This example demonstrates how to create comprehensive JSON schema evaluations using the Eval Protocol (EP) framework. The evaluation uses the json_schema_reward function to assess whether models generate JSON content that matches expected schemas, with options for both structural validation and LLM-based judgment.
You can find the complete code for this example at test_pytest_json_schema.py.

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Understanding JSON Schema Evaluation

JSON schema evaluation assesses a model’s ability to:
  • Generate valid JSON: Produce syntactically correct JSON content
  • Match expected structure: Create JSON objects that conform to specified schemas
  • Handle complex nested structures: Work with objects, arrays, and mixed data types
  • Extract JSON from responses: Parse JSON content from markdown code blocks or plain text
  • Validate type consistency: Ensure data types match schema specifications
Unlike text-based evaluations that focus on content generation, JSON schema evaluations test a model’s structured data generation capabilities - essential skills for AI systems that need to produce machine-readable, structured outputs.

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Understanding the Dataset Structure

The JSON schema dataset contains diverse test cases that evaluate different aspects of JSON generation, from simple object creation to complex nested structures with various data types.

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Dataset Format

Each entry in the dataset contains:
  • messages: Conversation history with user requests and assistant responses
  • ground_truth: Optional expected response (not used in schema validation)
  • evaluation_result: Pre-computed evaluation scores for validation
  • input_metadata: Additional context including expected schema and test case descriptions

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Example Dataset Entries

Perfect Schema Match: 
{
  "messages": [
    {
      "role": "user",
      "content": "Give me user information in the following JSON format:\n```json\n{\n  \"user\": {\n    \"name\": \"string\",\n    \"age\": \"number\",\n    \"isActive\": \"boolean\"\n  },\n  \"products\": [\n    {\n      \"id\": \"string\",\n      \"price\": \"number\"\n    }\n  ]\n}\n```"
    },
    {
      "role": "assistant",
      "content": "Here's the user information:\n```json\n{\n  \"user\": {\n    \"name\": \"John Doe\",\n    \"age\": 30,\n    \"isActive\": true\n  },\n  \"products\": [\n    {\n      \"id\": \"prod-1\",\n      \"price\": 29.99\n    }\n  ]\n}\n```"
    }
  ],
  "ground_truth": null,
  "input_metadata": {
    "dataset_info": {
      "test_case": "exact_match",
      "description": "JSON content that exactly matches the expected schema",
      "expected_schema": {
        "type": "object",
        "properties": {
          "user": {
            "type": "object",
            "properties": {
              "name": {"type": "string"},
              "age": {"type": "number"},
              "isActive": {"type": "boolean"}
            }
          },
          "products": {
            "type": "array",
            "items": {
              "type": "object",
              "properties": {
                "id": {"type": "string"},
                "price": {"type": "number"}
              }
            }
          }
        }
      }
    }
  }
}
Partial Schema Match with Missing Properties: 
{
  "messages": [
    {
      "role": "user",
      "content": "Give me user information in the following JSON format:\n```json\n{\n  \"user\": {\n    \"name\": \"string\",\n    \"age\": \"number\",\n    \"isActive\": \"boolean\",\n    \"address\": {\n      \"street\": \"string\",\n      \"city\": \"string\"\n    }\n  }\n}\n```"
    },
    {
      "role": "assistant",
      "content": "Here's the user information:\n```json\n{\n  \"user\": {\n    \"name\": \"John Doe\",\n    \"age\": 30,\n    \"email\": \"john@example.com\"\n  }\n}\n```"
    }
  ],
  "ground_truth": null,
  "input_metadata": {
    "dataset_info": {
      "test_case": "partial_match",
      "description": "JSON content with missing and extra properties compared to expected schema",
      "expected_schema": {
        "type": "object",
        "properties": {
          "user": {
            "type": "object",
            "properties": {
              "name": {"type": "string"},
              "age": {"type": "number"},
              "isActive": {"type": "boolean"},
              "address": {
                "type": "object",
                "properties": {
                  "street": {"type": "string"},
                  "city": {"type": "string"}
                }
              }
            }
          }
        }
      }
    }
  }
}
Type Mismatch Errors: 
{
  "messages": [
    {
      "role": "user",
      "content": "Give me user information in the following JSON format:\n```json\n{\n  \"name\": \"string\",\n  \"age\": \"number\",\n  \"tags\": [\"string\"]\n}\n```"
    },
    {
      "role": "assistant",
      "content": "Here's the user information:\n```json\n{\n  \"name\": \"John Doe\",\n  \"age\": \"30\",\n  \"tags\": {\n    \"tag1\": \"value1\"\n  }\n}\n```"
    }
  ],
  "ground_truth": null,
  "input_metadata": {
    "dataset_info": {
      "test_case": "mismatched_types",
      "description": "JSON content with type mismatches (string instead of number, object instead of array)",
      "expected_schema": {
        "type": "object",
        "properties": {
          "name": {"type": "string"},
          "age": {"type": "number"},
          "tags": {
            "type": "array",
            "items": {"type": "string"}
          }
        }
      }
    }
  }
}

