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cloudcompare-mcp

cloudcompare-mcp

by yufeioptimal
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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compute_cloud_to_mesh_distances

Compute signed distances from a point cloud to a reference mesh to evaluate the deviation between a scan and a CAD model. Outputs a labelled point cloud for inspection.

Instructions

Compute signed distances from a point cloud to a reference mesh (C2M distance). Useful for comparing a scan to a CAD model.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "mesh_path": "<mesh_path>",
      "cloud_path": "<cloud_path>",
      "output_path": "<output_path>"
    },
    "name": "compute_cloud_to_mesh_distances"
  }
}

Input Schema

NameRequiredDescriptionDefault
mesh_pathYesAbsolute path to the reference mesh (OBJ, PLY, etc.).
cloud_pathYesAbsolute path to the compared point cloud.
output_pathYesAbsolute path for the labelled output cloud.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description must fully disclose behavior. It only says 'compute signed distances' without mentioning side effects, destructiveness, or what signed distances imply (e.g., positive vs negative). Does not state if files are modified or created.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences that front-load the action and purpose. Every word contributes meaning; no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers the main purpose and a use case, but omits details about the output (e.g., that it saves a labelled cloud to output_path) and does not explain the return value or any side effects. Given the simplicity, it is adequate but incomplete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so parameters are already well-described. The description adds the concept of 'signed distances' and 'C2M' context, but does not provide additional semantic details beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states the action (compute signed distances), resources (point cloud and reference mesh), and acronym (C2M). Distinguishes from sibling compute_cloud_to_cloud_distances which compares two point clouds.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides a use case ('comparing a scan to a CAD model') but no explicit guidance on when to use this tool versus alternatives, nor when not to use it. Lacks prerequisites or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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