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blend-ai

by HoldMyBeer-gg
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Python
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flip_normals

Reverses all face normals on a mesh to correct uniformly inverted normals. Use when normals are uniformly wrong, not inconsistent.

Instructions

Flip the direction of all face normals on a mesh.

Reverses inside/outside of all faces. Use recalculate_normals instead if normals are inconsistent rather than uniformly wrong.

Args: object_name: Name of the mesh object.

selection: "ALL" to act on the whole mesh, or "CURRENT" to act only
    on what is already selected. Use the select_* tools to choose
    first. Defaults to "ALL".

Returns: Confirmation dict.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
selectionNoALL
object_nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.7.0
    • addedInput schema / additionalProperties
      Added value: +false
    • addedInput schema / properties / selection
      Added value: +{
+  "default": "ALL",
+  "enum": [
+    "ALL",
+    "CURRENT"
+  ],
+  "title": "Selection",
+  "type": "string"
+}
  2. Addedv1.2.2

TDQS

A4.7/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden, and it does disclose the meaningful side effect: it reverses inside/outside orientation of all faces. Selection semantics (whole mesh vs pre-selected) and the return shape are also stated. It does not mention permissions, undo behavior, or what happens on non-manifold meshes, which keeps it short of a 5.

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?

Front-loaded one-line summary, then the sibling-routing sentence, then compact Args/Returns blocks. Every sentence earns its place with no restatement of the tool name.

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

Completeness5/5

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

A destructive-ish mesh operation with two params is fully covered: purpose, alternative, selection behavior, and a note that a confirmation dict is returned (an output schema also exists). Nothing an agent needs to invoke this correctly is missing.

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

Parameters4/5

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

Schema coverage is 0%, so the description must compensate, and it does: object_name is identified as the mesh object, and selection's two enum values are given real meaning ('ALL' = whole mesh, 'CURRENT' = what is already selected) along with the default. It adds the semantic intent that the raw enum labels lack.

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?

States a specific verb+resource ('Flip the direction of all face normals on a mesh') and immediately disambiguates against the sibling recalculate_normals by naming the condition that separates them (uniformly wrong vs inconsistent). An agent can choose between the two without opening either schema.

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

Usage Guidelines5/5

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

Explicitly says to use recalculate_normals instead when normals are inconsistent rather than uniformly wrong, and tells the agent to run the select_* tools first when using selection='CURRENT'. Both the when and the when-not are covered.

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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