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aadeshrao123

Unreal-MCP

by aadeshrao123

get_niagara_particle_stats

Retrieve live particle counts per emitter, total spawned, execution state, and bounds from a previewing Niagara system to debug performance or verify emitter behavior.

Instructions

Get live particle counts and emitter execution state from running preview.

Shows how many particles are alive per emitter, total spawned count, execution state (Active/Inactive/Complete), and bounds info. Requires the system to be previewing in the Niagara editor or spawned in level.

Args: system_path: Path to the Niagara System asset emitter_name: Optional — filter to a specific emitter's stats

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
system_pathYes
emitter_nameNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It details what data is returned (alive counts, total spawned, execution state, bounds info) and the prerequisite for a running preview. It does not mention potential errors or non-running behavior, but the disclosed traits are sufficient for a read operation.

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 concise, with three sentences for purpose and behavior plus two parameter descriptions. It is front-loaded with the main action and includes no superfluous information.

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?

Given the tool has two parameters, no enums, and an output schema, the description covers the key aspects: purpose, output details, and prerequisites. It could mention error cases or behavior when the preview is not running, but overall it is complete enough for selection and invocation.

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?

The schema description coverage is 0%, so the description must add meaning. It includes an 'Args' section explaining system_path as 'Path to the Niagara System asset' and emitter_name as 'Optional — filter to a specific emitter's stats', adding significant semantic context beyond the schema's type and required fields.

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 starts with 'Get live particle counts and emitter execution state from running preview', which clearly specifies the verb (Get) and resource (particle stats and execution state). It distinguishes from sibling tools like get_niagara_system_info or get_niagara_emitters by focusing on live counts and state.

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?

The description states a prerequisite: 'Requires the system to be previewing in the Niagara editor or spawned in level.' This provides clear usage context. However, it does not explicitly mention alternatives or when not to use the tool, missing a full when/when-not guidance.

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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