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Step 1: Import Required Dependencies

First, we import the necessary modules from the EP framework: 
import json
from typing import Any, Dict, List
from eval_protocol.models import EvaluationRow
from eval_protocol.pytest import SingleTurnRolloutProcessor, evaluation_test
from eval_protocol.rewards.json_schema import json_schema_reward
  • json: Python’s JSON module for JSON parsing and validation
  • typing: Python’s typing module for type hints (Any, Dict, List)
  • EvaluationRow: Data structure containing conversation messages and ground truth
  • default_single_turn_rollout_processor: Default processor for single-turn conversations
  • evaluation_test: Decorator for configuring evaluation tests
  • json_schema_reward: Function to evaluate JSON content against expected schemas

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Step 2: Create the Dataset Adapter

We need to convert the JSON schema dataset format to the EP’s expected format: 
def json_schema_to_evaluation_row(rows: List[Dict[str, Any]]) -> List[EvaluationRow]:
    """
    Convert a json schema row to an evaluation row.
    
    This adapter extracts the conversation messages and metadata from the dataset,
    creating EvaluationRow objects that can be processed by the evaluation framework.
    
    Args:
        rows: List of JSON schema dataset entries with messages and metadata
        
    Returns:
        List of EvaluationRow objects ready for evaluation
    """
    dataset: List[EvaluationRow] = []
    for row in rows:
        dataset.append(
            EvaluationRow(
                messages=row["messages"][:1],  # Use only the first message (user prompt)
                ground_truth=row["ground_truth"],
                input_metadata=row["input_metadata"],
            )
        )
    return dataset
The adapter function:
  • Extracts conversation messages: Takes the user prompt from the dataset
  • Preserves metadata: Maintains the expected schema and test case information
  • Handles ground truth: Passes through any ground truth data (though not used in schema validation)
  • Creates evaluation rows: Converts dataset entries to the EP’s standard format

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Step 3: Configure the Evaluation Test

We use the @evaluation_test decorator to configure our JSON schema evaluation: 
@evaluation_test(
    input_dataset=["tests/pytest/data/json_schema.jsonl"],
    completion_params=[{"model": "accounts/fireworks/models/kimi-k2-instruct"}],
    mode="pointwise",
    rollout_processor=SingleTurnRolloutProcessor(),
    dataset_adapter=json_schema_to_evaluation_row,
)
async def test_pytest_function_calling(row: EvaluationRow) -> EvaluationRow:
    """Run pointwise evaluation on sample dataset using pytest interface."""
    expected_schema = row.input_metadata.dataset_info["expected_schema"]
    result = json_schema_reward(row.messages, expected_schema=expected_schema)
    row.evaluation_result = result
    print(row.evaluation_result)
    return row
The evaluation configuration:
  • input_dataset: Path to the JSON schema dataset file
  • model: Target model to evaluate (Fireworks Kimi model in this example)
  • mode: Set to “pointwise” for individual sample evaluation
  • rollout_processor: Uses default single-turn processor for conversation handling
  • dataset_adapter: References our custom adapter function

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Step 4: Implement the Evaluation Logic

