Repeatability
High
The transformation from SQL schema to GraphQL types follows deterministic rules: column types map to scalars, foreign keys map to relationships, tables map to types. The same logic applies every run, making this structurally identical each time.
Ambiguity Tolerance
Medium
The core output criteria are crisp—valid, executable GraphQL schema with resolver stubs—but decisions like naming conventions, pagination patterns, and how to handle many-to-many join tables require implicit choices the task doesn't specify. These gaps are manageable but need defaults or a brief spec.
Data & Tool Availability
High
The agent needs read access to the PostgreSQL database (or a schema dump) and a Python environment with psycopg2 or similar. Both are standard, easily granted, and the agent can query information_schema directly without special tooling.
Error Cost
Low
The output is a generated file, not a live system change—nothing is deployed or mutated. A bad output is caught immediately when the developer tries to load it into Apollo Server, and regenerating is trivial.
Human Judgment Required
Low
Type mapping, FK traversal, and resolver stub generation are mechanical. A human should review the output for domain-specific naming and business logic before production use, but the generation itself requires no intuition or taste.