KiCad MCP Server

Overview

KiCAD MCP bridges the gap between natural‑language AI assistants and professional PCB design by exposing KiCAD’s full feature set through the Model Context Protocol. Instead of manually navigating menus, designers can issue plain‑English commands—“create a 10 mm × 15 mm board, place an ATmega328 on the bottom layer, and route a differential pair between pins 1 and 2”—and have the server translate those requests into precise KiCAD operations. This eliminates repetitive GUI interactions and speeds up iteration cycles, especially in prototyping or educational settings where rapid feedback is essential.

The server offers a comprehensive suite of capabilities that cover the entire PCB workflow. It can create and manage projects, generate schematics with components and connections, manipulate board geometry (outlines, layers, properties), and perform advanced routing tasks such as differential pairs and copper pours. Design‑rule checks can be invoked on demand, and the system supports exporting in industry formats like Gerber, PDF, SVG, and 3D models. By providing the AI with contextual information about the current board state, KiCAD MCP enables more intelligent suggestions and error detection that would otherwise require manual inspection.

For developers integrating AI into design pipelines, KiCAD MCP’s modular architecture simplifies extension and maintenance. The TypeScript MCP server implements the Anthropic specification, ensuring compatibility with Claude and other MCP‑ready assistants. A Python layer interacts directly with KiCAD’s pcbnew API, handling all low‑level operations and robust error handling. This separation of concerns allows developers to add new tools or domains—such as thermal analysis or component sourcing—without touching the core communication logic.

Real‑world use cases include rapid prototyping, where engineers can prototype a new board by describing the layout and components in natural language and instantly see the results. Educational environments benefit from hands‑on learning; students can focus on circuit concepts while the AI handles tedious layout tasks. In a collaborative setting, multiple designers can issue commands concurrently, and the server resolves conflicts through KiCAD’s built‑in concurrency controls. The ability to generate schematics as well as layouts from a single interface is particularly valuable for teams that need to iterate quickly between design capture and physical implementation.

What sets KiCAD MCP apart is its end‑to‑end coverage of the PCB design cycle, coupled with a clean, protocol‑driven interface that fits seamlessly into existing AI workflows. Developers who already use Claude or other MCP clients can plug the server in with minimal friction, opening up a new paradigm where AI becomes an active design partner rather than a passive documentation tool.