cloudcompare-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| CLOUDCOMPARE_PATH | No | Path to the CloudCompare executable. If not set, the server looks in system PATH and platform default locations. |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| get_cloudcompare_infoA | Check if CloudCompare is installed and return its version and path. Call this first to confirm the tool is available. |
| load_cloud_infoA | Load a point cloud or mesh file and return basic statistics: number of points, bounding box, available scalar fields, etc. Supports LAS/LAZ, PLY, PCD, E57, XYZ, ASC, BIN, SHP, OBJ, and more. |
| subsampleA | Reduce the density of a point cloud using random, spatial, or octree subsampling. Output is saved to the specified path. |
| compute_cloud_to_cloud_distancesA | Compute nearest-neighbour distances from every point in the 'compared' cloud to the 'reference' cloud (C2C distance). Returns distance statistics and saves the labelled cloud. |
| compute_cloud_to_mesh_distancesA | Compute signed distances from a point cloud to a reference mesh (C2M distance). Useful for comparing a scan to a CAD model. |
| icp_registrationB | Register a 'data' cloud onto a 'model' cloud using the Iterative Closest Point (ICP) algorithm. Returns the transformation matrix and final RMS error. |
| compute_normalsA | Estimate surface normals for a point cloud using a local neighbourhood. Normals are required for many downstream operations (Poisson reconstruction, etc.). |
| filter_by_scalar_fieldA | Keep only points whose scalar-field value falls within [min_val, max_val]. Useful for height, intensity, or distance thresholding. |
| statistical_outlier_removalA | Remove statistical outliers by analysing the distance distribution to k nearest neighbours. Points farther than (mean + nSigma × std) from their neighbours are removed. |
| merge_cloudsB | Merge two or more point cloud files into a single output file. |
| convert_formatA | Convert a point cloud or mesh file to a different format. CloudCompare infers the target format from the output file extension (e.g. .las, .laz, .ply, .pcd, .xyz, .asc, .e57, .obj). |
| run_cloudcompare_commandA | Run an arbitrary CloudCompare CLI command for advanced or unsupported operations. Do NOT include the executable path or -SILENT flag — they are added automatically. Example args: ["-O", "/path/cloud.las", "-AUTO_SAVE", "OFF"] |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 12 tools
Each tool targets a distinct operation (e.g., distance computation, filtering, registration, conversion), with clear differentiation even between similar tools like cloud-to-cloud and cloud-to-mesh distances.
Most tools follow a `verb_noun` pattern with underscores, but 'icp_registration' and 'statistical_outlier_removal' deviate slightly by starting with a noun/adjective instead of a verb.
With 12 tools covering loading, filtering, registration, distance analysis, and format conversion, the set is well-scoped for point cloud processing without being overwhelming or too sparse.
The tool set covers essential point cloud operations comprehensively, and the inclusion of an arbitrary command execution tool ('run_cloudcompare_command') fills any potential gaps, ensuring no dead ends.