tesseract-26-eyeshot-mcp

tesseract-26-eyeshot-mcp

Enables AI-driven CAD operations through natural language, integrating LLM and Eyeshot SDK for model manipulation.

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README

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:

  1. frontend: React + Three.js interface combining a Chat Assistant with a 3D Canvas.
  2. backend-mcp: Python FastAPI server handling the MCP routing logic and tool execution.
  3. llm-service: Python FastAPI server responsible for talking to the LLM (Gemini/GPT) to map natural language to CAD operations.
  4. cad-engine: C# ASP.NET Core server utilizing the devDept Eyeshot SDK to perform CAD operations headlessly.
  5. shared: A set of common schemas mapping commands across the layer divides.

Setup

  1. Copy .env.example to .env.
  2. Assign your secret keys:
    • GEMINI_API_KEY: Your Gemini API key from Google AI Studio.
    • EYESHOT_LICENSE_KEY: Your Eyeshot production license key.
  3. 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.

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