MCP server that wraps the OrcaSlicer CLI: slice models, run parameter sweeps, and analyze G-code from any MCP client.
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# orca-mcp [](https://github.com/jeff-roche/orca-mcp/actions/workflows/ci.yml) An [MCP](https://modelcontextprotocol.io) server that wraps the **OrcaSlicer CLI**, letting any MCP compatible agent (Claude Code, Ollama, Codex, etc.) slice models, run parameter sweeps, and analyze the resulting G-code. ``` MCP client ──stdio──▶ orca-mcp ──subprocess──▶ orca-slicer CLI ──▶ G-code + estimates ``` Works on **Linux, macOS, and Windows**, including Flatpak and AppImage installs of OrcaSlicer. ## Why The OrcaSlicer CLI can slice headlessly, but driving it by hand means juggling profile paths and grepping G-code comments. This server turns that into structured tools an AI agent can use: *"slice this model with fan speeds of 20/40/60% and tell me which layers at the shoulder transition change"* becomes a two-tool-call workflow. ## Requirements - Python 3.10+ - OrcaSlicer installed (native package, AppImage, Flatpak, or Windows/macOS installer) - Linux headless machines: `xvfb-run` recommended (`dnf install xorg-x11-server-Xvfb` / `apt install xvfb`) — some OrcaSlicer builds need a display context even in CLI mode. The server detects this and retries under xvfb automatically. ## Install ```bash pip install orca-mcp # or from source: git clone https://github.com/jeff-roche/orca-mcp cd orca-mcp && pip install . ``` ## Configure your MCP client **Claude Desktop** (`claude_desktop_config.json`) or **Claude Code** (`claude mcp add`): ```json { "mcpServers": { "orca-mcp": { "command": "orca-mcp" } } } ``` If OrcaSlicer isn't on your PATH, point the server at it: ```json { "mcpServers": { "orca-mcp": { "command": "orca-mcp", "env": { "ORCASLICER_PATH": "/home/you/Applications/OrcaSlicer_Linux_V2.3.0.AppImage" } } } } ``` ### How the slicer is located First match wins: 1. `ORCASLICER_PATH` env var (any executable, including an AppIm…
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