JSON Schema Validator
Validate JSON data against schemas with real-time feedback
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Validate JSON data against schemas with real-time feedback
In 2025, JSON Schema validation has become essential for ensuring AI and LLM outputs meet exact specifications. With OpenAI's 100% schema compliance in structured output mode, Claude's reliable tool calling, and the rise of AI agents requiring precise data contracts, our JSON Schema validator is purpose-built for the AI era. Whether you're validating ChatGPT function calls, ensuring Claude's responses match your schema, or building multi-agent systems with strict data contracts, our tool provides instant validation with AI-specific insights.
JSON Schema has become the cornerstone of reliable AI applications. By defining exact schemas for LLM outputs, developers can transform unpredictable AI responses into structured, validated data. Major AI platforms now natively support JSON Schema:
response_format.json_schemagenai.protos.SchemaCreating effective schemas for AI requires understanding both JSON Schema syntax and AI behavior patterns. Here's a practical example for a customer support AI agent:
{
"$schema": "http://json-schema.org/draft-07/schema#",
"title": "Customer Support AI Response",
"type": "object",
"required": ["intent", "response", "confidence", "actions"],
"properties": {
"intent": {
"type": "string",
"enum": ["question", "complaint", "feedback", "request"],
"description": "The classified intent of the customer message"
},
"response": {
"type": "string",
"minLength": 10,
"maxLength": 500,
"description": "The AI's response to the customer"
},
"confidence": {
"type": "number",
"minimum": 0,
"maximum": 1,
"description": "AI's confidence in its interpretation"
},
"actions": {
"type": "array",
"items": {
"type": "object",
"required": ["type", "parameters"],
"properties": {
"type": {"type": "string"},
"parameters": {"type": "object"}
}
}
},
"reasoning": {
"type": "string",
"description": "Internal reasoning (improves accuracy by 40%)"
}
}
}💡 Pro Tip: Adding a "reasoning" field increases AI accuracy even if you don't use it in production - it forces the model to think through its response systematically.
Ensure data consistency across your entire application stack by validating against a single source of truth.
Schemas serve as living documentation, clearly defining what data structures your APIs expect and return.
Catch data issues at the boundary of your system before they propagate and cause harder-to-debug problems.
Manage API evolution with schema versioning, ensuring backward compatibility while adding new features.
type, enum, const - Define the data type and allowed values
minimum, maximum, multipleOf, exclusiveMinimum, exclusiveMaximum - Set numeric constraints
minLength, maxLength, pattern, format - Control string validation
items, minItems, maxItems, uniqueItems, contains - Define array constraints
properties, required, additionalProperties, patternProperties, dependencies - Structure object validation
if, then, else, allOf, anyOf, oneOf, not - Create complex validation logic
| Draft Version | Release Year | Key Features | Status |
|---|---|---|---|
| Draft 2020-12 | 2020 | $dynamicRef, $dynamicAnchor, prefixItems | Latest |
| Draft 2019-09 | 2019 | $recursiveRef, $recursiveAnchor, unevaluatedProperties | Stable |
| Draft-07 | 2018 | if/then/else, $comment, readOnly/writeOnly | Widely Used |
| Draft-06 | 2017 | const, contains, propertyNames, examples | Legacy |
| Draft-04 | 2013 | Basic validation, $ref, definitions | Deprecated |
1. Start Simple, Iterate: Begin with basic type validation and gradually add constraints as you understand your data requirements better. Over-constraining early can lead to brittle schemas.
2. Use References Wisely: Leverage $ref and $defs to create reusable schema components, reducing duplication and improving maintainability.
3. Document Everything: Use title, description, andexamples keywords to make your schemas self-documenting for other developers.
4. Version Your Schemas: Include version information in your schema IDs and maintain backward compatibility when possible. Consider using semantic versioning.
5. Test Edge Cases: Validate both valid and invalid data against your schemas. Ensure error messages are helpful and actionable.
6. Performance Considerations: Be mindful of complex regex patterns and deep recursion which can impact validation performance on large datasets.
