Score breakdown0 Version history1 Source
Category Weight Category score Contribution
Security prompt, exec, net, exfil, eval
35%
100
35.0 pts
Supply chain hash, typosquat, maintainer, lockfile
20%
100
20.0 pts
Maintenance staleness, pinning, CI
15%
100
15.0 pts
Transparency SKILL.md, perms, README
15%
100
15.0 pts
Community installs, verify, response
15%
100
15.0 pts
Findings & checks · 0 flagged
Security score 100 · 0 findings
✓ — All security checks passedNo findings in this category for the latest scan. pass
Supply chain score 100 · 0 findings
✓ — All supply chain checks passedNo findings in this category for the latest scan. pass
Maintenance score 100 · 0 findings
✓ — All maintenance checks passedNo findings in this category for the latest scan. pass
Transparency score 100 · 0 findings
✓ — All transparency checks passedNo findings in this category for the latest scan. pass
Community score 100 · 0 findings
✓ — All community checks passedNo findings in this category for the latest scan. pass
Every scanned point with the score it earned and what moved between them.
1 scans · 90 days 303286c latest
Jun 23, 2026 100 303286c
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
README.md · 1 file README.md 1.5 KB · Markdown Rendered Raw ⧉ Copy
AI-Powered CAD System (MCP + Eyeshot) This repository contains the hackathon prototype for an AI-driven CAD application utilizing the Model Context Protocol (MCP) and devDept Eyeshot SDK.
Architecture layers: frontend: React + Three.js interface combining a Chat Assistant with a 3D Canvas.backend-mcp: Python FastAPI server handling the MCP routing logic and tool execution.llm-service: Python FastAPI server responsible for talking to the LLM (Gemini/GPT) to map natural language to CAD operations.cad-engine: C# ASP.NET Core server utilizing the devDept Eyeshot SDK to perform CAD operations headlessly.shared: A set of common schemas mapping commands across the layer divides.Setup Copy .env.example to .env. Assign your secret keys: GEMINI_API_KEY: Your Gemini API key from Google AI Studio.EYESHOT_LICENSE_KEY: Your Eyeshot production license key.IMPORTANT : NEVER commit your .env file to the repository. It is already included in .gitignore to prevent accidental credential leaks.Requirements Node.js (for the frontend) Python 3.10+ (for MCP and LLM services) .NET 8 SDK (for the CAD engine) Command Flow example Prompt: "Load the sample.step model" MCP router sends to LLM service. LLM Service returns: {"action": "load_model", "file_path": "sample.step"} MCP router dispatches HTTP POST to CAD Engine with payload. CAD Engine executes the command and yields the modified state.