AI JSON Schema Enforcer

Schema enforcement for AI pipelines

Function-calling and structured output still produce extra fields, stringly-typed numbers, and missing keys. Validation alone rejects bad payloads; enforcement transforms them toward compliance so downstream code can proceed.

Enforcement options

• Remove extra properties when additionalProperties is false
• Add missing required fields (null or schema default)
• Coerce strings to numbers/booleans when the schema type demands it
• Trim strings to maxLength and clamp numbers to min/max

Gate LLM structured output

Function calling and JSON mode get models close to your schema, but not always all the way — an id arrives as "42" instead of 42, an extra commentary field sneaks in, or a required key is missing. Dropping the response is wasteful when the data is basically right. Enforcement nudges the payload into shape so your pipeline keeps moving, with a validation pass as the final gate.

Enforce then validate

Enforcement and validation are complementary. Run the enforcer first to coerce types, fill required fields, and strip extras, then run the JSON Schema Validator to confirm the result actually conforms. Rules that can't be fixed automatically — cross-field constraints, complex conditionals — surface as validation errors for a human to resolve.