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add_report_noise_event_node

Adds a Report Noise Event node to an Unreal Blueprint, triggering AI hearing perception. Set loudness and range so player actions like footsteps or gunfire are audible to PawnSensing-based AI.

Instructions

Add a 'Report Noise Event' node (UAISense_Hearing).

Ch.10: Reports a noise to the AI perception system so PawnSensing can detect it. Used to make the player's actions (shooting, footsteps) audible to AI.

Args: blueprint_name: Blueprint name (usually player Character) loudness: How loud the noise is (0.0-1.0) max_range: Max range the noise can be heard (0 = use PawnSensing threshold) node_position: Optional [X, Y] graph position

KB: see knowledge_base/04_AI_SYSTEMS.md#overview Example: add_report_noise_event_node(blueprint_name="/Game/MCP_Test/BP_Example")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
loudnessNo
max_rangeNo
node_positionNo
blueprint_nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.3/5.0
Behavior3/5

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

No annotations are provided, so the description carries the behavioral burden. It explains the node's role in AI perception and the max_range semantics (0 = use PawnSensing threshold), which is useful. However, it does not disclose that the node is added to the current Blueprint graph, prerequisites like a PawnSensing component, or failure behavior if blueprint_name is invalid.

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 well-organized with a clear purpose statement, labeled argument list, KB pointer, and concrete example. Every part earns its place, and there is no filler or redundant repetition.

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 tool has an output schema, so return values need no explanation. It covers purpose, all parameters, a KB reference, and a usage example. The main gap is not explicitly stating which graph the node is inserted into or what prerequisites are required, but for an add-node operation this is mostly adequate.

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

Parameters5/5

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

Schema description coverage is 0%, but the description compensates well by explaining every parameter: blueprint_name with an example, loudness with a range, max_range with its default behavior, and node_position with optional syntax. This adds real meaning beyond the raw schema.

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 verb and resource: add a 'Report Noise Event' node (UAISense_Hearing), and explains that it reports noise to the AI perception system so PawnSensing can detect it. It clearly distinguishes this from sibling tools like add_on_hear_noise_event by focusing on creating the reporting node rather than handling the hearing event.

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?

It gives a clear usage context: make player actions like shooting and footsteps audible to AI. It does not explicitly state when not to use it or name alternative tools, but the context and examples are sufficient for an agent to understand the intended scenario.

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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