main.py.md•2.57 kB
<metadata>
author: devin-ai-integration
timestamp: 2025-03-21T01:30:00Z
version: 1.0.0
related-files: [/src/ai/ai_service.py, /src/models/code_generator.py, /src/nlp/parameter_extractor.py]
prompt: "Build an MCP server for OpenSCAD"
</metadata>
<exploration>
The main application was designed to implement a Model Context Protocol (MCP) server for OpenSCAD integration. Several approaches were considered:
1. Using a standalone server with direct OpenSCAD CLI calls
2. Implementing a web service with REST API
3. Creating an MCP-compliant server with FastAPI
The MCP-compliant FastAPI approach was selected for its alignment with the project requirements and modern API design.
</exploration>
<mental-model>
The main application operates on a "tool-based MCP service" paradigm, where each capability is exposed as an MCP tool that can be called by AI assistants. This mental model aligns with the MCP specification and provides a clean separation of concerns.
</mental-model>
<pattern-recognition>
The implementation uses the Facade pattern to provide a simple interface to the complex subsystems (parameter extraction, code generation, OpenSCAD wrapper, etc.). This pattern simplifies the client interface and decouples the subsystems from clients.
</pattern-recognition>
<trade-off>
Options considered:
1. Monolithic application with tightly coupled components
2. Microservices architecture with separate services
3. Modular monolith with clear component boundaries
The modular monolith approach was chosen because:
- Simpler deployment and operation
- Lower latency for inter-component communication
- Easier to develop and debug
- Still maintains good separation of concerns
</trade-off>
<domain-knowledge>
The implementation required understanding of:
- Model Context Protocol (MCP) specification
- FastAPI framework
- OpenSCAD command-line interface
- 3D modeling and printing workflows
</domain-knowledge>
<technical-debt>
The current implementation has some limitations:
- In-memory storage of models (not persistent)
- Basic error handling
- Limited printer discovery capabilities
Future improvements planned:
- Persistent storage for models
- Enhanced error handling and reporting
- More robust printer discovery and management
</technical-debt>
<knowledge-refs>
[OpenSCAD Basics](/rtfmd/knowledge/openscad/openscad-basics.md) - Last updated 2025-03-21
[AI-Driven Code Generation](/rtfmd/decisions/ai-driven-code-generation.md) - Last updated 2025-03-21
</knowledge-refs>