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aadeshrao123

Unreal-MCP

by aadeshrao123

add_statetree_evaluator

Add a global evaluator to a StateTree asset by specifying the evaluator class and optional properties for node and instance data.

Instructions

Add a global evaluator to the StateTree.

Args: asset_path: Content path to the StateTree asset evaluator_class: Evaluator struct name (e.g. "MassEnemyHasTargetEvaluator") properties: JSON object for evaluator node properties instance_properties: JSON object for evaluator instance data properties

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
asset_pathYes
propertiesNo
evaluator_classYes
instance_propertiesNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are present, and the description does not disclose any behavioral traits such as side effects, permissions required, or whether the operation is destructive. The description only states the basic action without additional context.

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 concise: a single sentence and a parameter list. It is front-loaded with the purpose. The parameter list is structured clearly, but some redundancy exists with the schema. Overall efficient.

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

Completeness3/5

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

The description covers the basic operation and parameter meanings, but given the lack of annotations and the complexity of the tool (mutation, 4 parameters), it could provide more context about the role of global evaluators, typical usage, and whether the operation saves automatically. An output schema exists, so return values are not needed.

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?

With 0% schema coverage, the description adds valuable meaning to parameters, e.g., explaining 'evaluator_class' as an Evaluator struct name with an example, and clarifying that 'properties' and 'instance_properties' are JSON objects for node and instance data. This goes beyond the raw schema, though the JSON object formats could be further detailed.

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 the action ('Add') and the resource ('a global evaluator to the StateTree'), distinguishing it from sibling tools like add_statetree_task or add_statetree_condition.

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?

No guidance is provided on when to use this tool versus alternatives, nor any prerequisites or context for adding an evaluator. The description lists parameters but does not help the agent decide when to invoke this 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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