The core evaluation logic extracts the expected schema and applies the JSON schema reward function: 
async def test_pytest_function_calling(row: EvaluationRow) -> EvaluationRow:
    """Run pointwise evaluation on sample dataset using pytest interface."""
    # Extract the expected schema from the dataset metadata
    expected_schema = row.input_metadata.dataset_info["expected_schema"]
    
    # Apply the JSON schema reward function
    result = json_schema_reward(row.messages, expected_schema=expected_schema)
    
    # Store the evaluation result
    row.evaluation_result = result
    print(row.evaluation_result)
    return row
The evaluation process:
  1. Extracts expected schema: Gets the target JSON structure from metadata
  2. Applies schema validation: Uses json_schema_reward to compare generated JSON against expected schema
  3. Stores results: Saves the evaluation score and metrics in the row
  4. Returns processed row: Provides the evaluated row for further analysis

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Understanding the JSON Schema Reward Function

The json_schema_reward function provides comprehensive JSON validation capabilities:

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Core Features

Schema Extraction and Normalization:
  • Extracts JSON content from assistant responses (supports markdown code blocks)
  • Normalizes schemas for consistent comparison
  • Handles both object and string schema representations
Structural Similarity Calculation:
  • Uses Jaccard similarity to compare schema structures
  • Evaluates property matches, type consistency, and nested object alignment
  • Provides detailed scoring with property-level analysis
Error Handling:
  • Validates JSON syntax before schema comparison
  • Handles malformed JSON with appropriate error scoring
  • Provides clear error messages for debugging

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Test Cases and Evaluation Scenarios

The JSON schema evaluation covers various scenarios:

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✅ Perfect Matches

ScenarioDescription
Exact schema complianceJSON that perfectly matches expected structure
Type consistencyAll data types match schema specifications
Nested object handlingComplex nested structures with proper validation

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⚠️ Partial Matches

ScenarioDescription
Missing propertiesJSON with some expected fields omitted
Extra propertiesJSON with additional fields not in schema
Type mismatchesCorrect structure but wrong data types

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❌ Error Cases

ScenarioDescription
Invalid JSON syntaxMalformed JSON that cannot be parsed
Missing JSON contentResponses without extractable JSON
Empty structuresEdge cases with empty objects or arrays

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🔄 Complex Scenarios

ScenarioDescription
Array validationJSON arrays with consistent item structures
Mixed data typesObjects with various primitive and complex types
Nested arraysMulti-level nested structures with arrays of objects

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Expected Output

The evaluation produces detailed results including: Perfect Match Example: 
EvaluationResult(
    score=1.0,
    reason="Perfect schema match",
    metrics={
        "schema_similarity": MetricResult(
            score=1.0,
            reason="Schema similarity: 1.00",
            is_score_valid=True
        )
    }
)
Partial Match Example: 
EvaluationResult(
    score=0.5,
    reason="Partial schema match with missing and extra properties",
    metrics={
        "schema_similarity": MetricResult(
            score=0.5,
            reason="Schema similarity: 0.50",
            is_score_valid=False
        )
    }
)
Error Case Example: 
EvaluationResult(
    score=0.0,
    reason="Invalid JSON content",
    metrics={
        "error": MetricResult(
            score=0.0,
            reason="Invalid JSON content: Here's the user information:\n```json\n{\n  \"name\": \"John Doe\",\n  \"age\": \n}\n```",
            is_score_valid=False
        )
    }
)

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Conclusion

This JSON schema evaluation demonstrates how to assess AI models’ structured data generation capabilities using schema validation and similarity scoring. The evaluation ensures models can generate valid JSON content that conforms to expected schemas, handle complex nested structures, and maintain type consistency. This evaluation is particularly valuable for:
  • API integration testing: Validating JSON responses from AI models that interact with external APIs
  • Data pipeline validation: Ensuring structured data generation meets schema requirements
  • Model capability assessment: Evaluating language models’ ability to produce machine-readable outputs
The JSON schema evaluation focuses on structural correctness and type compliance rather than semantic content, making it essential for building reliable AI systems that can generate consistent, well-formed JSON data. It provides objective scoring with detailed property-level analysis, comprehensive error handling, and scalable automated validation. This comprehensive JSON schema evaluation framework provides robust assessment of model capabilities in structured data generation, essential for applications requiring reliable JSON output from AI systems.