bonsai-mcp

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Create and inspect architecture projects in the Bonsai MCP Harness workspace, including its local starter and optional upstream integration.

AI & Automation 957 stars 79 forks Updated today MIT

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Skill Content

# House of ideas Read `studio.json` to understand the current controls; `"$STUDIO_TOOLCHAIN/../studio.config.json"` describes their ranges. Run `"$STUDIO_TOOLCHAIN/run.sh" build` to make a new result. Successful artifacts and their measurements are in `out/runs/<id>/`; `out/latest.json` names the current result. A failed run preserves the last success and records the error in the verdict. The local workflow creates and reopens a real IFC4 building with IfcOpenShell, including storeys, slabs, walls, spaces and quantities. The in-pane drawing is a schematic model inspector. It does not certify structural adequacy or code compliance. Blender/Bonsai editing remains available through the pinned optional bridge. Use `"$STUDIO_TOOLCHAIN/../README.md"` for the integration contract and commands. Read the relevant files under `$STUDIO_UPSTREAM` before using an upstream API. Keep controls within their documented ranges, preserve the data needed to reproduce a comparison, and distinguish preview results from native service or hardware output. The viewer supports history and artifact downloads; tell the user which run contains the result, and what was actually measured. ## Author and check building data Choose the footprint, storeys, height, and room use, then run `build`. Reopen `building.ifc` with IfcOpenShell, inspect spaces/walls/openings, and read the quantity sets. Match their areas and volumes to `quantities.csv`. The canvas is a schematic view; IFC is the source artifact for ...

Details

Author
autonomous-ai
Repository
autonomous-ai/openharness
Created
1 months ago
Last Updated
today
Language
C
License
MIT

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