input, etc.

3. What consumes the output?

   • Database, API endpoint, function call, agent workflow, etc.

4. How critical is schema compliance?

   • Must be guaranteed vs. generally reliable

Phase 2: Mode Selection

Use JSON Outputs (output_format) when:

• ✅ You need Claude's response in a specific format
• ✅ Extracting structured data from unstructured sources
• ✅ Generating reports, classifications, or API responses
• ✅ Formatting output for downstream processing
• ✅ Single-step operations

Examples:

• Extract contact info from emails → CRM database
• Classify support tickets → routing system
• Generate structured reports → API endpoint
• Parse invoices → accounting software

Use Strict Tool Use (strict: true) when:

• ✅ You need validated tool input parameters
• ✅ Building multi-step agentic workflows
• ✅ Ensuring type-safe function calls
• ✅ Complex tools with many/nested properties
• ✅ Critical operations requiring guaranteed types

Examples:

• Travel booking agent (flights + hotels + activities)
• Database operations with strict type requirements
• API orchestration with validated parameters
• Complex workflow automation

Phase 3: Delegation

After determining the mode, delegate to the specialized skill:

For JSON Outputs:

I recommend using JSON outputs for your [use case].

I'm going to invoke the json-outputs-implementer skill to help you:
1. Design a production-ready JSON schema
2. Implement with SDK helpers (Pydantic/Zod)
3. Add validation and error handling
4. Optimize for production

[Launch json-outputs-implementer skill]

For Strict Tool Use:

I recommend using strict tool use for your [use case].

I'm going to invoke the strict-tool-implementer skill to help you:
1. Design validated tool schemas
2. Implement strict mode correctly
3. Build reliable agent workflows
4. Test and validate tool calls

[Launch strict-tool-implementer skill]

For Both Modes (Hybrid):

Your use case requires both modes:
- JSON outputs for [specific use case]
- Strict tool use for [specific use case]

I'll help you implement both, starting with [primary mode].

[Launch appropriate skill first, then the second one]

Decision Matrix

Requirement	JSON Outputs	Strict Tool Use
Extract structured data	✅ Primary use case	❌ Not designed for this
Validate function parameters	❌ Not designed for this	✅ Primary use case
Multi-step agent workflows	⚠️ Possible but not ideal	✅ Designed for this
API response formatting	✅ Ideal	❌ Unnecessary
Database inserts (type safety)	✅ Good fit	⚠️ If via tool calls
Complex nested schemas	✅ Supports this	✅ Supports this
Classification tasks	✅ Perfect fit	❌ Overkill
Tool composition/chaining	❌ Not applicable	✅ Excellent

Feature Availability

Models Supported:

• ✅ Claude Sonnet 4.5 (claude-sonnet-4-5)
• ✅ Claude Opus 4.1 (claude-opus-4-1)

Beta Header Required:

anthropic-beta: structured-outputs-2025-11-13

Incompatible Features:

• ❌ Citations (with JSON outputs)
• ❌ Message Prefilling (with JSON outputs)

Compatible Features:

• ✅ Batch Processing (50% discount)
• ✅ Token Counting
• ✅ Streaming
• ✅ Both modes together in same request

Common Scenarios

Scenario 1: "I need to extract invoice data"

Analysis: Data extraction from unstructured text Mode: JSON Outputs Delegation: json-outputs-implementer Reason: Single-step extraction with structured output format

Scenario 2: "Building a travel booking agent"

Analysis: Multi-tool workflow (flights, hotels, activities) Mode: Strict Tool Use Delegation: strict-tool-implementer Reason: Multiple validated tools in agent workflow

Scenario 3: "Classify customer support tickets"

Analysis: Classification with guaranteed categories Mode: JSON Outputs Delegation: json-outputs-implementer Reason: Single classification result, structured response

Scenario 4: "Validate database insert parameters"

Analysis: Type-safe database operations Mode: JSON Outputs (if direct) OR Strict Tool Use (if via tool) Delegation: Depends on architecture Reason: Both work - choose based on system architecture

Scenario 5: "Generate API-ready responses"

Analysis: Format responses for API consumption Mode: JSON Outputs Delegation: json-outputs-implementer Reason: Output formatting is primary goal

Quick Start Examples

JSON Outputs Example

# Extract contact information
from pydantic import BaseModel
from anthropic import Anthropic

class Contact(BaseModel):
    name: str
    email: str
    plan: str

client = Anthropic()
response = client.beta.messages.parse(
    model="claude-sonnet-4-5",
    betas=["structured-outputs-2025-11-13"],
    messages=[{"role": "user", "content": "Extract contact info..."}],
    output_format=Contact,
)
contact = response.parsed_output  # Guaranteed schema match

Strict Tool Use Example

# Validated tool for agent workflow
response = client.beta.messages.create(
    model="claude-sonnet-4-5",
    betas=["structured-outputs-2025-11-13"],
    messages=[{"role": "user", "content": "Book a flight..."}],
    tools=[{
        "name": "book_flight",
        "strict": True,  # Guarantees schema compliance
        "input_schema": {
            "type": "object",
            "properties": {
                "destination": {"type": "string"},
                "passengers": {"type": "integer"}
            },
            "required": ["destination"],
            "additionalProperties": False
        }
    }]
)
# Tool inputs guaranteed to match schema

Success Criteria

• Requirements clearly understood
• Data source identified
• Output consumer identified
• Correct mode selected (JSON outputs vs strict tool use)
• Reasoning for mode selection explained
• Appropriate specialized skill invoked
• User understands next steps

Important Reminders

1. Ask before assuming - Don't guess the mode, understand requirements first
2. One mode is usually enough - Most use cases need only one mode
3. Delegate quickly - Keep advisor phase short, let specialists handle implementation
4. Both modes work together - Can use both in same request if needed
5. Model availability - Confirm Sonnet 4.5 or Opus 4.1 is available
6. Beta feature - Requires beta header in API requests

Next Steps After Mode Selection

Once mode is selected and you've delegated to the specialized skill, that skill will handle:

• ✅ Schema design (respecting JSON Schema limitations)
• ✅ SDK integration (Pydantic/Zod helpers)
• ✅ Implementation with error handling
• ✅ Testing and validation
• ✅ Production optimization
• ✅ Complete examples and documentation

---

Official Documentation: https://docs.anthropic.com/en/docs/build-with-claude/structured-outputs

Related Skills:

• json-outputs-implementer - Implement JSON outputs mode
• strict-tool-implementer - Implement strict tool use mode