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aadeshrao123

Unreal-MCP

by aadeshrao123

find_actors

Search actors in Unreal Engine levels by name, label, class, or tag filters. Returns matched counts even when results are truncated.

Instructions

Flexible actor search across the level — combine any number of filters.

Use this to answer "is X in the world", "first 5 actors of class Y", "all actors starting with name Z and tagged W". All filters AND together. Returns total count scanned/matched even when results are truncated.

Args: name_pattern: Case-insensitive substring on UObject name (e.g. "Light"). label_pattern: Case-insensitive substring on outliner display label. class_filter: Class name (short or full path). When it resolves to a real UClass, uses IsA() (or exact_class for ==). Otherwise falls back to substring match on the actor's class name/path. tag: Match actors with this FName in their Tags. exact_class: When class_filter resolves to a UClass, require exact match instead of subclass. max_results: Cap on returned entries (0 = unlimited; truncated flag set). include_transform: Include location/rotation/scale per entry.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagNo
exact_classNo
max_resultsNo
class_filterNo
name_patternNo
label_patternNo
include_transformNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations provided, so the description carries full burden. It discloses AND logic for filters, case-insensitive matching, fallback behavior for class_filter, and return of total count scanned/matched even when truncated. Could mention whether it is read-only (likely safe).

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 well-structured with clear sections and front-loaded purpose. It includes a detailed args list, which is necessary due to no schema descriptions, but could be slightly more concise.

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 complexity (7 params, no annotations, no schema descriptions, but has output schema), the description is fairly complete. It explains behavior, each parameter, and output characteristics. Could mention what the returned actor list contains, but output schema likely covers that.

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 coverage is 0%, but the description provides detailed explanations for all 7 parameters, including behavior (case-insensitive, IsA vs substring, etc.). This fully compensates for the lack of schema descriptions.

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 'Flexible actor search across the level' and provides concrete use cases like 'is X in the world', 'first 5 actors of class Y', etc. It distinguishes from siblings like find_actors_by_name by emphasizing combining multiple filters.

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?

Explicit use cases are given, but it does not state when NOT to use this tool or mention alternatives. However, the sibling list is large and many are specific operations, so the context is clear enough.

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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