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yufeioptimal

cloudcompare-mcp

by yufeioptimal

compute_normals

Compute surface normals for point clouds using local neighbourhood analysis to enable Poisson reconstruction and other downstream operations. Supports LS, QUADRIC, or TRIANGULATION modes with radius or k-nearest neighbours.

Instructions

Estimate surface normals for a point cloud using a local neighbourhood. Normals are required for many downstream operations (Poisson reconstruction, etc.).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
input_pathYesAbsolute path to the input point cloud.
output_pathYesAbsolute path for the cloud with normals.
modeNoNormal estimation mode. LS=Least Squares (default).LS
radiusNoNeighbourhood radius in metres. Mutually exclusive with knn.
knnNoK nearest neighbours. Mutually exclusive with radius.
Behavior2/5

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

No annotations are provided, so the description carries full burden. It does not disclose behavioral traits such as whether the operation is destructive, required permissions, side effects, or error handling. The description is too brief for a writing operation.

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?

The description is extremely concise with two sentences, front-loaded with the core purpose, and contains no superfluous information.

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?

Given 5 parameters and no output schema, the description covers purpose and general usage but lacks details about output behavior, default values (beyond mode), and error conditions. It is adequate but not complete.

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 baseline is 3. The description adds context about 'local neighbourhood' but does not add significant meaning beyond the schema descriptions, which already explain radius and knn.

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?

The description clearly states the tool estimates surface normals for a point cloud using a local neighbourhood, with a specific verb and resource. It distinguishes from siblings like compute_cloud_to_cloud_distances by its unique function.

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?

The description mentions that normals are required for downstream operations, implying when to use, but does not provide explicit guidance on when not to use or compare to alternatives. The context is helpful but minimal.

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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