OpenSCAD MCP Server

# AI-Driven Code Generation for OpenSCAD <metadata> author: devin-ai-integration timestamp: 2025-03-21T01:30:00Z version: 1.0.0 tags: [ai, code-generation, openscad, architecture-decision] </metadata> ## Decision Context The OpenSCAD MCP Server requires a mechanism to translate natural language descriptions into valid OpenSCAD code. This architectural decision record documents the approach chosen for implementing AI-driven code generation. ## Options Considered ### Option 1: Template-Based Approach A simple approach using predefined templates with parameter substitution. **Pros:** - Simple implementation - Predictable output - Low computational requirements **Cons:** - Limited flexibility - Cannot handle complex or novel descriptions - Requires manual creation of templates for each shape type ### Option 2: Full Machine Learning Approach Using embeddings and neural networks to generate OpenSCAD code directly. **Pros:** - Highly flexible - Can handle novel descriptions - Potential for more natural interaction **Cons:** - High computational requirements - Requires training data - Less predictable output - Harder to debug and maintain ### Option 3: Hybrid Pattern Matching with Contextual Rules Combining pattern matching for parameter extraction with rule-based code generation. **Pros:** - Good balance of flexibility and predictability - Moderate computational requirements - Easier to debug and maintain - Can be extended with more sophisticated ML in the future **Cons:** - More complex than pure template approach - Less flexible than full ML approach - Requires careful design of rules and patterns ## Decision **Chosen Option: Option 3 - Hybrid Pattern Matching with Contextual Rules** The hybrid approach was selected because it provides a good balance of flexibility, maintainability, and computational efficiency. It allows for handling a wide range of natural language descriptions while maintaining predictable output and being easier to debug than a full ML approach. ## Implementation Details The implementation consists of two main components: 1. **Parameter Extractor**: Uses regex patterns and contextual rules to extract parameters from natural language descriptions. 2. **Code Generator**: Translates extracted parameters into OpenSCAD code using a combination of templates and programmatic generation. The `AIService` class provides the bridge between these components, handling the overall flow from natural language to code. ```python class AIService: def __init__(self, templates_dir, model_config=None): self.templates_dir = templates_dir self.model_config = model_config or {} self.templates = self._load_templates() def generate_openscad_code(self, context): description = context.get("description", "") parameters = context.get("parameters", {}) # Parse the description to identify key components components = self._parse_description(description) # Generate code based on identified components code = self._generate_code_from_components(components, parameters) return code ``` ## Consequences ### Positive - More flexible code generation than a pure template approach - Better maintainability than a full ML approach - Lower computational requirements - Easier to debug and extend - Can handle a wide range of natural language descriptions ### Negative - More complex implementation than a pure template approach - Requires careful design of patterns and rules - May still struggle with very complex or ambiguous descriptions ### Neutral - Will require ongoing maintenance as new shape types and features are added - May need to be extended with more sophisticated ML techniques in the future ## Follow-up Actions - Implement unit tests for the AI service - Create a comprehensive set of test cases for different description types - Document the pattern matching rules and code generation logic - Consider adding a feedback mechanism to improve the system over time