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AI-to-CATIA MCP

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    • A
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      Enables AI agents to drive CATIA V5 through COM for batch part, assembly, and drawing work, validating every step against tool schemas before touching CATIA and verifying results by volume change, 3D geometry references, and final poses. It ships a growing ledger of verified pitfalls that is surfaced alongside triggering errors and extended by agents, so later sessions avoid repeating the same failures.
      121
      MIT
    • A
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      Enables natural-language generation and editing of native CATIA V5 parts and assemblies through MCP tools, converting semantic requests into parametric CATPart/CATProduct models via the CATIA Automation API.
      MIT
    • A
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      B
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      Enables AI agents or local operators to inspect, plan, execute, and verify bounded KOMPAS-3D CAD operations, including sketch and feature editing, parametric parts, springs, transmissions, specifications, relinking, quality checks, and safe exports.
      118
      2
      MIT
    • A
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      Enables AI agents to design parametric 3D CAD models of industrial machinery end-to-end, including modeling, assembly, validation, and manufacturing drawings, through 79 MCP tools.
      2
      MIT
    • A
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      quality
      A
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      Enables AI agents to perform validated 3D CAD modeling using CAiD and OpenCASCADE, with tools for creating, modifying, querying, and exporting 3D shapes.
      3
      MIT
    • A
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      quality
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      Enables LLMs to safely generate parametric CAD parts (STEP/STL) using verified templates and FreeCAD, with validation and assembly support.
      MIT