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blend-ai

by HoldMyBeer-gg
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Python
Local

snap_to_grid

Align a named object to the nearest grid point using an optional grid size, and get its snapped coordinates for precise 3D placement.

Instructions

Snap an object's location to the nearest grid point.

Args: object_name: Name of the object. grid_size: Size of the grid cells. Defaults to 1.0.

Returns: Dict with the object name and snapped location.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
grid_sizeNo
object_nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv1.7.0
    • removedInput schema / properties / name
      Removed value: -{
-  "title": "Name",
-  "type": "string"
-}
    • addedInput schema / properties / object_name
      Added value: +{
+  "title": "Object Name",
+  "type": "string"
+}
    • changedInput schema / required
      Previous value: -[
-  "name"
-]New value: +[
+  "object_name"
+]
  2. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations, the description carries the full burden. It indicates the operation changes the object's location and returns the new location, but omits coordinate space (world vs local), side effects, error behavior, mode prerequisites, and reversibility, which are important for a mutation tool.

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

Conciseness4/5

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

The purpose is front-loaded in a single clear sentence, followed by structured Args and Returns sections. The Args block is slightly mechanical but justified by zero schema coverage, and there is no wasted prose.

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?

An output schema exists, so the Returns note is redundant. For a simple mutation tool, the description covers the essentials, but it still leaves gaps around coordinate space, side effects, and usage context that an agent would need when selecting and invoking it.

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 description coverage is 0%, so the description must compensate. It documents both parameters — object_name as 'Name of the object' and grid_size with a default of 1.0 — but does not specify units for grid_size or how the object name is resolved.

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

Purpose4/5

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

The description states a specific verb ('Snap') and resource ('an object's location') with a clear target ('nearest grid point'). It implicitly distinguishes from siblings like set_location or apply_transforms, but it does not explicitly name or route against them.

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?

There is no guidance on when to use this tool versus alternatives such as set_location, nor any prerequisites, mode requirements, or exclusions. The Args block only documents parameters, not usage context.

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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