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MCPBridge Server

by RBG-WebDesign
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TypeScript
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level_actors

List actors in the current Unreal Engine level, filtering by name, class, or folder. Optionally include transforms and components for detailed inspection.

Instructions

List actors in the current level with optional filters. Returns name, class, folder, and optionally transforms and components.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "level_actors"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoMax actors to return (default 500)
name_filterNoFilter by actor label (supports * and ? wildcards)
class_filterNoFilter by class name (supports * and ? wildcards)
folder_filterNoFilter by World Outliner folder prefix
include_componentsNoInclude component list (default false)
include_transformsNoInclude location/rotation/scale (default true)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.0

TDQS

A3.8/5.0
Behavior3/5

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

No annotations are provided, so the description must carry the transparency burden. The verb 'List' and the return-field sentence imply a read-only operation and describe outputs, but the description does not disclose default limits, pagination, or explicitly state that it makes no modifications. This is adequate for a simple read tool but lacks richer behavioral context.

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 a single, front-loaded sentence that efficiently states the purpose, scope, and return values without any redundant or filler text.

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?

For a straightforward read/list tool with a fully documented schema, the description covers the key context: current level, optional filters, and returned fields. It does not mention edge details like default inclusion of transforms, but the schema provides those specifics, making it acceptably complete.

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 provides full descriptions for all six parameters, so the description adds little beyond the generic phrase 'optional filters.' With 100% schema coverage, the baseline of 3 is appropriate.

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 uses a specific action ('List actors in the current level') and clearly states the resource and scope, distinguishing it from mutation tools like actor_spawn or actor_delete. It also lists return fields (name, class, folder, optional transforms/components), making the purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for retrieving actors from the current level, but it does not explicitly compare this tool with sibling tools such as level_outliner or state when to prefer one over the other. It provides clear context but no exclusionary or alternative 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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