NeuralForge exhibits significant definition quality gaps. While all 14 tools have names and descriptions, most descriptions are under 100 chars and lack actionable context. Input schemas are present but minimal, most parameters lack type constraints (enums, ranges, patterns). No output schemas are documented. Error handling is absent; tools return raw API responses or generic error strings. Parameter descriptions are sparse or missing entirely. The server conflates multiple concerns (e.g., start_service handles 11 different services with a single string param, no enum). No security considerations are evident (API URLs hardcoded, no secret injection pattern). Composition is weak: tools like run_pipeline and finetune_start expose low-level parameters (epochs, lora_rank) that should be abstracted or defaulted.

• Convert service_name parameters in start_service, stop_service, and cleanup_storage to enums: {"type": "string", "enum": ["comfyui", "wan2gp", "hunyuan3d", ...]}. This prevents hallucinated service names.
• Document output schemas for all tools. E.g., get_system_status should return {"gpu": {"mem_used_mb": int, "mem_total_mb": int, "temp_c": float, "util_percent": int}, "system": {...}, "services": [{"name": str, "status": str, "port": int}]}.
• Add error categorization and recovery guidance. E.g., rag_search should return {"error": "Connection timeout", "category": "retryable", "guidance": "Qdrant service may be down. Try get_system_status to check service health."}.
• Expand parameter descriptions with format, constraints, and discovery hints. E.g., 'collection: Name of the RAG collection (string). Call rag_list_collections() first to see available collections. Default: estonian_laws (Estonian legislation).'
• Move API URLs to environment variables (NEURALFORGE_PANEL_URL, NEURALFORGE_QDRANT_URL, NEURALFORGE_OLLAMA_URL) and load them in __init__. This enables secure deployment without hardcoding endpoints.