Scored across 11 tools
Most tools have clearly distinct purposes: preview_story vs inspect_schema differ in depth, generate_dataset (story-driven) vs generate_from_schema (schema-driven) are separated by input, and audit_dataset (internal consistency) vs validate_domain (external plausibility ranges) are explicitly contrasted. The only mild overlap is preview_story and inspect_schema, which both take a story and surface structure, but the descriptions clarify the lighter-vs-heavier distinction well.
Every tool follows a clean snake_case verb_noun pattern: preview_story, validate_yaml, create_sandbox, query_sandbox, list_domains, inspect_schema, generate_dataset, generate_from_schema, seed_database, validate_domain, audit_dataset. No camelCase mixing or vague verbs, and the naming maps predictably to resource plus action.
11 tools is well-scoped for a synthetic-data platform spanning preview, generation, validation, sandboxing, and DB seeding. Each tool earns its place and there is no redundancy or filler.
The surface covers the full lifecycle: discovery (list_domains), preview (preview_story/inspect_schema), authoring validation (validate_yaml), generation (generate_dataset/generate_from_schema), post-hoc QA (audit_dataset/validate_domain), and live deployment (create_sandbox/query_sandbox/seed_database). No obvious gaps or dead ends for the stated domain.