statistical_outlier_removal

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

No annotations exist, so the description carries the burden. It explains the removal criterion (mean + nSigma*std) and implies destructive behavior (points removed). However, it does not disclose whether the input file is modified, output overwriting behavior, or potential side effects like memory usage.

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

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

Two sentences, direct and front-loaded with the purpose. Every sentence adds value with no redundancy.

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

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

The description covers the core algorithm but lacks contextual details like typical parameter values, statistical assumptions (e.g., Gaussian distribution), or prerequisites (e.g., point cloud format). Adequate but not thorough.

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

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

Schema coverage is 100%, so parameters are well-documented in the schema. The description adds a formula referencing knn and n_sigma but no extra constraints or examples. Baseline 3 is appropriate.

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

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

The description clearly states the verb 'remove', the resource 'statistical outliers', and the specific algorithm (distance distribution to k nearest neighbors, threshold mean + nSigma*std). This distinguishes it from sibling tools like filter_by_scalar_field or icp_registration.

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

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

The description provides no guidance on when to use this tool versus alternatives (e.g., filter_by_scalar_field), nor does it mention prerequisites or when not to use it. Only the algorithm is explained.

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