OpenSCAD MCP Server

import os from typing import Dict, Any from dotenv import load_dotenv # Load environment variables from .env file load_dotenv() # Base directories BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) OUTPUT_DIR = os.path.join(BASE_DIR, "output") # Output subdirectories IMAGES_DIR = os.path.join(OUTPUT_DIR, "images") MULTI_VIEW_DIR = os.path.join(OUTPUT_DIR, "multi_view") APPROVED_IMAGES_DIR = os.path.join(OUTPUT_DIR, "approved_images") MODELS_DIR = os.path.join(OUTPUT_DIR, "models") SCAD_DIR = os.path.join(BASE_DIR, "scad") # Google Gemini API configuration GEMINI_API_KEY = os.getenv("GEMINI_API_KEY", "") # Set via environment variable GEMINI_BASE_URL = "https://generativelanguage.googleapis.com/v1beta" GEMINI_MODEL = "gemini-2.0-flash-exp-image-generation" # Default model for image generation # CUDA Multi-View Stereo configuration (local) CUDA_MVS_PATH = os.getenv("CUDA_MVS_PATH", os.path.join(BASE_DIR, "cuda-mvs")) CUDA_MVS_USE_GPU = os.getenv("CUDA_MVS_USE_GPU", "False").lower() == "true" # Default to CPU for macOS compatibility # Remote CUDA Multi-View Stereo configuration REMOTE_CUDA_MVS = { # General settings "ENABLED": os.getenv("REMOTE_CUDA_MVS_ENABLED", "True").lower() == "true", "USE_LAN_DISCOVERY": os.getenv("REMOTE_CUDA_MVS_USE_LAN_DISCOVERY", "True").lower() == "true", # Server connection "SERVER_URL": os.getenv("REMOTE_CUDA_MVS_SERVER_URL", ""), # Empty means use LAN discovery "API_KEY": os.getenv("REMOTE_CUDA_MVS_API_KEY", ""), "DISCOVERY_PORT": int(os.getenv("REMOTE_CUDA_MVS_DISCOVERY_PORT", "8765")), # Connection parameters "CONNECTION_TIMEOUT": int(os.getenv("REMOTE_CUDA_MVS_CONNECTION_TIMEOUT", "10")), "UPLOAD_CHUNK_SIZE": int(os.getenv("REMOTE_CUDA_MVS_UPLOAD_CHUNK_SIZE", "1048576")), # 1MB "DOWNLOAD_CHUNK_SIZE": int(os.getenv("REMOTE_CUDA_MVS_DOWNLOAD_CHUNK_SIZE", "1048576")), # 1MB # Retry and error handling "MAX_RETRIES": int(os.getenv("REMOTE_CUDA_MVS_MAX_RETRIES", "3")), "BASE_RETRY_DELAY": float(os.getenv("REMOTE_CUDA_MVS_BASE_RETRY_DELAY", "1.0")), "MAX_RETRY_DELAY": float(os.getenv("REMOTE_CUDA_MVS_MAX_RETRY_DELAY", "60.0")), "JITTER_FACTOR": float(os.getenv("REMOTE_CUDA_MVS_JITTER_FACTOR", "0.1")), # Health check "HEALTH_CHECK_INTERVAL": int(os.getenv("REMOTE_CUDA_MVS_HEALTH_CHECK_INTERVAL", "60")), "CIRCUIT_BREAKER_THRESHOLD": int(os.getenv("REMOTE_CUDA_MVS_CIRCUIT_BREAKER_THRESHOLD", "5")), "CIRCUIT_BREAKER_RECOVERY_TIMEOUT": float(os.getenv("REMOTE_CUDA_MVS_CIRCUIT_BREAKER_RECOVERY_TIMEOUT", "30.0")), # Processing parameters "DEFAULT_RECONSTRUCTION_QUALITY": os.getenv("REMOTE_CUDA_MVS_DEFAULT_QUALITY", "normal"), # low, normal, high "DEFAULT_OUTPUT_FORMAT": os.getenv("REMOTE_CUDA_MVS_DEFAULT_FORMAT", "obj"), "WAIT_FOR_COMPLETION": os.getenv("REMOTE_CUDA_MVS_WAIT_FOR_COMPLETION", "True").lower() == "true", "POLL_INTERVAL": int(os.getenv("REMOTE_CUDA_MVS_POLL_INTERVAL", "5")), # Output directories "OUTPUT_DIR": MODELS_DIR, "IMAGES_DIR": IMAGES_DIR, "MULTI_VIEW_DIR": MULTI_VIEW_DIR, "APPROVED_IMAGES_DIR": APPROVED_IMAGES_DIR, } # Venice.ai API configuration (optional) VENICE_API_KEY = os.getenv("VENICE_API_KEY", "") # Set via environment variable VENICE_BASE_URL = "https://api.venice.ai/api/v1" VENICE_MODEL = "fluently-xl" # Default model for fastest image generation (2.30s) # Image approval configuration IMAGE_APPROVAL = { "ENABLED": os.getenv("IMAGE_APPROVAL_ENABLED", "True").lower() == "true", "AUTO_APPROVE": os.getenv("IMAGE_APPROVAL_AUTO_APPROVE", "False").lower() == "true", "APPROVAL_TIMEOUT": int(os.getenv("IMAGE_APPROVAL_TIMEOUT", "300")), # 5 minutes "MIN_APPROVED_IMAGES": int(os.getenv("IMAGE_APPROVAL_MIN_IMAGES", "3")), "APPROVED_IMAGES_DIR": APPROVED_IMAGES_DIR, } # Multi-view to model pipeline configuration MULTI_VIEW_PIPELINE = { "DEFAULT_NUM_VIEWS": int(os.getenv("MULTI_VIEW_DEFAULT_NUM_VIEWS", "4")), "MIN_NUM_VIEWS": int(os.getenv("MULTI_VIEW_MIN_NUM_VIEWS", "3")), "MAX_NUM_VIEWS": int(os.getenv("MULTI_VIEW_MAX_NUM_VIEWS", "8")), "VIEW_ANGLES": [0, 90, 180, 270], # Default view angles (degrees) "OUTPUT_DIR": MULTI_VIEW_DIR, } # Natural language processing configuration for MCP NLP = { "ENABLE_INTERACTIVE_PARAMS": os.getenv("NLP_ENABLE_INTERACTIVE_PARAMS", "True").lower() == "true", "PARAM_EXTRACTION_PROMPT_TEMPLATE": """ Extract the following parameters from the user's request for 3D model generation: 1. Object description 2. Number of views requested (default: 4) 3. Reconstruction quality (low, normal, high) 4. Output format (obj, ply, stl, scad) 5. Any specific view angles mentioned If a parameter is not specified, return the default value or leave blank. Format the response as a JSON object. User request: {user_request} """, } # Deprecated configurations (moved to old folder) # These are kept for reference but not used in the new workflow DEPRECATED = { "SAM2_CHECKPOINT_PATH": os.getenv("SAM2_CHECKPOINT_PATH", os.path.join(BASE_DIR, "models", "sam2_vit_b.pth")), "SAM2_MODEL_TYPE": os.getenv("SAM2_MODEL_TYPE", "vit_b"), "SAM2_USE_GPU": os.getenv("SAM2_USE_GPU", "False").lower() == "true", "THREESTUDIO_PATH": os.path.join(BASE_DIR, "threestudio") } # Create necessary directories for directory in [OUTPUT_DIR, IMAGES_DIR, MULTI_VIEW_DIR, APPROVED_IMAGES_DIR, MODELS_DIR, SCAD_DIR]: os.makedirs(directory, exist_ok=True)