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add_cross_product_node

Adds a Cross Product node to a Blueprint, returning a vector perpendicular to two input vectors for computing normals and right-angle vectors.

Instructions

Add a 'Cross Product' node between two vectors.

Ch.14: Returns a vector perpendicular to both inputs. Useful for computing normals and right-angle vectors.

Args: blueprint_name: Blueprint name node_position: Optional [X, Y] graph position

KB: see knowledge_base/26_CHAOS_PHYSICS_AND_DESTRUCTION.md#overview Example: add_cross_product_node(blueprint_name="/Game/MCP_Test/BP_Example")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
node_positionNo
blueprint_nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations, the description must carry the behavioral disclosure burden. It communicates that the tool inserts a node and that the node returns a perpendicular vector, and the example shows expected usage. It does not disclose side effects such as whether the blueprint is modified persistently, compiled, or saved, which is a meaningful gap for a mutation-oriented 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 description is front-loaded with the main action, followed by a short math note, arguments, a KB reference, and an example. It is compact and scannable, though the 'Ch.14' prefix adds limited value and could be dropped without losing essential meaning.

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?

For a two-parameter node-insertion tool, the description covers the blueprint target, optional graph position, mathematical purpose, and includes an example and a KB link. The output schema covers return shape, so not restating it is acceptable; the main omission is post-add behavior such as compilation or persistence.

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 adds useful meaning for node_position ('Optional [X, Y] graph position') and the example clarifies the blueprint_name path format. However, 'blueprint_name: Blueprint name' largely restates the schema, and no coordinate defaults or format details are provided.

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?

States a clear action ('Add') and object ('Cross Product' node), and explains the mathematical behavior ('Returns a vector perpendicular to both inputs'), which distinguishes it from related vector-math siblings like dot product or normalize. It does not explicitly name a sibling alternative, but the semantics are specific enough to identify the tool's purpose.

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

Usage Guidelines4/5

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

'Useful for computing normals and right-angle vectors' gives concrete circumstances for selecting this tool. It lacks an explicit when-not-to-use or named alternative, so it does not reach the full 5, but the context is clear and actionable.

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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