SolidWorks용 AI 기반 CAD 자동화 — MCP(모델 컨텍스트 프로토콜) 서버.

SolidPilot은 AI 모델이 CAD 기능 수준에서 SolidWorks와 함께 작업할 수 있게 합니다. 목표는 모델이 "어떤 API 메서드를 호출해야 하는가?"가 아니라 "내가 어떤 CAD 의도를 실현하고 있는가?"라는 관점에서 추론하는 것입니다. 의도는 CAD 중립적인 중간 표현으로 변환되고, 결정론적 컴파일러가 그 표현을 구체적인 SolidWorks 연산으로 낮춥니다.

SolidPilot은 Claude 전용 플러그인이 아닙니다; SolidWorks와 AI 사이의 일반적인 다리 역할입니다. MCP는 오픈 표준이기 때문에 MCP 지원 AI 클라이언트는 클로드, OpenClaw, OpenAI 기반 에이전트, 로컬 LLM과 함께 연결할 수 있습니다. 아키텍처는 처음부터 이 확장성을 위해 설계되었습니다: 실행 계층과 플래너 계층은 어떤 클라이언트가 자신을 호출하는지 알지 못합니다; 클라이언트당 얇은 어댑터는 공유 브리지 코어를 재사용합니다. adapters/claude/가 현재 구현 방식이며; 새로운 AI 클라이언트를 지원한다는 것은 단지 새 어댑터를 추가하는 것뿐입니다.

저장소: mcp-server-solidworks · 공개명: SolidPilot · 대상 버전: SolidWorks 2026 SP3

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핵심 아이디어

SolidPilot

AI-driven CAD automation for SolidWorks — an MCP (Model Context Protocol) server.

SolidPilot lets an AI model work with SolidWorks at the CAD feature level. The goal is for the model to reason in terms of “which CAD intent am I realizing?” instead of “which API method should I call?”. Intent is converted into a CAD-neutral intermediate representation, and a deterministic compiler lowers that representation into concrete SolidWorks operations.

SolidPilot is not a Claude-only plugin; it is a general bridge between SolidWorks and AI. Because MCP is an open standard, any MCP-capable AI client can connect — alongside Claude, OpenClaw, OpenAI-based agents, and local LLMs are also targeted. The architecture was designed for this extensibility from the start: the execution and planner layers do not know which client is calling them; a thin adapter per client reuses a shared bridge core. adapters/claude/ is the current implementation; supporting a new AI client means only adding a new adapter.

Repository: mcp-server-solidworks · Public name: SolidPilot · Target version: SolidWorks 2026 SP3

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Core Idea

The SolidWorks API exposes thousands of methods. Presenting each one to the AI as a separate “tool” explodes context size and token cost — the economic problem that stalls similar projects.

SolidPilot solves this by raising the level of abstraction:

• The AI produces intent at the feature level (for example, “put a hole in the top face”).
• That intent is expressed as a CAD-neutral Feature Graph IR.
• A deterministic compiler lowers the IR into ordered, concrete SolidWorks operations.
• A single feature therefore maps to many low-level operations, and one model call per request is enough.

Because the IR is the spine, the same machinery runs backwards: a 2D technical drawing is design intent expressed in two dimensions, so it can be read into the same IR and built.

From a 2D drawing file to a solid part

Hand SolidPilot a .DXF or .DWG — the file a supplier or a customer actually sends, with no model behind it — and it reconstructs the part:

drawing.dwg → analyze_drawing → (draw dialect: views, edge classes, chained contours,
              true-valued dimensions, bend notes) → Feature Graph IR → compiler → SolidWorks

The reader decides which of two answers the drawing warrants. When every decision the part needs is forced by the drawing itself — today, sheet-metal flat patterns — it lowers the whole part to IR on its own and analyze_drawing(mode='build') builds it in one call. When the drawing needs real engineering reading (the normal case for a machined part), it says so and hands over the structured analysis, which the model interprets under a versioned rule set (the recipe://usage/reverse resource) and builds through submit_feature_graph.