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ConstraintDSL

A lightweight domain-specific language for representing JSON Schema constraints, optimized for small language model consumption.

Why ConstraintDSL?

Raw JSON Schema is a hierarchical, nested format designed for validation — not for language model reasoning. ConstraintDSL flattens schema constraints into explicit, line-oriented statements that small models (200M–1.5B parameters) can parse and follow reliably.

Empirical evidence

Schema representation Schema success (unseen schemas)
Raw JSON Schema 55.0%
ConstraintDSL 96.3%
ConstraintDSL (random field names) 91.9%

Same model (CodeT5+ 220M), same training procedure, same data volume. Only the schema representation changed.

Specification v1

Field declaration

FIELD <path> TYPE <type> [VALUES <v1|v2|...>] REQUIRED <yes|no>

Path notation

  • Flat: FIELD priority TYPE string REQUIRED yes
  • Nested: FIELD customer.name TYPE string REQUIRED yes
  • Array of primitives: FIELD tags[] TYPE string REQUIRED no
  • Array of objects: FIELD items[].sku TYPE string REQUIRED yes

Types

  • string, integer, number, boolean, null
  • string_email, string_date-time, string_uri (format variants)
  • string with VALUES clause for enums

Tool declaration

TOOL <name>
ARG <path> TYPE <type> [VALUES <v1|v2|...>] REQUIRED <yes|no>

Examples

Simple object

JSON Schema:

{"type":"object","properties":{"priority":{"type":"string","enum":["low","medium","high"]},"description":{"type":"string"}},"required":["priority"]}

ConstraintDSL:

FIELD priority TYPE string VALUES low|medium|high REQUIRED yes
FIELD description TYPE string REQUIRED no

Nested object

FIELD customer.name TYPE string REQUIRED yes
FIELD customer.age TYPE integer REQUIRED no
FIELD ticket.priority TYPE string VALUES low|medium|high REQUIRED yes

OpenAI tool

TOOL create_ticket
ARG priority TYPE string VALUES low|medium|high REQUIRED yes
ARG description TYPE string REQUIRED yes
ARG customer_id TYPE integer REQUIRED no

MCP tool

TOOL read_sensor
ARG sensor_id TYPE string REQUIRED yes
ARG metric TYPE string VALUES temperature|humidity|pressure REQUIRED yes

Input format for models

TASK repair_structured_output

SPEC
FIELD priority TYPE string VALUES low|medium|high REQUIRED yes
FIELD description TYPE string REQUIRED yes

BROKEN_OUTPUT
{"priority":"urgent"}

Target output:

{"priority":"high","description":""}

Source compilers

Compilers are provided for:

  • JSON Schema → ConstraintDSL
  • OpenAI function/tool definitions → ConstraintDSL
  • Anthropic tool definitions → ConstraintDSL
  • MCP tool definitions → ConstraintDSL

See schema_compiler.py for the reference implementation.

Ablation results

DSL variant Schema success
Full DSL 96.3%
Shuffled field names 73.4%
Shuffled enum values 89.8%
Shuffled required flags 93.2%
Field names only 72.2%
No DSL 78.4%

Field names are the most critical component (-22.9pp when shuffled), followed by enum values (-6.5pp) and required flags (-3.1pp).

Known limitations

ConstraintDSL significantly improves structural reasoning in small models, but does not fully eliminate lexical bias. Models trained with ConstraintDSL may still substitute field names with semantically similar alternatives from their training vocabulary (e.g., actionactive). This is a model-level limitation, not a DSL limitation — the DSL provides correct field names, but the model may not faithfully reproduce them.

License

Apache-2.0

Citation

@software{structfix_constraint_dsl,
  title = {ConstraintDSL: Schema Representation for Small Language Models},
  author = {Ottema},
  year = {2026},
  url = {https://huggingface.co/datasets/ottema/constraint-dsl}
}
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