As AI models become more sophisticated, ensuring their outputs conform to expected schemas is crucial for building reliable applications. Here are proven patterns for validating different types of AI outputs:
{
"type": "object",
"required": ["title", "content", "metadata"],
"properties": {
"title": {"type": "string", "maxLength": 100},
"content": {"type": "string", "minLength": 100},
"metadata": {
"type": "object",
"properties": {
"readingTime": {"type": "number"},
"keywords": {"type": "array", "items": {"type": "string"}},
"category": {"type": "string"}
}
}
}
}{
"type": "object",
"properties": {
"entities": {
"type": "array",
"items": {
"type": "object",
"required": ["name", "type", "confidence"],
"properties": {
"name": {"type": "string"},
"type": {"enum": ["person", "organization", "location"]},
"confidence": {"type": "number", "minimum": 0, "maximum": 1}
}
}
}
}
}RAG systems combine LLMs with external knowledge bases, requiring strict schema validation for both retrieval and generation phases. Here's how to structure schemas for RAG applications:
{
"$schema": "http://json-schema.org/draft-07/schema#",
"title": "RAG System Response",
"type": "object",
"required": ["query", "retrieved_docs", "answer", "metadata"],
"properties": {
"query": {
"type": "string",
"description": "Original user query"
},
"retrieved_docs": {
"type": "array",
"minItems": 1,
"maxItems": 10,
"items": {
"type": "object",
"required": ["id", "content", "relevance_score"],
"properties": {
"id": {"type": "string"},
"content": {"type": "string", "maxLength": 2000},
"relevance_score": {"type": "number", "minimum": 0, "maximum": 1},
"source": {"type": "string", "format": "uri"}
}
}
},
"answer": {
"type": "string",
"minLength": 50,
"description": "Generated answer based on retrieved documents"
},
"metadata": {
"type": "object",
"properties": {
"model": {"type": "string"},
"total_tokens": {"type": "integer"},
"latency_ms": {"type": "number"},
"citations": {"type": "array", "items": {"type": "string"}}
}
}
}
}In 2025, multi-agent AI systems are becoming standard for complex tasks. JSON Schema ensures reliable communication between agents:
Data doesn't match the expected type. Ensure numbers aren't quoted as strings and arrays aren't objects.
Object is missing properties listed in the 'required' array. Check for typos in property names.
Object contains properties not defined in schema when additionalProperties is false.
String doesn't match the regex pattern. Test patterns separately and ensure proper escaping.
Data doesn't match format constraints like email, uri, date-time. Verify format specifications.
JSON Schema plays a crucial role in API development, particularly in OpenAPI (Swagger) specifications. It enables automatic validation, documentation generation, and client SDK creation. By defining schemas for request bodies, response payloads, and parameters, you create a single source of truth that drives your entire API ecosystem.
Combine multiple schemas using allOf, anyOf, and oneOf to create flexible validation rules that can handle polymorphic data structures.
Use if, then, and else keywords to apply different validation rules based on the data's content, enabling context-aware validation.
Create schemas that validate other schemas, ensuring your schema definitions themselves follow organizational standards and best practices.
Leverage $dynamicRef and $dynamicAnchor in newer drafts for advanced recursive structures and dynamic schema resolution.
JSON is a data format, while JSON Schema is a vocabulary for describing the structure and validation rules for JSON data. Think of JSON Schema as a blueprint that JSON data must follow.
Yes! JSON Schema fully supports nested validation. Use the 'properties' keyword for objects and 'items' for arrays, and you can nest these definitions as deeply as needed.
By default, all properties are optional. Use the 'required' array to specify which properties must be present. You can also use 'minProperties' and 'maxProperties' for additional control.
Yes! Use 'dependencies', 'dependentSchemas', or conditional keywords like 'if/then/else' to create validation rules that depend on other fields' values.
No, JSON Schema is language-agnostic. Validators exist for virtually every programming language, making it perfect for polyglot environments.
Use the schema validation features in your AI SDK (OpenAI's response_format, Claude's tool definitions) or validate post-generation using libraries like Ajv or Pydantic. Always include the schema in your system prompt for best results.
Draft-07 and Draft 2020-12 are most widely supported by AI platforms. OpenAI and Anthropic both support Draft-07 fully, making it the safest choice for AI applications in 2025.
Yes! Studies show that providing explicit schemas improves output accuracy by 35-40%. Adding a "reasoning" field in your schema can further improve accuracy even if you don't use the reasoning in production.
Ready to ensure your JSON data meets its specifications? Our validator supports all major JSON Schema drafts and provides instant, detailed feedback on validation errors. Paste your schema and JSON data above to get started, or use our sample schemas to explore the power of JSON Schema validation.