booth-layout-mcp

An MCP server that lets an AI assistant design a trade-show / vending-event floor plan from plain language — place booths, keep fire-code aisles, assign vendors, validate for overlaps and out-of-bounds, and export an SVG map. Ships with a Claude skill so "lay out my show floor" just works.

"100×80 hall, 6 rows of 10 booths at 8×8 ft with 6 ft cross-aisles, plus a sponsor row of 5 along the top wall" → a validated floor plan + floor.svg.

Why

Event organizers describe a floor in words; turning that into correct geometry (consistent booths, legal aisles, nothing overlapping, nothing off the edge) is fiddly and error-prone by hand. This exposes the layout engine as MCP tools, so a model can do the placement and — crucially — validate it, while a human stays in charge of vendor assignments and final sign-off.

Related MCP server: Talk to Figma MCP

What's in it

Tools

create_floor · add_booth · grid · along_wall · assign_vendor · move_booth · list_booths · validate · export_svg

Every placement is bounds-checked (booths outside the hall are dropped) and validate() reports overlapping pairs, out-of-bounds booths, and floor utilization — so the model can self-correct before returning a plan.

Run it

pip install -r requirements.txt

# try the engine directly (no MCP client) — writes an SVG you can open:
python examples/demo.py

# run as an MCP server (stdio):
python server.py

Register it with any MCP client (e.g. Claude Desktop / Claude Code):

{
  "mcpServers": {
    "booth-layout": { "command": "python", "args": ["/path/to/booth-layout-mcp/server.py"] }
  }
}

Then drop skills/booth-layout/ into your skills directory and ask: "Lay out an 80×60 hall with 40 booths at 8×8, double rows and 6 ft aisles, and a sponsor row along the top." The assistant places, validates, and hands back a map.

Design notes

• Feet, top-left origin. One consistent coordinate space keeps geometry and fire-code reasoning (aisle widths, perimeter margins) simple.

• Validation is a first-class tool, not an afterthought — the value of letting a model place booths is only realized if it can also check its own work.

• Plans are plain JSON, so the same layout drops straight into a web canvas editor or a print pipeline.

• Human-in-the-loop. The tools build and check geometry; a person still owns vendor assignments and approval.

Tech

Python · Model Context Protocol (FastMCP) · dependency-free geometry/SVG core.

License

MIT