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aadeshrao123

Unreal-MCP

by aadeshrao123

set_niagara_curve

Sets a curve (Float, Color, or Vector) on a Niagara module input parameter by specifying system path, emitter, module, input name, script usage, curve type, and keys.

Instructions

Set a curve on a module input parameter.

Args: system_path: Path to the Niagara System asset emitter_name: Name of the target emitter module_name: Name of the module input_name: Name of the input parameter script_usage: Stack the module is in curve_type: Curve type — "FloatCurve", "ColorCurve", "VectorCurve" keys: JSON array of curve keys, e.g. [{"time":0,"value":1},{"time":1,"value":0}] For color curves: [{"time":0,"r":1,"g":0,"b":0,"a":1}]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keysYes
curve_typeYes
input_nameYes
module_nameYes
system_pathYes
emitter_nameYes
script_usageYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It explains the curve types and the key JSON format, but lacks details on side effects (e.g., overwriting or appending curves), error handling, or required permissions. This is adequate but not comprehensive.

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 a clear one-sentence purpose followed by a list of args. Every sentence is necessary, and there is no redundancy. It is front-loaded with the action.

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 7 required parameters and no annotations, the description covers the core functionality well, including curve type and key format. An output schema exists (not shown) which likely documents return values. Minor omission: no mention of whether the input must already exist or if the tool validates keys.

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?

The input schema has 0% description coverage, but the description lists all 7 parameters and provides format examples for 'keys' and 'curve_type.' For other parameters (e.g., 'system_path'), only names are given, which are self-explanatory but add minimal value. Baseline for low coverage is 3.

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 clearly states 'Set a curve on a module input parameter,' specifying the verb and resource. This distinguishes it from sibling tools like 'set_niagara_module_input' or 'set_niagara_user_parameter,' which handle different aspects.

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?

The description does not provide any guidance on when to use this tool versus alternatives, nor does it mention prerequisites, limitations, or exclusions. Without such context, the agent may misuse the tool.

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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