3D Visualizer MCP server

Give AI agents tools to inspect, compare and export 3D data. The server runs locally and accesses files inside directories you explicitly allow.

Install and connect

Install uv, then add this configuration to your MCP client. Replace the root with your data folder:

{
  "mcpServers": {
    "3d-visualizer": {
      "command": "uvx",
      "args": [
        "--from",
        "3d-visualizer[mcp]==0.4.2",
        "3d-visualizer-mcp",
        "--root",
        "/absolute/path/to/data"
      ]
    }
  }
}

No API key is required. The client starts the server and discovers its tools over stdio. Version 0.4.2 is the published release described here.

What agents can do

• Open point clouds and meshes from allowed local files or remote URLs.

• Project depth and disparity images using supplied calibration and optional aligned RGB images, including COLMAP depth workflows.

• Inspect coordinates, attributes and presentation state; move the camera, focus a selection, set viewpoints and capture PNG previews.

• Select labeled regions, isolate geometry, measure distances and export subsets with attributes and point provenance where supported.

• Transform objects, adjust visibility, point size, coloring and opacity; compare predictions and references with distance coloring.

• Save and restore scene states and named views; control supported model animations and camera keyframes.

• Visualize NumPy arrays and PyTorch tensors through the companion Python API.

Rendering requirements

Interactive views and PNG captures in release 0.4.2 require a compatible MCP Apps client with WebGL and widget-to-server calls. Plain stdio tool discovery works without a browser, GPU or dataset; it does not prove that a client can render the inline viewer. The server is local software, not a public hosted endpoint. Remote URLs are inputs to the viewer.

Example requests

• "Open this PCD, list its labels, isolate label 20 and frame it."

• "Show this depth image as a cloud using the calibration beside it."

• "Compare these prediction and reference clouds, then capture the result."

• "Export the selected object with its labels and original point indices."

For file conventions, client configuration and detailed workflows, see the MCP reference and Python documentation.

Optional agent skill

The 3D inspection skill helps agents choose inspection workflows and translate calibration from nearby files. Install the whole folder, including its depth reference, in your client's skill directory. MCP tool descriptions and workflow resources remain available without the skill; installing it does not configure the MCP connection.