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rhino-mcp

by rokibul-mist
OverviewSchemaSourceRelated ServersScoreDiscussionsAdmin
Python
Hybrid

rhino_smooth_polyline

Apply Laplacian smoothing to polylines or NURBS control-point nets to reduce irregularity. Adjust blend factor and iterations while optionally pinning endpoints.

Instructions

Apply Laplacian smoothing to a polyline (or NURBS curve via its control points).

    Each iteration replaces every interior point P_i with
    ``(1 - factor) * P_i + factor * (P_{i-1} + P_{i+1}) / 2``.
    With ``pin_endpoints=True`` the first and last points are held.
    Returns the new curve's object_id (the input is replaced).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
argsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4/5.0
Behavior5/5

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

The description clearly discloses mutating behavior: 'Returns the new curve's object_id (the input is replaced).' It also explains the smoothing formula, iteration behavior, and the pin_endpoints effect, all of which go beyond the minimal readOnlyHint=false annotation.

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 compact and well-structured: the core purpose is front-loaded, the formula is concise, and each sentence adds useful information. No filler or redundant restatement of the tool name.

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

Completeness4/5

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

The description covers the key behavioral facts: what is smoothed, how the algorithm works, endpoint pinning, mutability, and return value. Minor gaps remain around iteration semantics and document targeting, but the schema and output schema fill some of those gaps.

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?

The description adds meaning for factor and pin_endpoints through the formula and explanation, and clarifies that curve_id can be a polyline or NURBS control-point net. However, it does not explain iterations or doc_id, and with schema description coverage at 0% the description only partially compensates for the gap.

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 states a specific operation — 'Apply Laplacian smoothing to a polyline (or NURBS curve via its control points)' — with a clear verb and resource. It also distinguishes its scope by noting it works on curves and replaces the input, preventing confusion with surface or mesh smoothing siblings.

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

Usage Guidelines2/5

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

The description explains what the tool does but gives no guidance on when to choose it over alternatives like rhino_fair_curve or rhino_smooth. There are no explicit exclusions, prerequisites, or comparative usage hints.

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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